The increased availability of Graphical Processing Units (GPUs) in personal computers has made parallel pro-
gramming worthwhile, but not necessarily easier. This paper will take advantage of the power of a GPU, in
conjunction with the Central Processing Unit (CPU), in order to simulate target trajectories for large-scale
scenarios, such as wide-area maritime or ground surveillance. The idea is to simulate the motion of tens of
thousands of targets using a GPU by formulating an optimization problem that maximizes the throughput. To
do this, the proposed algorithm is provided with input data that describes how the targets are expected to
behave, path information (e.g., roadmaps, shipping lanes), and available computational resources. Then, it is
possible to break down the algorithm into parts that are done in the CPU versus those sent to the GPU. The
ultimate goal is to compare processing times of the algorithm with a GPU in conjunction with a CPU to those
of the standard algorithms running on the CPU alone. In this paper, the optimization formulation for utilizing
the GPU, simulation results on scenarios with a large number of targets and conclusions are provided.
KEYWORDS: Digital filtering, Nonlinear filtering, Electronic filtering, Particles, Gaussian filters, Filtering (signal processing), Monte Carlo methods, Systems modeling, Linear filtering, Particle filters
The Probability Hypothesis Density Filter (PHD) is a multitarget tracker for recursively estimating the number
of targets and their state vectors from a set of observations. The PHD filter is capable of working well in
scenarios with false alarms and missed detections. Two distinct PHD filter implementations are available in the
literature: the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) and the Gaussian Mixture
Probability Hypothesis Density (GM-PHD) filters. The SMC-PHD filter uses particles to provide target state
estimates, which can lead to a high computational load, whereas the GM-PHD filter does not use particles, but
restricts to linear Gaussian mixture models. The SMC-PHD filter technique provides only weighted samples
at discrete points in the state space instead of a continuous estimate of the probability density function of the
system state and thus suffers from the well-known degeneracy problem. This paper proposes a B-Spline based
Probability Hypothesis Density (S-PHD) filter, which has the capability to model any arbitrary probability
density function. The resulting algorithm can handle linear, non-linear, Gaussian, and non-Gaussian models and
the S-PHD filter can also provide continuous estimates of the probability density function of the system state. In
addition, by moving the knots dynamically, the S-PHD filter ensures that the splines cover only the region where
the probability of the system state is significant, hence the high efficiency of the S-PHD filter is maintained at
all times. Also, unlike the SMC-PHD filter, the S-PHD filter is immune to the degeneracy problem due to its
continuous nature. The S-PHD filter derivations and simulations are provided in this paper.
This paper presents algorithms for prediction, tracking, and retrodiction for targets whose motion is constrained
by external conditions (e.g., shipping lanes, roads). The targets are moving along a path, defined by way-points
and segments. Measurements are obtained by sensors at low revisit rates (e.g., spaceborne). Existing tracking
algorithms assume that the targets follow the same motion model between successive measurements, but in a
low revisit rate scenario targets may change the motion model between successive measurements. The proposed
prediction algorithm addresses this issue by considering possible motion model whenever targets move to a
different segment. Further, when a target approaches a junction, it has the possibility to travel into one of
the multiple segments connected to that junction. To predict the probable locations, multiple hypotheses for
segments are introduced and a probability is calculated for each segment hypothesis. When measurements become
available, segment hypothesis probability is updated based on a combined mode likelihood and a sequential
probability ratio test is carried out to reject the hypotheses. Retrodiction for path constrained targets is also
considered, because in some scenarios it is desirable to find out the target's exact location at some previous time
(e.g., at the time of an oil leakage). A retrodiction algorithm is also developed for path constrained targets so
as to facilitate motion forensic analysis. Simulation results are presented to validate the proposed algorithm.
KEYWORDS: Artificial intelligence, Radar, Maritime surveillance, Data fusion, Surveillance, Detection and tracking algorithms, Motion models, Time metrology, Head, System identification
Using the Automatic Identification System (AIS) ships identify themselves intermittently by broadcasting their
location information. However, traditionally radars are used as the primary source of surveillance and AIS is
considered as a supplement with a little interaction between these data sets. The data from AIS is much more
accurate than radar data with practically no false alarms. But unlike the radar data, the AIS measurements
arrive unpredictably, depending on the type and behavior of a ship. The AIS data includes target IDs that can
be associated to initialized tracks. In multitarget maritime surveillance environment, for some targets the revisit
interval form the AIS could be very large. In addition, the revisit intervals for various targets can be different.
In this paper, we proposed a joint probabilistic data association based tracking algorithm that addresses the
aforementioned issues to fuse the radar measurements with AIS data. Multiple AIS IDs are assigned to a track,
with probabilities updated by both AIS and radar measurements to resolve the ambiguity in the AIS ID source.
Experimental results based on simulated data demonstrate the performance the proposed technique.
KEYWORDS: Detection and tracking algorithms, Sensors, Target detection, Automatic tracking, Evolutionary algorithms, Distance measurement, Signal to noise ratio, Motion models, Error analysis, Monte Carlo methods
Target tracking in high clutter or low signal-to-noise environments presents many challenges to tracking systems.
Joint Maximum Likelihood estimator combined with Probabilistic Data Association (JML-PDA) is a well-known
parameter estimation solution for the initialization of tracks of very low observable and low signal-to-noise-ratio
targets in higher clutter environments. On the other hand, the Joint Probabilistic Data Association (JPDA)
algorithm, which is commonly used for track maintenance, lacks automatic track initialization capability. This
paper presents an algorithm to automatically initialize and maintain tracks using an integrated JPDA and
JML-PDA approach that seamlessly shares information on existing tracks between the JML-PDA (used for
initialization) and JPDA (used for maintenance) components. The motivation is to share information between
the maintenance and initialization stages of the tracker, that are always on-going, so as to enable the tracking of
an unknown number of targets using the JPDA approach in heavy clutter. The effectiveness of the new algorithm
is demonstrated on a heavy clutter scenario and its performance is tested on negibouring targets with association
ambiguity using angle-only measurements.
KEYWORDS: Radar, Detection and tracking algorithms, Tin, Data processing, Sensors, Antennas, Target detection, Systems modeling, Phased arrays, Time metrology
Electronically scanned array radars as well as mechanically steered rotating antennas return measurements
with different time stamps during the same scan while sweeping form one region to another. Data association
algorithms process the measurements at the end of the scan in order to satisfy the common one measurement
per track assumption. Data processing at the end of a full scan resulted in delayed target state update. This
issue becomes more apparent while tracking fast moving targets with low scan rate sensors. In this paper, we
present new dynamic sector processing algorithm using 2D assignment for continuously scanning radars. A
complete scan can be divided into sectors, which could be as small as a single detection, depending on the
scanning rate and sparsity of targets. Data association followed by filtering and target state update is done
dynamically while sweeping from one end to another. Along with the benefit of immediate track updates,
continuous tracking results in challenges such as multiple targets spanning multiple sectors and targets crossing
consecutive sectors. Also, associations performed in the current sector may require changes in association done
in previous sectors. Such difficulties are resolved by the proposed 2D assignment algorithm that implements
an incremental Hungarian assignment technique. The algorithm offers flexibility with respect to assignment
variables for fusing of measurements received in consecutive sectors. Furthermore the proposed technique can
be extended to multiframe assignment for jointly processing data from multiple scanning radars. Experimental
results based on rotating radars are presented.
Data association is the crucial part of any multitarget tracking algorithm in a scenario with multiple closely
spaced targets, low probability of detection and high false alarm rate. Multiframe assignment, which solves the
data association problem as a constrained optimization, is one of the widely accepted methods to handle the
measurement origin uncertainty. If the targets do not maneuver, then multiframe assignment with one or two
frames will be enough to find the correct data association. However, more frames must be considered in the
data association for maneuvering targets. Also, a target maneuver might be hard to detect when maneuvering
index, which is the function of sampling time, is small. In this paper, we propose an improved multiframe
data association with better cost calculation using backward multiple model recursion, which increases the
maneuvering index. The effectiveness of the proposed algorithm is demonstrated with simulated data.
In this paper, we study the performance of the multipath-assisted multitarget tracking using multiframe assignment
for initiating and tracking multiple targets by employing one or more transmitters and receivers. The basis
of the technique is to use the posterior Cramer-Rao lower bound (PCRLB) to quantify the optimal achievable
accuracy of target state estimation. When resolved multipath signals are present at the sensors, if proper measures
are not taken, multiple tracks will be formed for a single target. In typical radar systems, these spurious
tracks are removed from tracking, and therefore the information carried in such target return tracks are wasted.
In multipath environment, in every scan the number of sensor measurements from a target is equal to the number
of resolved signals received by different propagation modes. The data association becomes more complex as this
is in contrary to the standard data association problem whereas the total number of sensor measurements from
a target is equal to at most one. This leads to a challenging problem of fusing the direct and multipath measurements
from the same target. We showed in our evaluations that incorporating multipath information improves
the performance of the algorithm significantly in terms of estimation error. Simulation results are presented to
show the effectiveness of the proposed method.
The most popular and well-studied estimation method is the Kalman filter (KF), which was introduced in the
1960s. It yields a statistically optimal solution for linear estimation problems. The smooth variable structure
filter (SVSF) is a relatively new estimation strategy based on sliding mode theory, and has been shown to be
robust to modeling uncertainties. The SVSF makes use of an existence subspace and of a smoothing boundary
layer to keep the estimates bounded within a region of the true state trajectory. This article discusses the
application of two estimation strategies (the KF and the SVSF) on a multi-target tracking problem.
KEYWORDS: Radar, Signal to noise ratio, Detection and tracking algorithms, Antennas, Receivers, Transmitters, Target detection, Electronic filtering, Surveillance, Signal generators
Multiple-Input Multiple-Output (MIMO) radars with widely-separated antennas have attracted much attention
in recent literature. The highly efficient performance of widely-separated MIMO radars in target detection
compared to multistatic radars have been widely studied by researchers. However, multiple target localization
by the enlightened structure has not been sufficiently explored. While Multiple Hypothesis Tracking (MHT)
based methods have been previously applied for target localization, in this paper, the well-known 2-D assignment
method is used instead in order to handle the computational cost of MHT. The assignment based algorithm works
in a signal-level mode. That is, signals in receivers are first matched to different transmitters and, then, outputs
of matched filters are used to find the cost of each combination in the 2-D assignment method. The main benefit
of 2-D assignment is to easily incorporate new targets that are suitable for targets with multiple scatters where a
target may be otherwise unobservable in some pairs. Simulation results justify the capability of 2-D assignment
method in tackling multiple target localization problems, even in relatively low SNRs.
In this paper, the problem of tracking multiple targets in unknown clutter background using the Joint Integrated
Probabilistic Data Association (JIPDA) tracker and the Multiple Hypotheses Tracker (MHT) is studied. It is
common in real tracking problems to have little or no prior information on clutter background. Furthermore, the
clutter backgroundmay be dynamic and evolve with time. Thus, in order to get accurate tracking results, trackers
need to estimate parameters of clutter background in each sampling instant and use the estimate to improve
tracking. In this paper, incorporated with the JIPDA tracker or the MHT algorithm, a method based on Nonhomogeneous
Poisson point processes is proposed to estimate the intensity function of non-homogeneous clutter
background. In the proposed method, an approximated Bayesian estimate for the intensity of non-homogeneous
clutter is updated iteratively through the Normal-Wishart Mixture Probability Hypothesis Density (PHD) filter
technique. Then, the above clutter density estimate is used in the JIPDA algorithm and the MHT algorithm for
multitarget tracking. It is demonstrated thorough simulations that the proposed clutter background estimation
method improves the performance of the JIPDA tracker in unknown clutter background.
Target classification is of great importance for modern tracking systems. The classification results could be
fed back to the tracker to improve tracking performance. Also, classification results can be applied for target
identification, which is useful in both civil and military applications. While some work has been done on
Joint Tracking and Classification (JTC), which can enhance tracking results and make target identification
feasible, a common assumption is that the statistical description of classes is predefined or known a prior.
This is not true in general. In this paper, two automatic multiple target classification algorithms, which can
automatically classify targets without prior information, are proposed. The algorithms learn the class description
from the target behavior history. The input to the algorithm is the noisy target state estimate, which in turn
depends on target class. Thus, class description is learnt from the target behavior history rather than being
predefined. This motivates the proposed two-level tracking and classification formulation for automatic multiple
target classification. The first level consists of common tracking algorithm such as the Joint Probability Data
Association (JPDA), the Multiple Hypothesis Tracking (MHT) or the Probability Hypothesis Density (PHD)
filter. In the second level, a Mean-Shift (MS) classifier and a PHD classifier are applied to learn the class
descriptions respectively based on the state estimations from the first level tracker. The proposed algorithms
only require the kinematic measurements from common radar. However, feature information can be easily
integrated. Besides theoretical derivations, extensive experiments based on both simulated and real data are
performed to verify the efficiency of the proposed technique.
KEYWORDS: Personal digital assistants, Target detection, Filtering (signal processing), Surveillance, Time metrology, Electronic filtering, Sensors, Digital filtering, Algorithm development, Detection and tracking algorithms
Data association is the key component in single or multiple target tracking algorithms with measurement origin.
Probabilistic Data Association (PDA), in which all validated measurements are associated probabilistically to
the predicted estimate, is a well-known method to handle the measurement origin uncertainty. In PDA, the
effect of measurement origin uncertainty is incorporated into the updated covariance by adding the spread of the
innovations term. The updated covariance may become very large after few time steps in high clutter scenarios
due to spread of the innovations term. Large covariance results in a large gate, which is used to limit the possible
measurements that could have originated from the target. Hence, the track will be lost and estimate will just
follow the prediction. Also, large gate will make the well-separated target assumption invalid, even if the targets
are well-separated. Hence, after a few time steps all the targets in the surveillance region come under the same
group, making the Joint Probabilistic Data Association (JPDA). In this paper, adaptive gating techniques are
proposed to avoid the steady increase in the updated covariance in high clutter. The effectiveness of the proposed
techniques is demonstrated on simulated data.
In this paper an efficient approach to nonlinear non-Gaussian state estimation based on spline filtering is presented.
The estimation of the conditional probability density of the unknown state can be ideally achieved
through Bayes rule. However, the associated computational requirements make it impossible to implement this
online filter in practice. In the general particle filtering problem, estimation accuracy increases with the number
of particles at the expense of increased computational load. In this paper, B-Spline interpolation is used to
represent the density of the state pdf through a low order continuous polynomial. The motivation is to reduce
the computational cost. The motion of spline control points and corresponding coefficients is achieved through
implementation of the Fokker-Planck equation, which describes the propagation of state probability density
function between measurement instants. This filter is applicable for a general state estimation problem as no
assumptions are made about the underlying probability density.
In this paper, the previous work multipath-assisted multitarget tracking using multiframe assignment is extended
to the case where there are uncertainties in multipath reflection points at the receiver. An algorithm is proposed
for initiating and tracking multiple targets using multiple transmitters and receivers. This algorithm is capable of
exploiting multipath target returns from distinct and unknown propagation modes. When multipath returns are
not utilized appropriately within the tracker, (e.g., discarded as clutter or incorporated with incorrect propagation
mode assumption) the potential information in the multipath returns is lost. In real scenarios, it is more
appropriate to assume that the locations of the reflection points/surfaces are not accurately known.
Integrating multipath information into the tracker by correctly identifying the multipath mode and identifying
the reflection point can help improve the accuracy of tracking. The challenge in improving tracking results using
multipath measurements is the fusion of direct and multipath measurements from the common target when the
multipath-reflection mode is unknown. The problem becomes even more challenging with false alarms and missed
detections. We propose an algorithm to track the target with uncertainty in multipath reflection points/surface
using the multiframe assignment technique. Simulation results are presented to show the effectiveness of the
proposed algorithm on a ground target tracking problem.
KEYWORDS: Receivers, Transmitters, 3D metrology, Detection and tracking algorithms, 3D acquisition, Target detection, Sensors, Signal to noise ratio, Radar, Fiber optic illuminators
Passive Coherent Location (PCL) is a low-cost system for tracking of air targets clandestinely using illuminators of
opportunity such as FM broadcast and digital TV. Due to an increased interest in PCL systems, researchers have
been working on different configurations of available sources of opportunity and receivers capable of extracting
plots from reflected signals of opportunity. The configuration can be multiple-transmitter-single-receiver or singletransmitter-
multiple-receiver. Unlike standard radar systems, which can be optimized for detection probability
and/or false alarm rate using different transmitted signals and adaptive thresholding, PCL systems are prone to
poor detection due to low signal-to-noise (SNR). This leads to high clutter with low probability of detection of
target of interest. In this work, we implement a multisensor-multitarget tracking system that fuses measurements
from different PCL systems to improve tracking results. The benefits of the fusion are demonstrated using real
data from NATA SET Panel 108 on PCL as well as simulated data.
In this paper an algorithm for multipath-assisted multitarget tracking using multiframe assignment is proposed
for initiating and tracking multiple targets using one or more transmitters and receivers. This algorithm is capable
of exploiting multipath target returns from distinct propagation modes that are resolvable by the receiver. When
resolved multipath returns are not utilized within the tracker, i.e., discarded as clutter, potential information
conveyed by the multipath detections of the same target is wasted. In this case, spurious tracks are formed using
target-originated multipath measurements, but with an incorrect propagation mode assumption. Integrating
multipath information into the tracker (and not discarding it) can help improve the accuracy of tracking and
reduce the number of false tracks. The challenge in improving tracking results using multipath measurements
is the fusion of direct and multipath measurements from the common target. The problem will be considered
in an environment with false alarms and missed detections. We propose a multiframe assignment technique to
incorporate multipath information. The simulation results are presented to show the effectiveness of the proposed
algorithm with an example of tracking ground targets.
In this paper, methods of tracking multiple targets in non-homogeneous clutter background is studied. In many
scenarios, after detection process, measurement points provided by the sensor (e.g., sonar, infrared sensor, radar)
are not distributed uniformly in the surveillance region. On the other hand, in order to obtain accurate results,
the target tracking filter requires information about clutter's spatial density. Thus, non-homogeneous clutter
point spatial density has to be estimated based on the measurement point set and tracking filter's outputs. Also,
due to the requirement of compatibility, it is desirable for this estimation method to be integrated into current
tracking filters. In this paper, a recursive maximum likelihood method and an approximated Bayesian method
are proposed to estimate the clutter point spatial density in non-homogeneous clutter background and both will
in turn be integrated into Probability Hypothesis Density (PHD) filter. Here, non-homogeneous Poisson point
processes, whose intensity function are assumed to be mixtures of Gaussian functions, are used to model clutter
points. The mean and covariance of each Gaussian function is estimated and used in the update equation of the
PHD filter. Simulation results show that the proposed methods are able to estimate the clutter point spatial
density and improve the performance of PHD filter over non-homogeneous clutter background.
The Probability Hypothesis Density (PHD) filter is a powerful new tool in the field of multitarget tracking. Unlike
classical multi-target tracking approaches, such as Multiple Hypothesis Tracking (MHT), in each scan it provides a
complete solution to multi-target state estimation without the necessity for explicit measurement-to-track data
association. The PHD filter recursively propagates the first order moment of the multi-target posterior. This allows us to
determine the expected number of targets as well as their state estimates at each scan. However, there is no implicit
connection between the target state estimates in consecutive scans. In this paper, a new cluster-based approach is
proposed for track labeling in the Sequential Monte Carlo (SMC i.e. particle filter based) PHD filter. The method
associates a likelihood vector to each particle in the SMC estimate. This vector indicates the likelihood that the particle
estimate belongs to each of the established target tracks. This likelihood vector is propagated along with the PHD
moment and updated with the PHD function. By maintaining a set of associations from scan to scan, the new method
provides a complete PHD solution for a multi-target tracking application over time. The method is tested on both clean
and noisy multi-target tracking scenarios and the results are compared to some previously published methods.
Probability Hypothesis Density (PHD) filter is a unified framework for multitarget tracking and provides estimates
for a number of targets as well as individual target states. Sequential Monte Carlo (SMC) implementation
of a PHD filter can be used for nonlinear non-Gaussian problems. However, the application of PHD based state
estimators for a distributed sensor network, where each tracking node runs its own PHD based state estimator,
is more challenging compared with single sensor tracking due to communication limitations. A distributed state
estimator should use the available communication resources efficiently in order to avoid the degradation of filter
performance. In this paper, a method that communicates encoded measurements between nodes efficiently while
maintaining the filter accuracy is proposed. This coding is complicated in the presence of high clutter and
instantaneous target births. This problem is mitigated using novel adaptive quantization and encoding techniques.
The performance of the algorithm is quantified using a Posterior Cramer-Rao Lower Bound (PCRLB),
which incorporates quantization errors. Simulation studies are performed to demonstrate the effectiveness of the
proposed algorithm.
In this paper, a new state estimation algorithm for estimating the states of targets that are separable into
linear and nonlinear subsets with non-Gaussian observation noise distributed according to a mixture of Gaussian
functions is proposed. The approach involves modeling the collection of targets and measurements as random
finite sets and applying a new Rao-Blackwellised Approximate Conditional Mean Probability Hypothesis Density
(RB-ACM-PHD) recursion to propagate the posterior density. The RB-ACM-PHD filter jointly estimates the
time-varying number of targets and the observation sets in the presence of data association uncertainty, detection
uncertainty, noise and false alarms. The proposed algorithm approximates a mixture Gaussian distribution with a
moment-matched Gaussian in the weight update phase of the filtering recursion. A two dimensional maneuvering
target tracking example is used to evaluate the merits of the proposed algorithm. The RB-ACM-PHD filter
results in a significant reduction in computation time while maintaining filter accuracies similar to the standard
sequential Monte Carlo PHD implementation.
Passive Coherent Location (PCL) systems use existing commercial signals (e.g., FM broadcast, digital TV) as
the illuminators of opportunity in air defence systems. PCL Sytems have many advantages such as low cost,
covert operation and low vulnerability to electronic counter measures, over conventional radar systems. The main
disadvantage of PCL systems is that the transmitter locations and the transmitted signals cannot be controlled.
Thus, it is possible to have multiple transmitters that transmit the same signal/frequency inside the coverage
region of the receiver. Thus, multiple measurements that originated from different transmitters and reflected
by the same target will be received. Even though using multiple transmitters will facilitate better estimates
of the target states due to spatial diversity, one cannot use these measurements without resolving transmitter
and measurement origin uncertainties. This adds another level of complexity to the standard data association
problem where the uncertainty is only in measurement origins. That is, there are two uncertainties that need to
be resolved in order to track multiple targets. One is the measurement-to-target association and the other is the
measurement-to-transmitter association. In this work, a tracking algorithm is proposed to track multiple targets
using PCL systems with the above data association uncertainties. The efficiency of the proposed algorithm is
demonstrated on realistically simulated data.
The Interacting Multiple Model (IMM) estimator has been proven to be effective in tracking agile targets.
Smoothing or retrodiction, which uses measurements beyond the current estimation time, provides better estimates
of target states. Various methods have been proposed for multiple model smoothing in the literature.
In this paper, a new smoothing method, which involves forward filtering followed by backward smoothing while
maintaining the fundamental spirit of the IMM, is proposed. The forward filtering is performed using the standard
IMM recursion, while the backward smoothing is performed using a novel interacting smoothing recursion.
This backward recursion mimics the IMM estimator in the backward direction, where each mode conditioned
smoother uses standard Kalman smoothing recursion. Resulting algorithm provides improved but delayed estimates
of target states. Simulation studies are performed to demonstrate the improved performance with a
maneuvering target scenario. The comparison with existing methods confirms the improved smoothing accuracy.
This improvement results from avoiding the augmented state vector used by other algorithms. In addition, the
new technique to account for model switching in smoothing is a key in improving the performance.
Multiple-Input Multiple-Output (MIMO) radars are a new generation of radar systems that bring with them
many benefits compared to the traditional phased-array radars. This paper discuses localization techniques for
multiple targets when a MIMO radar is used as a measurement tool. A multiple hypotheses-based approach
is used to estimate parameters of targets from raw measurements. Received amplitudes and associated range
bins are taken as raw measurements. The multiple hypothesis-based method is implemented in two steps. First,
hypotheses are initialized using the fist q pairs of transmitters and receivers. Then, a sequential method is
applied to initial hypotheses to find final estimates of targets. A comparison is also made between multistatic
and MIMO radars for target detection and localization via simulations. The effect of putting threshold on raw
data is taken into consideration in both detecting and localizing targets for multistatic radars. Finally, simulation
results confirm the superiority of MIMO radars for multiple target localization.
This paper discusses a target tracking system that provides improved estimates of target states using target
orientation information in addition to standard kinematic measurements. The objective is to improve state
estimation of highly maneuverable targets with noisy kinematic measurements. One limiting factor in obtaining
accurate state estimates of highly maneuvering targets is the high level of uncertainty in velocity and acceleration.
The target orientation information is helpful in alleviating this problem to accurately determine the velocity and
acceleration components. However, there is no sensor that explicitly measures target orientation. In this paper,
the Observable Operator Model (OOM) is used together with multiple sensor information to estimate target
orientation measurement. This is done by processing the sensor feature measurements from different aspect
angles and the estimated target orientation measurement is used in conjunction with kinematic measurements
to conclusively estimate target states. Simulation results show that the incorporation of target orientation can
enhance the tracking performance in the presence of fast moving and/or maneuvering targets. In addition, the
Posterior Cramer-Rao lower bound (PCRLB) that quantifies the achievable performance is derived. It is shown
that the proposed estimator meets the PCRLB.
Passive sonar is widely used in practice to covertly detect maritime vessels. However, the detection of stealthy
vessels often requires active sonar. The risk of the overt nature of active sonar operation can be reduced by
using multistatic sonar techniques. Cheap sonar sensors that do not require any beamforming technique can be
exploited in a multistatic system for spacial diversity. In this paper, Gaussian mixture probability hypothesis
density (GMPHD) filter, which is a computationally cheap multitarget tracking algorithm, is used to track multiple
targets using the multistatic sonar system that provides only bistatic range and Doppler measurements.
The filtering results are further improved by extending the recently developed PHD smoothing algorithm for
GMPHD. This new backward smoothing algorithm provides delayed, but better, estimates for the target state.
Simulations are performed with the proposed method on a 2-D scenario. Simulation results present the benefits
of the proposed algorithm.
The joint target tracking and classification using target-to-sensor aspect-dependent Radar Cross Section (RCS)
and kinematic data for multistatic sonar network is presented in this paper. The scattered signals measured from
different orientations of a target may vary due to aspect-dependant RCS. A complex target may contain several
dozen significant scattering centers and dozens of other less significant scatterers. Because of this multiplicity
of scatterers, the net RCS pattern exhibits high variation with aspect angle. Thus, radar cross sections from
multiple aspects of a target, which are obtained via multiple sensors, will help in accurately determining the target
class. By modeling the deterministic relationship that exits between RCS and target aspect, both the target class
information and the target orientation can be estimated. Kinematic data are also very helpful in determining the
target class as it describes the target motion pattern and its orientation. The proposed algorithm exploits the
inter-dependency of target state and the target class using aspect-dependent RCS and kinematic information in
order to improve both the state estimates and classification of each target. The simulation studies demonstrate
the merits of the proposed joint target tracking and classification algorithm based on aspect-dependant RCS and
kinematic information.
In this paper, we consider the tracking of multiple targets in the presence of clutter with poorly localized sensors in
multistatic sensor networks. In multistatic sensor networks, we have a few active sensors that emit the signals and
many passive sensors that receive the signals originated from the active sensors and reflected by the targets and
clutter. In anti-submarine warfare, sensors are typically deployed from aircraft. Optimal tracking performance
can be achieved if all the sensor locations are known. However, in general, sensor deployment accuracy is poor,
and sensors can also drift significantly over time. Hence, the location uncertainties will increase with time. If
the sensors have global position system (GPS) receiver, then their locations can be located with reasonable
accuracy. However, most of the cheap sensors do not have a GPS, and therefor, location uncertainties must
be taken in to consideration while tracking. An advantage of multistatic sensors compared to independent
monostatic sensors is that the sensors can also be tracked accurately. In this paper, we propose how to improve
the tracking performance of multiple targets by incorporating sensor uncertainties. We obtain a bound on the
tracking performance with location uncertainties being taken into consideration, and propose a technique to
select a subset of sensors (if only a few of the available sensors can be used at any measurement time) that
should be used at each time step based on the bound. Simulation results illustrating the performance of the
proposed algorithms are also presented.
Passive coherent location (PCL), which uses the commercial signals as illuminators of opportunity, is an emerging
technology in air defense systems. The advantages of PCL are low cost, low vulnerability to electronic counter
measures, early detection of stealthy targets and low-altitude detection. However, limitations of PCL include lack
of control over illuminators, poor bearing accuracy, time-varying sensor parameters and limited observability.
In this paper, multiple target tracking using PCL with high bearing error is considered. In this case, the
challenge is to handle high nonlinearity due to high measurement error. In this paper, we implement the
converted measurement Kalman filter, unscented Kalman filter and particle filter based PHD filter for PCL
radar measurements and compare their performances.
Due to the availability of cheap passive sensors, it is possible to deploy a large number of them for tracking
purposes in anti-submarine warfare (ASW). However, modern submarines are quiet and difficult to track with
passive sensors alone. Multistatic sensor networks, which have few transmitters (e.g., dipping sonars) in addition
to passive receivers, have the potential to improve the tracking performance. We can improve the performance
further by moving the transmitters according to existing target states and any possible new targets. Even
though a large number of passive sensors are available, due to frequency, processing power and other physical
limitations, only a few of them can be used at any one time. Then the problems are to decide the path of the
transmitters and select a subset from the available passive sensors in order to optimize tracking performance.
In this paper, the PCRLB, which gives a lower bound on estimation uncertainty, is used as the performance
measure. We present an algorithm to decide jointly the optimal path of the movable transmitters, by considering
their operational constraints, and the optimal subset of passive sensors that should be used at each time steps for
tracking multiple, possibly time-varying, number of targets. Finding the optimal solution in real time is difficult
for large scale problems, and we propose a genetic algorithm based suboptimal solution technique. Simulation
results illustrating the performance of the proposed algorithm are also presented.
In this paper, we consider the problem of sensor resource management in decentralized tracking systems with
asynchronous communication and sensor selection. Due to the availability of cheap sensors, it is possible to
deploy a large number of sensors and use them to monitor a large surveillance region. Even though a large
number of sensors are available, due to frequency, power and other physical limitations, only a maximum of
certain number of sensors can be used by any fusion center at any one time. The problem is then to select the
sensor subsets that should be used at each sampling time in order to optimize the tracking performance under
the given constraints. In recent papers, we proposed algorithms to handle the above problem in centralized,
distributed and decentralized architectures. However, in the paper for sensor subset selection for decentralized
architecture, we assumed that all the fusion centers change their sensors at the same time, and their sensor
change time interval is fixed and known. However, in general case, fusion centers may change their sensors at
different time, and their sensor change intervals may not be fixed. In this case, the sensor management become
more difficult. We have to decide when to change the subsets, and how to incorporate the changes made in the
neighboring fusion centers in selecting the future sensor subsets. We propose an efficient algorithm to handle the
above problem in real time. Simulation results illustrating the performance of the proposed algorithm are also
presented.
KEYWORDS: Signal to noise ratio, Particles, Sensors, Particle filters, Point spread functions, Detection and tracking algorithms, Clouds, Target detection, Monte Carlo methods, Digital filtering
In this paper we address the problem of detecting and tracking a single dim target in unknown background noise.
Several methodologies have been developed for this problem, including track-before-detect (TBD) methods which
work directly on unthresholded sensor data. The utilization of unthresholded data is essential when signal-to-noise
ratio (SNR) is low, since the target amplitude may never be strong enough to exceed any reasonable
threshold. Several problems arise when working with unthresholded data. Blurring and non-Gaussian noise
can easily lead to very complicated likelihood expressions. The background noise also needs to be estimated.
This estimate is a random variable due to the random nature of the background noise. We propose a recursive
TBD method which estimates the background noise as part of its likelihood evaluation. The background noise
is estimated by averaging over nearby sensor cells not affected by the target. The uncertainty of this estimate
is taken into account by the likelihood evaluation, thereby yielding a more robust TBD method. The method
is implemented using sequential Monte Carlo evaluation of the optimal Bayes equations, also known as particle
filtering. Simulation results show how our method allows detection and tracking to be carried out in an uncertain
environment where current recursive TBD methods fail.
Invited Panel Discussion Topics: Research Challenges in Sensor Management; Fundamental Statistics for Resource Management; Issues in Formulating Utility Functions for Sensor
Management; Resource Management for Distributed Attention in Sensor Networks; Research Challenges in Network and Service Management for Distributed Net-Centric Fusion; Resource Management in Sensor Networks; Performance Metrics for Combed Tracking and Sensor Management.
In this paper, we consider the problem of sensor resource management in decentralized tracking systems. Due to the availability of cheap sensors, it is possible to use a large number of sensors and a few fusion centers (FCs) to monitor a large surveillance region. Even though a large number of sensors are available, due to frequency, power and other physical limitations, only a few of them can be active at any one time. The problem is then to select sensor subsets that should be used by each FC at each sampling time in order to optimize the tracking performance subject to their operational constraints. In a recent paper, we proposed an algorithm to handle the above issues for joint detection and tracking, without using simplistic clustering techniques that are standard in the literature. However, in that paper, a hierarchical architecture with feedback at every sampling time was considered, and the sensor management was performed only at a central fusion center (CFC). However, in general, it is not possible to communicate with the CFC at every sampling time, and in many cases there may not even be a CFC. Sometimes, communication between CFC and local fusion centers might fail as well. Therefore performing sensor management only at the CFC is not viable in most networks. In this paper, we consider an architecture in which there is no CFC, each FC communicates only with the neighboring FCs, and communications are restricted. In this case, each FC has to decide which sensors are to be used by itself at each measurement time step. We propose an efficient algorithm to handle the above problem in real time. Simulation results illustrating the performance of the proposed algorithm are also presented.
In this paper, we consider the general problem of dynamic assignment of sensors to local fusion centers (LFCs) in a distributed tracking framework. As a result of recent technological advances, a large number of sensors can be deployed and used for tracking purposes. However, only a certain of number of sensors can be used by each local fusion center due to physical limitations. In addition, the number of available frequency channels is also limited. We can expect that the transmission power of the future sensors will be software controllable within certain lower and upper limits. Thus, the frequency reusability and the sensor reachability can be improved. Then, the problem is to select the sensor subsets that should be used by each LFC and to find their transmission frequencies and powers, in order to maximize the tracking accuracies as well as to minimize the total power consumption. This is an NP-hard multi-objective mixed-integer optimization problem. In the literature, sensors are clustered based on target or geographic location, and then sensor subsets are selected from those clusters. However, if the total number of LFCs is fixed and the total number of targets varies or a sensor can detect multiple targets, target based clustering is not desirable. Similarly, if targets occupy a small part of the surveillance region, location based clustering is also not optimal. In addition, the frequency channel limitation and the advantage of the variable transmitting power are not discussed well in the literature. In this paper, we give the mathematical formulation of the above problem. Then, we present an algorithm to find a near optimal solution to the above problem in real time. Simulation results illustrating the performance of the sensor array manager are also presented.
In this paper we consider the general problem of managing an array of sensors in order to track multiple targets in the presence of measurement origin uncertainty. There are two complicating factors: the first is that because of physical limitations (e.g., communication bandwidth) only a small number of sensors can be utilized at any one time. The second complication is that the associations of measurements to targets/clutter are unknown. It
is this second factor that extends our previous work [14]. Hence sensors must be utilized in an efficient manner to alleviate association ambiguities and allow accurate target state estimation. Our sensor management technique is then based on controlling the Posterior Cramer-Rao Lower Bound (PCRLB), which provides a measure of the optimal achievable accuracy of target state estimation. Only recently have expressions for multitarget PCRLBs been determined [7], and the necessary simulation techniques are computationally expensive. However, in this paper we propose some approximations that reduce the computational load and we present two sensor selection
strategies for closely spaced (but, resolved) targets. Simulation results show the ability of the PCRLB based sensor management technique to allow efficient utilization of the sensor resources, allowing accurate target state estimation.
Large-scale sensor array management has applications in a number of target tracking problems. For example, in ground target tracking, hundreds or even thousands of unattended ground sensors (UGS) may be dropped over a large surveillance area. At any one time it may then only be possible to utilize a very small number of the available sensors at the fusion center because of bandwidth limitations. A similar situation may arise in tracking sea surface or underwater targets using a large number of sonobuoys. The general problem is then to select a subset of the available sensors in order to optimize tracking performance. The Posterior Cramer-Rao Lower Bound (PCRLB), which quantifies the obtainable accuracy of target state estimation, is used as the basis for network management. In a practical scenario with even hundreds of sensors, the number of possible sensor
combinations would make it impossible to enumerate all possibilities in real-time. Efficient local (or greedy) search techniques must then be used to make the computational load manageable. In this paper we introduce an efficient search strategy for selecting a subset of the sensor array for use during each sensor change interval in multi-target tracking. Simulation results illustrating the performance of the sensor array manager are also presented.
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