KEYWORDS: Radar, Modulation, Frequency modulation, Fermium, Amplitude modulation, Signal to noise ratio, Doppler effect, Target detection, Monte Carlo methods, Signal processing
Data containing the radar signature of amoving person on the groundwere collected at ranges of up to 30 kmfroma moving
airborne platform using the DRDC Ottawa X-bandWideband Experimental Airborne Radar (XWEAR). The human target
radar echo returns were found to possess a characteristic amplitude modulated (AM) and frequency modulated (FM)
signature which could be usefully characterized in terms of conventional AM and FM modulation parameters. Human
detection performance after space time adaptive processing is frequently limited by false alarms arising from incomplete
cancellation of large radar cross-section discretes during the whitening step. However, the clutter discretes possess different
modulation characteristics from the human targets discussed above. The ability of pattern classification techniques to use
this parameter measurement space to distinguish between human targets and clutter discretes is explored and preliminary
results presented.
An examination of the application of Space Time Adaptive Processing (STAP) techniques to real, multi-channel, medium
grazing angle, radar sea clutter data is undertaken and the detection performance is quantified against simulated moving
maritime surface targets. The application of sub-optimal STAP approaches to the maritime radar detection problem is
shown to be complicated by non-stationarity of sea clutter and rapid variations of the sea clutter spectrum due to transient
wave activity. Observed performance gains from maritime STAP are much more limited than those observed for Ground
Moving Target Indication (GMTI) due to the inherent spectral width of sea clutter and the slow Doppler velocities of
maritime targets. Three sub-optimal STAP processing architectures are examined and PRI-Staggered Post-Doppler is
shown to provide consistently superior detection performance for the data set in question.
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.
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.
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.
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: 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.
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.
KEYWORDS: Data modeling, Radar, Data processing, Signal processing, Global Positioning System, Expectation maximization algorithms, Target detection, Data conversion, Binary data, Mathematical morphology
In this paper, the problem of detection, classification and tracking of highly manoeuvring boats in sea clutter is
considered. The considered problem is challenging due to numerous inherent issues: abrupt direction changes,
high level of false alarms, lowered detectability, group movement and re-grouping, among many others. The
results of applying a proposed measurement extraction and estimation technique to a set of real data from
DRDC-Ottawa trials using Ground Moving Target Indicator (GMTI) radar are described. Real radar data
containing a small manoeuvring boat in sea clutter is processed using Expectation Maximization (EM) Gaussian
Mixture Model (GMM) based estimation. A trial was undertaken to collect data against highly maneuvering
speedboats in the sea. All the data were collected in the GMTI single-channel high-resolution spotlight mode.
True data were collected using GPS recording equipment. Real data processing results are presented.
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.
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.
This paper describes an empirical approach to characterizing and simulating sea clutter based on the identification
and grouping of so called 'clutter events'. Clutter events are grouped based on azimuth width (or equivalently
existence time). The groups are characterized with regards to mean spike amplitude and relative occurrence
rate of events. The character of the azimuth amplitude profile within a group is further characterized in terms
of associated amplitude probability distribution function (apdf), amplitude profile and variance. The multiparameter
characterization is shown to be sufficiently robust to allow the simulation of a scene that exhibits
not only a qualitative similarity to the real clutter but a demonstrable quantitative correspondence. When the
cumulative distribution function (cdf) of the clutter simulated per the new approach is compared with that of
the real sea clutter returns an excellent match is achieved. Thus the new simulation method is shown to be
consistent with the simpler but widely used apdf characterization.
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.
KEYWORDS: Nonlinear filtering, Electronic filtering, Digital filtering, Detection and tracking algorithms, Monte Carlo methods, Computer simulations, Filtering (signal processing), Defense and security, Numerical analysis, Process modeling
This paper presents a novel continuous approximation approach to nonlinear/non-Gaussian Bayesian tracking.
A good representation of the probability density and likelihood functions is essential for the effectiveness of
nonlinear filtering algorithms since these functions could be multi-modal. The proposed approach uses B-spline
interpolation to represent the density and likelihood functions and tensor product approaches to extend the
filter to multidimensional case. The filter is applicable under most general circumstances since it does not make
any assumption on the form of the underlying probability density. An advantage of the proposed method is
that it retains accurate density information in a continuous low-order polynomial form and finding the target
probability in any region of the state space is straightforward. Further processing based on probability density
such as finding the higher order moments of the state estimates could also be performed with less computational
power. Simulation results are presented to demonstrate the proposed algorithm.
A passive coherent location (PCL) system exploits the ambient FM radio or television signals from powerful
local transmitters, which makes it ideal for covert tracking. In a passive radar system, also known as PCL
system, a variety of measurements can be used to estimate target states such as direction of arrival (DOA), time
difference of arrival (TDOA) or Doppler shift. Noise and the precision of DOA estimation are main issues in
a PCL system and methods such as conventional beam forming (CBF) algorithm, algebraic constant modulus
algorithm (ACMA) are widely analyzed in literature to address them. In practical systems, although it is
necessary to reduce the directional ambiguities, the placement of receivers closed to each other results in larger
bias in the estimation of DOA of signals, especially when the targets move off bore-sight. This phenomenon leads
to degradation in the performance of the tracking algorithm. In this paper, we present a method for removing
the bias in DOA to alleviate the aforementioned problem. The simulation results are presented to show the
effectiveness of the proposed algorithm with an example of tracking airborne targets.
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.
In this paper, we present an efficient data association algorithm for tracking ground targets that perform move-stop-move maneuvers using ground moving target indicator (GMTI) radar. A GMTI radar does not detect the
targets whose radial velocity falls below a certain minimum detectable velocity. Hence, to avoid detection enemy
targets deliberately stop for some time before moving again. When targets perform move-stop-move maneuvers,
a missed detection of a target by the radar leads to an ambiguity as to whether it is because the target has
stopped or due to the probability of detection being less than one. A solution to track move-stop-move target
tracking is based on the variable structure interacting multiple model (VS-IMM) estimator in an ideal scenario
(single target tracking with no false measurements) has been proposed. This solution did not consider the data
association problem. Another solution, called two-dummy solution, considered the data association explicitly and
proposed a solution based on the multiframe assignment algorithm. This solution is computationally expensive,
especially when the scenario is complex (e.g., high target density) or when one wants to perform high dimensional
assignment. In this paper, we propose an efficient multiframe assignment-based solution that considers the second
dummy measurement as a real measurement than a dummy. The proposed algorithm builds a less complex
assignment hypothesis tree, and, as a result, is more efficient in terms of computational resource requirement.
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