Wind is a well known performance detractor for telescope pointing. A dome is often used for larger telescopes to minimize the wind load on the telescope. However, the dome does not eliminate the impact of wind but rather alters its static and dynamic load on the telescope structure. Unfortunately, predicting the interaction of the wind and dome on telescope performance is quite difficult so even a dome design that mitigates wind effects does not allow the telescope control designer to ignore the wind load. The control system must be prepared for on-site modifications to accommodate a dynamic wind disturbance and the combined telescope control and structure design dictate available control solution methods and their effectiveness. This paper quantifies the impact of wind induced jitter at the system level and examines the nature of the wind disturbance and control system solution alternatives. While the chosen solution is straightforward, its practical implementation involves subtleties in the control and structure cooperative design. The author employs recent test data to support the conclusions.

A common requirement in electro-optical surveillance or weapon delivery systems is to point at or scan a target from a moving vehicle. The required gimbal commands are developed and presented for a variety of gimbal configurations using position and / or velocity information such as is commonly available from an onboard INS or GPS system. Both pointing angle and angular rate approaches are considered along with considerations for back-scanning with, for example, a fast steering mirror. The results are also shown to be applicable as a rate-aiding enhancement to many closed-loop tracking systems. While no fundamentally new techniques are presented in this paper, an organized approach to a common problem is presented along with some control system implementation issues.

Nowadays, armored ground vehicles such as tanks have to face an increasing number of threats. For instance, helicopters have become important threats with mainly more and more improved locking-on mode and firing mode capabilities. Plus, new lighweight ground vehicles are current threats to deal with. Consequently, Search sights have to upgrade their detection, locking-on, tracking and identification capabilities to counter multiple threats on various backgrounds and atmospheric conditions (desert, landscape...). Currently, Infrared sensors are the main core of these Surveillance Systems. In most cases, an operator (tank chief) via a joystick manages them. In this paper, a practical example of Search Sight Management for tanks associated with new automatic processes including operator information feedback is presented. The case studied here is a real-time implemented Semi-Automatic Search Infrared Sensor Suite. The article provides also system performances resulting from Field Trials. They highlight the impact of the new function in the overall system reaction time.

In an airborne optical imaging system, a key function is to command and control the observation direction or line of sight whose aim is to track various targets during a determined period. Indeed, the optical images will be affected by the residue of pointing. Moreover, the airborne environment adds complementary difficulties on the line of sight control. The Line of sight command is composed of three phases : the "designation", the "hanging" and the "tracking" phases. Each of one is characterized by a specific control law. The first one allows to place the instrument line of sight following the provisional target trajectory. The second control law is optimized for the target acquisition and the third one is dedicated to track the target. The acquired imagery allows, after validation of the known target and/or rallying it by human intervention, to calculate an angular deflection for measurement of tracking error. According to the scientific objective of the imaging system, various types of targets could be observed. So the angular deflection measurement is calculated by barycentric or images correlation methods. This information is injected into the second control law which will be substituted, without unhooking, to the first performed for designation. The line of sight of the imaging system is realized with a gyro-mirror for the fine pointing in front of a camera and an independent mechanical framework, supporting the camera and the gyro-mirror. This pedestal offers to the instrument a wide angular field of view but a coarse pointing. These elements individually controllable are dimensioned for the design and realization for the control law. This paper presents each station to study needs for the definition and the realization of the control law for an airborne optical imaging instrument. This paper also describes an approach of the harmonization of the lines of sight of different instruments pointing the same target.

This paper solves a general track fusion problem with feedback when the sensors used are asynchronous, communication delays exist between sensor platforms and track fusion center, and tracks may arrive out- of-sequence. For the proposed linear fusion rule, the solution is shown to be optimal in the minimum mean square sense. The batch processing of incoming tracks provides an elegant solution for the out-of-sequence and latent tracks without special processing of such data.

The symmetric measurement equation (SME) approach to multiple target tracking is revisited using unscented Kalman and particle filters. The unscented Kalman filter (UKF) promises more accurate approximation of nonlinearities and simpler implementation of the SME approach than the EKF. The particle filter implementation offers the ability to explore the limits of the SME approach. In the first portion of this paper, experiences with SME for tracking one-dimensional motion are reviewed. The second portion of this paper discusses the challenges that arise when using the SME approach to track two-dimensional motion and introduces a new set of two-dimensional SME equations. Finally, Taylor series expansions are used to explore differences between Kalman filter-SME pairings. Using the Taylor series representation, we show how the choice of SME formulation affects the representation, and consequently approximation, of uncertainty in the Kalman filters.

An algorithm for solving the simultaneous hyperbolic equations that arise in multilateration using time difference of arrival measurements (TDOA) is presented. This weighted, non-iterative algorithm is able to reduce error by more than 50% over unweighted variations of the algorithm. This algorithm was developed for use in a passive multistatic sensor network. By using low gain antennas and TDOA, we are able to provide platforms with situational awareness while maintaining a low RCS.

Tracking a maneuvering target weakens the performance of predictive-model-based Bayesian state estimators (Kalman Filter). Therefore, the particle is used to track maneuverable targets instead of Kalman filter and its extensions. The particle filter proved more efficiency compared to Kalman filter and its extensions, e.g. Extended Kalman Filter (EKF) and Interacting Multiple Model (IMM). Unfortunately, due to the highly uncertainty and incompleteness of the information in a highly-maneuverable target-tracking problem, the advantage of the particle filter is weakened. Both auxiliary and smoothing particle filter were proposed to overcome this problem. In this paper, we compare the performance of both auxiliary and smoothing particle filter in tracking a highly maneuverable target. We applied both algorithms to track a maneuverable target in a multiple-sensors network. Monte Carlo simulation showed that the smoothing particle filter has a better performance when compared to auxiliary particle filter in tracking a maneuvering target.

Robust and accurate tracking of multiple objects is a key challenge in video surveillance. Tracking algorithms generally suffer from either one or more of the following problems, excluding detection errors. First, objects can be incorrectly interpreted as one of the other objects in the scene. Second, interactions between objects, such as occlusions, may cause tracking errors. Third, globally-optimum tracking is hard to achieve since the combinatorial assignment problem is NP-Complete. We present a modified Multiple-Hypothesis Tracking algorithm, MHT, for globally optimum tracking of moving objects. The system defines five states for tracked objects: appear, disappear, track, split, and merge, and these states cover all the interactions of object pairs. After the detection of objects in the current frame, a resemblance matrix is computed for every object pair. We convert the two-dimensional resemblance matrix into a three-dimensional state-likelihood structure and use a MHT technique to solve the state-assignment problem in 3D. This prevents incorrect assignments due to local minima in the assignment process. Moreover, the method models occlusion cases with the split and merge states. Finally, this method approximates a globally optimum state assignment in polynomial time complexity.

The Passive Ground Moving Target Indication (PGMTI) program was a demonstration of the feasibility of autonomously detecting and tracking moving targets on the ground in real-time. Several scenarios were coordinated with ground vehicles and personnel so that the performance capabilities as well as any limitations of the system could be demonstrated. The objectives of the program were to demonstrate the following for the PGMTI system: 1.Process data in real time *PMTD Algorithm *High Pass Spatial Filter *Tracker, Declaration Logic and Data Logger 2.Meet performance metrics *Probability of Detection; PD >= 80% *Probability of False Detection; PFD <= 0.02% *Probability of Track; PT >= 90% *Probability of False Track; PFT <= 0.01% DRA used two different sensors to demonstrate the PGMTI performance; a near-IR and a mid-IR sensor. The near-IR system operated in real-time, and the mid-IR system was non-real-time. The approach to demonstrate the performance of the PGMTI system was to perform the following two tasks for each planned scenario containing the ground moving targets: 1)Collect the real-time processed data, from the near-IR sensor, which consists of the sensor data and a track file containing data on all tracks established by the system, and to analyze the results on the ground. 2)Collect raw data from the mid-IR sensor, post-process this data, and analyze results to determine performance. The analysis showed that all objectives were met provided there was at least 5% contrast between the ground moving target and its background. The PGMTI system concept demonstrated excellent performance and continued development is warranted.

No abstract provided

Typical automatic target recognition (ATR) systems rely on measurements from images; however, acquiring the image is dependent on knowing the target location. A dynamic sensor manager points a sensor in the general target direction. Once the general target area is identified (coarse resolution), it is imperative that an ATR system increase pixels on target (fine resolution) to maintain accurate target identification. For this paper, we are concerned about maintaining target position by user-tracker reciprocal cueing. From a general wide-area search image, an operator can refine the target location by monitoring or selecting boundary points around a target. The SNAKE tracking algorithm maintains a track on a target from image sequences by developing a contour between points. For measurement drop-out, we predict target covariance from the previous image-target contour through a Kalman filter. The SNAKE-prediction region for a maneuvering target produces a precise target location from which features can be extracted for target recognition. While the SNAKE algorithm is mature, its usefulness for robust tracking is limited in that that sensor must be locked on the target for the entire process. In this development, we utilize track prediction information to follow targets through occlusions, maintain target tracks through sensor dropouts, and fuse operator inputs to refine the target location.

The interpretation of video imagery is the quintessential goal of computer vision. The ability to group moving pixels into regions and then associate those regions with semantic labels has long been studied by the vision community. In urban nighttime scenarios, the difficulty of this task is simultaneously alleviated and compounded. At night there is typically less movement in the scene, which makes the detection of relevant motion easier. However, the poor quality of the imagery makes it more difficult to interpret actions from these motions. In this paper, we present a system capable of detecting moving objects in outdoor nighttime video. We focus on visible-and-near-infrared (VNIR) cameras, since they offer low cost and very high resolution compared to alternatives such as thermal infrared. We present empirical results demonstrating system performance on a parking lot surveillance scenario. We also compare our results to a thermal infrared sensor viewing the same scene.

A high performance tracking system that adaptively adjusts the tracker algorithms and track loop parameters based on real-time scene statistics has been developed and demonstrated against realistic target scenarios. The HPATS provides the capability to acquire and track very low contrast targets in the presence of background clutter and time-vaying target conditions to sub-pixel accuracy. HPATS is applicable to both fire control and terminal guidance applications that incorporate imaging sensors. An overview of the tracking system design, simulation modeling, tracker metrics tools, and field test examples of low contrast target tracking performance is presented. The HPATS technology development included a high fidelity Integrated Flight Simulation (IFS) that modeled the end to end performance of the missile fly out, the target acquisition, the target tracking, aim-point selection, terminal guidance, and lethality.

We propose a new approach for laser guided weapon guidance that minimizes the total active laser target designation time. The weapon makes use of inertial or GPS guidance within a Kalman filtering framework, and maintains covariance information indicating the uncertainty of its knowledge of the weapon-to-target vector. At any time, the missile needs to be sure that it can navigate to any point within the area around the target that is described by this covariance. Therefore, at each moment during the flight, there exists a covariance threshold above which the weapon cannot guarantee its ability to navigate to the target. This threshold will decrease with time as the weapon-to-target distance decreases. In our proposed approach, when the threshold is exceeded, the weapon requests a brief laser designation of the target. The laser designation provides an accurate measurement of the bearing of the target with respect to the missile, and this is used to improve the estimate of the weapon-to-target vector. In turn, this can be fed back into the Kalman filter to improve the internal state estimate. By minimizing laser designation time, this approach reduces the chance of compromise of the designation agent, and of the fact that targeting is taking place. It also achieves the benefit of improving the accuracy of the underlying inertial or other navigational system, or alternatively the estimate of absolute target position.

Correlation tracking is used in civilian and military automatic target recognition and surveillance systems, to track objects based on their 2-dimensional shape. However traditional correlation-tracking systems have difficulty robustly detecting an object when the object is partially obscured by clutter. This paper describes one of the main problems of image-based correlation tracking systems, and proposes a novel solution. The reference image update problem occurs when the tracked object undergoes rapid shape change in the presence of clutter, here the reference image of the target is updated with an image corrupted by clutter, this can cause the system to walk-off and lose track of the target-object. The novel solution presented is based on research into modelling biological vision systems. We developed a prototype system designed to track an object changing size and shape in the presence of obscuring clutter. The system was tested on both real and simulated infrared imagery of aircraft found to be robust in the presence of obscuring clutter.

This paper presents a rotational motion estimation and correction technique for digital image stabilization. An equivalent rotation model is derived so as to accommodate a combined rotational and the translational motion. Based on this model, the rotation center and angle are estimated, and followed by the motion compensation. The suggested estimation algorithm does not require the time consuming parameter searching, while showing a comparable performance to the previous ones.