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PCRLB based multisensor array management for multitarget tracking

PCRLB based multisensor array management for multitarget tra...

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16th conference on signal and data processing of small targets

作者： tharmarasa, R Kirubarajan, T Hernandez, ML McMaster Univ ECE Dept ETF Lab Hamilton ON L8S 4K1 Canada

ISBN: (纸本)081945351X

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.

关键词： sensor management multisensor-multitarget tracking Posterior Cramer-Rao Lower Bound non-linear filtering data association

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Equivalence classes of future paths for sensor allocation and threat analysis

Equivalence classes of future paths for sensor allocation an...

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17th conference on signal and data processing of small targets

作者： Svenson, P Swedish Def Res Agcy Div Command & Control Syst Dept Data & Informat Fus SE-16490 Stockholm Sweden

ISBN: (纸本)0819459186

Sensor allocation and threat analysis are difficult fusion problem that can sometimes be approximately solved using simulations of the future movement of adversary units. In addition to requiring, detailed motion models, such simulation also requires large amounts of computational resources, since a large number of possibilities must be examined. In this paper, we extend our previously introduced framework for doing such simulations more efficiently. the framework is based on defining equivalence classes of future paths of a set of units. In the simplest case, two paths are considered equivalent if they give rise to the same set of observations. For sensor management, each considered sensor plan thus entails an equivalence relation on the set of future paths. this can be used to significantly reduce the number of "alternative futures" that need to be considered for the simulation. For threat analysis, the equivalence relation can instead be based on the perceived threat against own units. We describe how the equivalence classes induced by such relations could be used to improve the visualization of threat analysis systems. User interaction can also be used to refine the equivalence classes;we argue that such interaction will be essential for international operations where is it difficult to define actors and targets.

关键词： sensor management resource allocation threat analysis

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data association combined with the probability hypothesis density filter for multitarget tracking

Data association combined with the probability hypothesis de...

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16th conference on signal and data processing of small targets

作者： Lin, L Bar-Shalom, Y Kirubarajan, T Univ Connecticut Dept ECE Storrs CT 06269 USA

ISBN: (纸本)081945351X

Multiple target tracking requires data association that operates in conjunction with filtering. When multiple targets are closely spaced, the conventional approach (MHT/assignment) may not give satisfactory results. this is mainly because of the difficulty in deciding what the number of targets is. Recently, the probability hypothesis density (PHD) filter, which propagates the PHD or the first moment instead of the full multitarget posterior density, was proposed in. (6-10) In this approach, the integral of the PHD over a region in the state space is the expected number of targets within this region and the peaks in the PHD can be regarded as the estimated locations of the targets at a given time step. In this approach the data association problem is not considered, i.e., the PHD is obtained only for a frame at a time. In our paper, a data association method combined with the PHD approach is proposed for multitarget tracking, i.e., we keep a separate tracker for each target, use the PHD filter to get the estimated number and locations of the targets at each time step, and then perform the "peak-to-track" association, whose results can provide information for PHD peak extraction at the next time step. Besides, by keeping a separate tracker for each target, our approach provides more information than the standard PHD filter. Using our approach, the multitarget tracking can be performed with automatic track initiation, maintenance, spawning, merging and termination. Simulation results demonstrate that our approach is computationally feasible and effective.

关键词： data association probability hypothesis density multitarget tracking peak extraction group target tracking assignment algorithms

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An MDL approach for multiple low observable track initiation

An MDL approach for multiple low observable track initiation

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14th conference on signal and data processing of small targets

作者： Chen, H Kirubarajan, T Bar-Shalom, Y Pattipati, KR Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)0819444782

In this paper the track initiation problem is formulated as multiple composite hypothesis testing using maximum likelihood estimation with probabilistic data association (ML-PDA), an algorithm known to work under very low SNR conditions. this algorithm does not have to make a decision as to which measurement is target originated. the hypothesis testing is based on the minimum description length (MDL) criterion. We first review some well-known approaches for statistical model selection and the advantage of the MDL criterion. then we present an approximate penalty in accounting for the model complexity to simplify the calculation of MDL. Finally, we apply the. MDL approach for the detection and initiation of tracks of incoming tactical ballistic missiles in the exo-atmospheric phase using a surface based electronically scanned array (ESA) radar. the targets are characterized by low SNR, which leads to low detection probability and high false alarm rate. the target acquisition problem is formulated using a batch of radar scans to detect the presence of up to two targets. the ML-PDA estimator is used to initiate the tracks assuming the target trajectories follow a deterministic state propagation. the approximate MDL criterion is used to determine the number of valid tracks in a surveillance region. the detector and estimator are shown to be effective even at 4.4 dB average SNR in a resolution cell (at the end of the signal processing chain).

关键词： track initiation minimum description length maximum likelihood estimator probabilistic data association model selection

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Map integration in tracking

Map integration in tracking

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19th conference on signal and data processing of small targets

作者： Sworder, David D. Boyd, John E. Hutchins, R. G. Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Cub Def Applicat San Diego CA 92186 USA Naval Postgrad Sch Dept EECE Monterey CA USA

ISBN: (纸本)9780819468475

A multiple model tracking algorithm has considerable advantage when the target is moving on a road grid. A map-enhanced algorithm magnifies this by excluding large regions of the 2D motion-space from search. However, integrating the grid of streets and junctions into a recursive algorithm is challenging. this paper presents a map-enhanced, tracker that uses local models tuned to motions on the four cardinal directions. Map compliance is achieved by re-initializing the local kinematic state when a modal change occurs and by moving the local estimate to the map after a measurement update. Unfortunately, the transition dynamics of the modal state depend upon the kinematic state. A proposed junction influence function is based upon a rapidly decaying measure of the distance from the target to the nearest compatible junction.

关键词： nonlinear filters target tracking data fusion

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On track and tracklet fusion filtering

On track and tracklet fusion filtering

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14th conference on signal and data processing of small targets

作者： Drummond, OE CyberRnD Inc Culvar City CA 90230 USA

ISBN: (纸本)0819444782

Track and tracklet fusion filtering is complicated because the estimation errors of tracks from two sources for the same target may be cross-correlated. this cross-correlation of these errors should be taken into account when designing the filter used to combine the track data. this paper addresses the various track and tracklet fusion methods and their impact on communications load and tracking characteristics. In track and tracklet fusion a sequence of measurements is processed at the sensor or platform level to form tracks;then sensor level track data (in the form of tracks or tracklets) for a target is distributed and fused with each other or with a global track. Track Fusion and Tracklet Fusion are also sometimes called Hierarchical Fusion, Federated Fusion, or Distributed Fusion. Track data can include features or other information useful for target classification. One characteristic of track and tracklet fusion that distinguishes one method from another is whether the local track data is combined or if global tracks are maintained and updated using the sensor track data. Another important issue is whether there is filter process noise to accommodate target maneuvers. Some filtering methods are designed for maneuvering targets and others are not. this paper enumerates the various track and tracklet fusion methods for processing data from distributed sensors and their impact on filter performance and communications load.

关键词： target tracking multiple sensor tracking track fusion multiple target tracking sensor data fusion Bayesian methods tracklet distributed tracking estimation and filtering

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Complexity reduction in MHT/MFA tracking

Complexity reduction in MHT/MFA tracking

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17th conference on signal and data processing of small targets

作者： Poore, AB Numerica Corp Ft Collins CO 80527 USA

ISBN: (纸本)0819459186

the performance advantage of the multiple frame data association methods over the single frame methods follows from the ability to hold difficult decisions in abeyance until more information is available and the opportunity to change past decisions to improve current decisions. In dense tracking environments the performance improvements of multiple frame methods over single frame methods is very significant, making it the preferred solution for difficult tracking problems. the price that one pays for this performance gain is the computational complexity (NP-hard) of the resulting data association problem. the number of strings of data or arcs that one forms for N frames of data each containing M reports is M(N) wherein we have omitted missed reports. this number grows exponentially with the length of the window (i.e., N). thus, much preprocessing is required to manage memory usage and to achieve good runtime performance. the control of the computations makes use of a variety of techniques including a) bin gating, b) coarse pair and triple point dynamic gating, c) multiframe gating, d) medium gating based on filter prediction gates, e) fine gating based on likelihood ratios, f) track hypothesis pruning, h) problem partitioning, and i) cluster tracking. While this work comments on many of these methods, the goal is to derive methods bin, coarse pair, and multiframe gating in a simple setting as possible.

关键词： Detection and tracking algorithms Sensors Electronic filtering 3D metrology Computing systems Mahalanobis distance Time metrology Calculus data processing signal processing

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A randomized heuristic approach for multidimensional association in target tracking

A randomized heuristic approach for multidimensional associa...

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16th conference on signal and data processing of small targets

作者： Sinha, A Kirubarajan, T McMaster Univ Dept Elect & Comp Engn ETFLab Hamilton ON L8S 4K1 Canada

ISBN: (纸本)081945351X

the combinatorial optimization problem of multidimensional assignment has been treated with renewed interest because of its extensive application in target tracking, cooperative control, robotics and image processing. In this work we particularly concentrate on data association in multisensor-multitarget tracking algorithms, in which solving the multidimensional assignment is an essential step. Current algorithms generate good suboptimal solutions (with quantifiable accuracy) to these problems in pseudo polynomial time. However, in dense scenarios these methods can become inefficient because of the resulting dense candidate association tree. Also, in order to generate the top m (or ranked) solutions these algorithms need to solve a number of optimization problems, which increases the computational complexity significantly. In this paper we develop a Randomized Heuristic Approach (RHA), in which, in each step, instead of choosing the best solution indicated by the heuristic, one of the solutions is chosen randomly depending on the "probability" associated with it. the resulting algorithm produces solutions that are as good as or better than those produced by Lagrange relaxation-based algorithms that have much higher computational complexity. this method also produces other ranked best solutions with no further computational requirement.

关键词： multisensor-multitarget tracking data association randomized heuristic approach assignment algorithms

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Fusion of uncertain combat identity declarations and implication rules using the belief function theory

Fusion of uncertain combat identity declarations and implica...

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17th conference on signal and data processing of small targets

作者： Ristic, B Smets, P Def Sci & Technol Org ISRD Labs 200 Edinburgh SA 5111 Australia

ISBN: (纸本)0819459186

Surveillance systems typically perform target identification by fusing target ID declarations supplied by individual sensors with a prior knowledge-base. Target ID declarations are usually uncertain in the sense that: (1) their associated confidence factor is less than unity, (2) they are non-specific (the true hypothesis belongs to a subset A of the universe 9). Prior knowledge is typically represented by a set of possibly uncertain implication rules. An example of such a rule is: if the target is Boeing 737 than it is neutral or friendly with probability 0.8. the uncertainty auain manifests itself here in two ways: the rule holds only with a certain probability (typically less than 1.0) and the rule is non-specific (neutral or friendly). the paper describes how the fusion of ID declarations and the implication rules can be handled elegantly within the framework of the belief function theory as understood by the transferable belief model (TBM). Two illustrative examples are worked out in details in order to clarify the theory.

关键词： data fusion target identification belief function evidential theory transferrable belief model implication rules

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Performance evaluation of time-weighted backvalues least squares vs. NOGA track estimators via sensor data fusion and track fusion for small target detection applications - art. no. 59130Z

Performance evaluation of time-weighted backvalues least squ...

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17th conference on signal and data processing of small targets

作者： Imam, N Barhen, J Glover, CW Oak Ridge Natl Lab Comp Sci & Math Div Ctr Engn Sci Adv Res Oak Ridge TN 37831 USA

ISBN: (纸本)0819459186

the rapid growth and increasing sophistication of airborne surveillance technology have spurred intense research efforts in the development and implementation of tracking algorithms capable of processing a large number of targets using multi-sensor data. In this paper, a, novel tracking algorithm, the NOGA tracker, is presented and compared with the more conventional Time-Weighted Backvalues Least Squares (TWBLS) estimator for accuracy (numerical and phenomenological), ease of implementation, and time performance. the NOGA tracker combines model predictions and sensor measurements to produce best estimates for quantities of interest. the state estimator model used for NOGA is a simple second order auto-regression which is combined with an uncertainty reduction scheme involving nonlinear Lagrange optimization process in which the inverse of a global covariance matrix is used as the natural metric for the Bayesian inference that underlies the combining process. the NOGA tracker explicitly incorporates sensor and model uncertainties in the estimation process, and uses model sensitivities to propagate the associated covariance matrices accurately and in a systematic way.

关键词： Uncertainty reduction track fusion sensor fusion multi-sensor tracking auto-regressive

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