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m-best SD assignment algorithm with application to multitarget tracking

m-best SD assignment algorithm with application to multitarg...

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conference on signal and data processing of small targets

作者： Popp, RL Pattipati, KR Bar-Shalom, Y ALPHATECH Inc Informat Technol Burlington MA 01803 USA

ISBN: (纸本)0819428221

In this paper we describe a novel data association algorithm, termed m-best SD, that determines in O(mSkn(3)) time (m assignments, S lists of size n, k relaxations) the m-best solutions to an SD assignment problem. this algorithm is applied to the following problem. Given line of sight (i.e., incomplete position) measurements from S sensors, sets of complete position measurements are extracted, namely, the 1-st, 2-nd,... m-th best (in terms of likelihood) sets of composite measurements are determined solving a static SD assignment problem. Utilizing the joint likelihood functions used to determine the m-best SD assignment solutions, the composite measurements are then quantified with a probability of being correct using a JPDA-like technique. Lists of composite measurements from successive scans, along with their corresponding probabilities, are then used in turn with a state estimator in a dynamic 2D assignment algorithm to estimate the states of the moving targets over time. the dynamic assignment cost coefficients are based on a likelihood function that incorporates the "true" composite measurement probabilities obtained from the (static) m-best SD assignment solutions. We demonstrate this algorithm on a multitarget passive sensor track formation and maintenance problem, consisting of multiple time samples of line of sight (LOS) measurements originating from multiple (S = 7) synchronized high frequency direction finding sensors. Another significance of this work is that the m-best SD assignment algorithm (in a sliding window mode) provides for an efficient implementation of a Multiple Hypothesis Tracking (MHT) algorithm by obviating the need for a brute force enumeration of an exponential number of joint hypotheses.

关键词： multisensor-multitarget tracking data association m-best assignment SD assignment

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IMM/MHT tracking with an unscented particle filter with application to ground targets

IMM/MHT tracking with an unscented particle filter with appl...

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19th conference on signal and data processing of small targets

作者： Lancaster, J. Blackman, S. Yu, L. Raytheon POB 902 El Segundo CA 90245 USA

ISBN: (纸本)9780819468475

Particle filter tracking, a type of sequential Monte Carlo method, has long been considered to be a very promising, but time-consuming tracking technique. Methods have been developed to include a particle filter as part of a Variable Structure, Interactive Multiple Model (VS-IMM) structure and to integrate it into the Multiple Hypothesis Tracker (MHT) scoring structure. By integrating a particle filter as just one of many filters in Raytheon's MHT, the particle filter is applied sparingly on difficult off-road targets. this dramatically reduces the computation time as well as improves tracking performance in circumstances in which the other filters do not excel. Moreover, terrain information may be taken into account in the particle propagation process. In particular, an Unscented Particle Filter (UPF) was implemented in order to address the potential dominance of a small set of degenerate particles and/or poor prior distribution sampling from hampering the ability of the particle filter to accurately handle a maneuver. the Unscented Particle Filter treats every particle as its own Kalman filter. After the distribution of particles is adjusted in order to take into account the terrain, each particle is divided into sigma point states. these sigma points are propagated forward in time and then recombined to form a new composite particle state and covariance. these reformed particles are used in scoring and can be updated with a new observation. Since the Unscented Particle Filter includes the covariances in these calculations, this particle filter approach is more accurate and potentially requires fewer particles than an ordinary particle filter. By adding an Unscented Particle Filter to the other filters in an MHT tracker, the advantages of the UPF can be utilized in an efficient manner in order to enhance tracking performance.

关键词： Ground Target Tracking Particle Filter Unscented Particle Filter Multiple Hypothesis Tracking UPF tracking applications terrain PF tracking applications

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Advanced detection and correlation based automatic target recognition

Advanced detection and correlation based automatic target re...

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11th conference on Automatic Target Recognition

作者： Mahalanobis, A Muise, R Lockheed Martin Orlando FL 32819 USA

ISBN: (纸本)0819440744

Recognizing targets in infra-red (IR) images has been a long-standing and challenging problem. In this paper we outline a combination of detection methods based on optimized feature discrimination and correlation based classification techniques that demonstrate an improvement ability to separate target from clutter. Preliminary results are shown on a small subset of data obtained from NVESD to illustrate the possible performance gains.

关键词： Target detection Automatic target recognition Sensors Image processing Detection and tracking algorithms Image filtering signal to noise ratio Target recognition Feature extraction Infrared imaging

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Computationally efficient assignment-based algorithms for data association for tracking with angle-only sensors

Computationally efficient assignment-based algorithms for da...

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19th conference on signal and data processing of small targets

作者： Sathyan, T. Sinha, A. Kirubarajan, T. McMaster Univ Dept Elect & Comp Engn 1280 Main St West Hamilton ON L8S 4K1 Canada

ISBN: (纸本)9780819468475

In this paper we describe computationally efficient assignment-based algorithms to solve the data association problem in synchronous passive multisensor tracking systems. A traditional assignment-based solution to this problem is to solve the measurement-to-measurement association using multidimensional (S-dimensional or S-D with S sensors) assignment formulation and the measurement-to-track association using two-dimensional assignment formulation. Even though this solution has been proven to be effective, it is computationally very expensive. One of the reasons is that in calculating the assignment cost of each possible candidate association one requires to find the maximum likelihood (ML) estimate of the unknown target state. the algorithms proposed in this paper use prior information of the targets that are being tracked to reduce the requirement for the costly ML estimation. the first algorithm is similar to the traditional two step technique except that it uses the predicted track information to avoid building the whole assignment tree in the measurement-to-measurement association. In particular, based on the predicted track information first validation gates are constructed for every target. then, when forming the assignment tree, only the branches connecting measurements that satisfy the validation gate requirement are constructed. the second algorithm is a one-step algorithm in that it directly assigns the measurements to the tracks. We pose the data association problem as an (S + 1)-D assignment with the first dimension being the predicted state information of the tracks, and the rest of the S dimensions are the lists of measurements from the sensors. the costs of each possible (S + 1)-tuple are calculated based on the predicted track information, hence, the requirement for an ML estimate is eliminated. Further, we show that when the target maneuvers are not very high, and when the sensor measurements are uncorrelated the (S + 1)-D assignment approximately

关键词： multitarget/multisensor tracking data association assignment technique auction algorithm passive sensor tracking

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Passive relative ranging applied to sensor-to-sensor data association

Passive relative ranging applied to sensor-to-sensor data as...

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14th conference on signal and data processing of small targets

作者： Grantham, JL Brahm, SJ Zounes, RS Whitt, E Comp Sci Corp Huntsville AL 35802 USA

ISBN: (纸本)0819444782

Ballistic Missile Defense (BMD) effectiveness depends on a system's capability to acquire, track, identify, and engage threat missiles. the probability of a successful engagement can be improved by performing multiple-sensor data fusion, especially if the participating sensor systems are based on both radar frequency (RF) and infrared (IR) phenomenology. In this paper, we apply this observation to the "Target Object Map (TOM) correlation problem" for the standard configuration of a kill vehicle (with a single or multicolor IR seeker) receiving uplinks from a ground based radar. Specifically, we examine the application of a relative ranging technique that augments the angles-only track information of a passive IR sensor with non-parametric range-ranking of the threat complex. Since data association performance is significantly better for three-dimensional (3-D) matching than for two-dimensional (2-D) matching, the idea is to take advantage of relative range-ranking information of the threat complex to potentially improve performance. Numerous techniques that attempt to extract absolute range estimates from a passive IR sensor have been investigated by researchers in the BMD community and it is understood that range information allows for improved threat tracking, radiant intensity estimates, and data association performance. However, extracting absolute target range estimates from irrdiance measurements is extremely difficult because of the presence of data uncertainties / ambiguities, environment and sensor noises, and small angular rates of tracked objects. Passive Relative Ranging (PRR) is distinct in that it focuses on the "relative" range-ranking of objects;knowledge that one object is closer than a second object, while not relevant for improving track or intensity estimation performance, can possibly improve the performance of sensor-to-sensor object assignment. the proposed PRR technique is based on the physical range-squared relationship between intensity and

关键词： passive relative ranging data association passive ranging target object map Cramer-Rao bound

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Joint MAP bias estimation and data association: Algorithms

Joint MAP bias estimation and data association: Algorithms

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19th conference on signal and data processing of small targets

作者： Danford, Scott Kragel, Bret Poore, Aubrey Numer Corp POB 271246 Ft Collins CO 80527 USA

ISBN: (纸本)9780819468475

the problem of joint maximum a posteriori (MAP) bias estimation and data association belongs to a class of nonconvex mixed integer nonlinear programming problems. these problems are difficult to solve due to both the combinatorial nature of the problem and the nonconvexity of the objective function or constraints. A specific problem that has received some attention in the tracking literature is that of the target object map problem in which one tries match a set of tracks as observed by two different sensors in the presence of biases, which are modeled here as a translation between the track states. the general framework also applies to problems in which the costs are general nonlinear functions of the biases. the goal of this paper is to present a class of algorithms based on the branch and bound framework and the "all-pairs" and k-best heuristics that provide a good initial upper bound for a branch and bound algorithm. these heuristics can be used as part of a real-time algorithm or as part of an "anytime algorithm" within the branch and bound framework. In addition, we consider both the A*-search and depth-first search procedures as well as several efficiency improvements such as gating. While this paper focuses on the algorithms, a second paper will focus on simulations.

关键词： MAP bias estimation data association heuristics A*-search branch and bound K-best solutions nonconvex MINLP

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3-D track initiation in clutter using 2-D measurements

3-D track initiation in clutter using 2-D measurements

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13th conference on signal and data processing of small targets

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

ISBN: (纸本)0819441872

In this paper we present an algorithm for initiating 3-D tracks using range and azimuth (bearing) measurements from a 2-D radar on a moving platform. the work is motivated by the need to track possibly low-flying targets, e.g., cruise missiles, using reports from an aircraft-based surveillance radar. Previous work on this problem considered simple linear motion in a flat earth coordinate frame [13]. Our research extends this to a more realistic scenario where the earth's curvature is also considered. the target is assumed to be moving along a great circle at a constant altitude. After the necessary coordinate transformations, the measurements are nonlinear functions of the target state and the observability of target altitude is severely limited. the observability, quantified by the Cramer-Rao Lower Bound (CRLB), is very sensitive to the sensor-to-target geometry. the paper presents a Maximum Likelihood (ML) estimator for estimating the target motion parameters in the Earth Centered Earth Fixed coordinate frame from 2-D range and angle measurements. In order to handle the possibility of false measurements and missed detections, which was not considered in [13], we use the Probabilistic data Association (PDA) algorithm to weight the detections in a frame. the PDA-based modified global likelihood is optimized using a numerical search. the accuracies obtained by the resulting ML-PDA estimator are quantified using the CRLB for different scnsor-target configurations. It is shown that the proposed estimator is efficient, that is, it meets the CRLB. Of particular interest is the achievable accuracy for estimating the target altitude, which is not observed directly by the 2-D radar, but can be only inferred from the range and bearing observations.

关键词： target tracking track initiation Cramer-Rao lower bound NIL estimator probabilistic data association

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Long-time Adaptive Coherent Detection of small targets in Sea Clutter using Inversion of Block Tridiagonal Covariance Matrices 15

Long-time Adaptive Coherent Detection of Small Targets in Se...

引用

15th International conference on signal processing Systems, ICSPS 2023

作者： Zhang, Xiaojun Xue, Yufan Shui, Penglang National Key Laboratory of Radar Signal Processing Xidian University China

ISBN: (纸本)9781510675056

Low-velocity small target detection in maritime surveillance radars is always a challenging task. Low signal-to-clutter ratio requires long-time coherent integration to obtain enough gain of target returns. However, long-time coherent integration encounters insufficient secondary data due to the spatial inhomogeneity of sea clutter. In this paper, considering the decorrelation time of speckle component of sea clutter short up to a dozen of milliseconds, the spherical invariant random vector (SIRV) model with block tridiagonal speckle covariance matrix and the inverse Gamma distributed texture is proposed to model sea clutter sequences in several tenths of a second. In this model, a long-time adaptive generalized likelihood ratio test with linear threshold detector (GLRT-LTD) is constructed. Owing to the block tridiagonal structure of speckle covariance matrices, the adaptive detection requires much less reference cells for speckle covariance matrix estimation and much lower computational cost for its inversion. the proposed detector is verified by an X-band CSIR radar database with low-velocity small boats as test targets. the experimental result shows that it obtains competitive detection performance in comparison with the state-of-the-art detectors. © 2024 spie.

关键词： Covariance matrix

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Performance analysis of underwater hydrophone arrays in tracking manoeuvring sea-surface targets - art. no. 591314

Performance analysis of underwater hydrophone arrays in trac...

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17th conference on signal and data processing of small targets

作者： Hernandez, ML Goddard, N QinetiQ Ltd Malvern Technol Ctr CSIP Malvern WR14 3PS Worcs England

ISBN: (纸本)0819459186

In this paper, we assess the capability of underwater hydrophone (UH) arrays to locate and track manoeuvring targets. A UH array is a horizontal line array of omnidirectional pressure sensors that is deployed on the seabed. the measurements at each UH array are then affected by two idiosyncrasies, termed "source direction ambiguity" and "coning error". In this paper, the posterior Cramer-Rao lower bound (PCRLB) is used as the measure of system performance, providing a bound on the optimal achievable accuracy of target state estimation. We demonstrate the impact of the measurement idiosyncrasies on the PCRLB, with the bound shown to be greater (poorer performance) than when using standard bearings-only sensors. We also include clutter (i.e. we allow each measurement to be either target generated or a false positive), as well as both state-dependent measurement errors and a state-dependent probability of detection. Building on previous work, we show that the measurement origin uncertainty can again be expressed as an information reduction factor (IRF), with this IRF now shown to be a function of both the target range and orientation in relation to each UH array. We consider simulated scenarios that contain features characteristic of recent sea trials conducted by QinetiQ Ltd. the two key features of the trial scenarios is that we have very sparse prior knowledge, and each target has the potential to perform a series of manoeuvres. We use a recent PCRLB formulation for tracking manoeuvring targets that approximates the potentially multi-modal target distribution using a best-fitting Gaussian distribution. We present simulation results for multi-sensor scenarios, demonstrating that this is indeed a difficult tracking problem. Tracking is particularly difficult when the target crosses the line of the UH arrays, making triangulation difficult;and when the target is in the "end-fire" of at least one UH array. It is also difficult to detect and triangulate distant targets.

关键词： posterior cramer-rao lower bound underwater hydrophone Arrays manoeuvring target tracking information reduction factor best-fitting Gaussian approximation

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Efficient multi-sensor resource management using Cramer-Rao lower bounds

Efficient multi-sensor resource management using Cramer-Rao ...

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14th conference on signal and data processing of small targets

作者： Hernandez, ML Kirubarajan, T Bar-Shalom, Y QinetiQ Ltd Malvern Technol Ctr Malvern WR14 3PS Worcs England

ISBN: (纸本)0819444782

this paper describes the development of a general framework for the efficient management of multiple sensors in target tracking. the basis of the technique is to quantify, and subsequently control, the accuracy of target state estimation. the Posterior Cramer-Rao lower bound provides the means of achieving this aim by enabling us to determine a bound on the performance of all unbiased estimators of the unknown target state. the general approach is then to use optimization techniques to control the measurement process in order to achieve accurate target state estimation. We are concerned primarily with the deployment and utilization of a limited sensor resource. We also allow for measurement origin uncertainty, with sensor measurements either target generated or false alarms. We exploit previous work to determine a general expression for the Fisher Information Matrix in this case. We show that by making certain assumptions we can express the measurement uncertainty as a constant information reduction factor. this enables the Fisher Information Matrix to be calculated quickly, allowing Cramer-Rao bounds to be utilized for real-time, online sensor management. An example is presented in which the aim is to track a submarine by deploying a series of passive sonobuoys. the target moves according to a constant velocity model, and each sonobuoy provides bearings-only measurements. We show that the sensor management framework determines quick deployment strategies. this enables the target to be accurately localized, and at the same time makes efficient use of the limited sensor resource.

关键词： sensor management data fusion Posterior Cramer-Rao lower bound Fisher Information Matrix target state estimation

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