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Efficient data association for move-stop-move target tracking

Efficient data association for move-stop-move target trackin...

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20th conference on signal and data processing of small targets

作者： Sathyan, T. McDonald, Mike Kirubarajan, T. McMaster Univ Dept Elect & Comp Engn 1280 Main St West Hamilton ON L8S 4K1 Canada Def Res & Dev Canada Ottawa ON K1A 0K2 Canada

ISBN: (纸本)9780819471604

In this paper, we present an efficient data association algorithm for tracking ground targets that perform move-stop-move maneuvers using round 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-dumm,y 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.

关键词： move-stop-move maneuver ground target tracking GMTI radar data association multiframe assignment

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A novel integrated radar sounder simulation technique for modelling large and small-scale surface scattering phenomena 27

A novel integrated radar sounder simulation technique for mo...

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conference on Image and signal processing for Remote Sensing XXVII

作者： Sbalchiero, Elisa thakur, Sanchari Cortellazzi, Marco Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci Via Sommar 5 I-38123 Trento Italy

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

Radar sounders (RS) play an important role in planetary investigation. However, the complex tasks of predicting performance and interpreting RS data and predicting performance require to perform data simulations. In the literature, there are different methods for RS data simulation, including: i) numerical methods involving the exact solution of Maxwell's equations based on 3D modelling;and ii) methods based on ray-tracing and facet modelling. Among numerical methods, the Finite-Difference Time-Domain (FDTD) technique allows one to precisely model complex nonlinear targets, both geologically and geophysically, at the cost of high computational load. On the contrary, coherent ray-tracing methods reduce the computational cost by triangulating the targets into facets with size comparable to the wavelength. In this work, we combine the advantages of FDTD and ray-tracing methods into a novel integrated simulation technique for modelling scattering phenomena at two scales of wave interaction, i.e. large (facet) scale and small (sub-facet) scale. the coherent facet method is used to simulate facet- scale scattering phenomena, while FDTD is used to evaluate the sub-facet-scale scattering due to roughness on top of the single facets. We investigate the method's effectiveness by generating a one-layer synthetic DEM as a fractional Brownian motion (fBm) process superimposed by different values of RMS slope of the small-scale roughness and by analysing how the received signal changes in terms of signal-to-noise ratio. the results show the effectiveness of the method in representing small-scale roughness realistically.

关键词： Radar sounder simulations FDTD ray-tracing coherent simulator small-scale roughness planetary exploration

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A multiple model probability hypothesis density filter for tracking maneuvering targets

A multiple model probability hypothesis density filter for t...

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16th conference on signal and data processing of small targets

作者： Punithakumar, K Kirubarajan, T Sinha, A McMaster Univ ECE Dept Hamilton ON L8S 4K1 Canada

ISBN: (纸本)081945351X

Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. With multiple targets, representing the full posterior distribution over target states is not practical. the problem becomes even more complicated when the target number varies, in which case the dimensionality of the state space itself becomes a discrete random variable. the Probability Hypothesis Density (PHD) filter, which propagates only the first-order statistical moment (or the PHD) of the full target posterior. has been shown to be a computationally efficient solution to multitarget tracking problems with varying number of targets. the integral of PHD in any region of the state space gives the expected number of targets in that re-ion. With maneuvering targets, detecting and tracking the changes in the target motion model also become important;but current PHD implementations do not provide a mechanism for handling this. the target dynamic model uncertainty can be resolved by assuming multiple models for possible motion modes and then combining the mode-conditioned estimates in a manner similar to the one used in the Interacting Multiple Model (IMM) estimator. In this paper a multiple model implementation of the PHD filter, which approximates the PHD by a set of weighted random samples propagated over time using Sequential Monte Carlo methods, is proposed. the resulting filter can handle nonlinear, non-Gaussian dynamics with uncertain model parameters in multisensor-multitarget tracking scenarios. Simulation results are presented to show the effectiveness of the proposed filter over sin-le-model PHD filters.

关键词： maneuvering target tracking nonlinear filtering probability hypothesis density filter sequential Monte Carlo methods multiple model estimation

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data fusion handoff within a federation of fusion systems

Data fusion handoff within a federation of fusion systems

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19th conference on signal and data processing of small targets

作者： Shea, Peter J. Roskamp, Bryce Black River Syst Co 17685 Juniper PathSuite 300 Lakeville MN 55044 USA

ISBN: (纸本)9780819468475

As the military continues to move forward with an increased number of sensor and fusion systems, it becomes necessary for these systems to be able to communicate efficiently and effectively. In these environments there are multiple sensor and fusion systems that in the past have operated independently of one another. As an increasing number of systems become available, eventually an overlap in the coverage area occurs between these fusion systems. this results in a need for coordination between these semi-autonomous fusion systems. Short of a complete redesign of all the fusion systems, a solution is required to address the handoff of data between these systems. the primary goal of this paper is to describe a data fusion handoff capability that is able to augment these existing systems. this is accomplished by the use of a Handoff Manager that is added to each fusion system. the Handoff Manager is responsible for developing a global representation the track information displayed onboard its own fusion system that is common with the other members of the federation of fusion systems. this is accomplished by using a global track numbering scheme that requires communication and adjudication between the multiple Handoff Manager components that are present on the different fusion systems within the federation. this paper will define the data fusion handoff problem and describe our approach for handling the data fusion handoff problem within the context of overlapping and non-overlapping sensor environments. We will conclude with a discussion of results for a sample problem and of the path forward.

关键词： data fusion tracking distributed fusion track handoff

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Track management in a multisensor MHT for targets with aspect-dependent SNR - art. no. 623613

Track management in a multisensor MHT for targets with aspec...

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18th conference on signal and data processing of small targets

作者： Blanding, Wayne R. Willett, Peter K. Bar-Shalom, Yaakov Coraluppi, Stefano Univ Connecticut Dept ECE Storrs CT 06269 USA

ISBN: (纸本)0819462926

In many active sonar tracking applications, targets frequently undergo fading detection performance in which the target's detection probability can shift suddenly from a high to a low value. this characteristic is a function of the undersea environment. Using a multistatic active sonar problem, we examine the performance of track management (initiation and termination) routines where the target detection probability is based on an underlying Hidden Markov Model (HMM) with high and low detection states. Using a likelihood ratio test, we develop the optimum track initiation performance as measured by a System Operating Characteristic, similar to a Receiver Operating Characteristic, which plots the probability of initiating a true track versus the probability of initiating a false track. We show that near-optimal performance can be attained using track initiation logic that differentiates the measurements as to receiver source in an "M detections of N scans from C sensors" type of rule. Performance can further be improved by using a composite track initiation test that combines two or three such rules in a logical OR operation. We next show that the use of a Shiryaev-Roberts test for track termination yields the quickest detection of false tracks for a given duration of true target tracks when compared to a Page test and rule-based tests of the form "M or fewer detections from K scans".

关键词： target tracking multistatic sonar track initiation

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Tracking and classification using aspect-dependent RCS and kinematic data

Tracking and classification using aspect-dependent RCS and k...

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20th conference on signal and data processing of small targets

作者： Sutharsan, S. tharmarasa, R. Lang, T. Kirubarajan, T. McMaster Univ ECE Dept Estimat Tracking & Fus Lab ETFLab Hamilton ON Canada Gen Dynam Canada Ltd Ottawa ON Canada

ISBN: (纸本)9780819471604

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.

关键词： a spect-dependent RCS joint tracking and classification target-to-sensor orientation kinematic information multistatic sensor network

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A comparison of recursive angle-only target tracking algorithms

A comparison of recursive angle-only target tracking algorit...

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13th conference on signal and data processing of small targets

作者： Elliott, RJ Dufour, F Malcolm, WP Univ Calgary Fac Management Calgary AB T2N 1N4 Canada

ISBN: (纸本)0819441872

In this article we consider estimation for a discrete-time hybrid dynamical system. Hybrid dynamical systems arise quite naturally in many estimation problems, for example target tracking in a maneuvering target scenario. the most commonly used suboptimal estimation algorithm for the problem class just described, is the so called Interacting Multiple Model (IMM) algorithm. In the work presented here, angle-only target tracking problems are considered, in particular we consider scenarios including maneuvering targets. One approach to this class of problem is to apply the IMM scheme. Using the scheme in Elliott and Dufour,(1) we compute estimates of position and velocity for a maneuvering target when only the target bearing is available. these estimates are compared to the IMM scheme applied to the same problem. It is shown that the algorithm proposed in Elliott and Dufour(1) offers significant improvements over the IMM scheme.

关键词： angle-only tracking hybrid dynamics maneuvering targets reference probability

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Track covariance compensation for data misassociations: Simplifications for reduced complexity - art. no. 696916

Track covariance compensation for data misassociations: Simp...

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20th conference on signal and data processing of small targets

作者： Drummond, Oliver E. CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)9780819471604

the primary components of a target track are the estimated state vector and its error variance-covariance matrix (or simply the covariance). the estimated state indicates the location and motion of the target. the track covariance is intended to indicate the uncertainty or inaccuracy of the target state estimate. the covariance is computed by the track processor and may or may not realistically indicate the inaccuracy of the state estimate. Covariance Consistency is the property that a computed variance-covariance matrix realistically represents the covariance of the actual errors of the estimate. the computed covariance of the state estimation error is used in the computations of the data association processing function and the estimation filter;consequently, degraded track consistency might cause misassociations (correlation errors) and degraded filter processing that can degrade track performance. the computed covariance of the state estimation error is also used by downstream functions, such as the network-level resource management functions, to indicate the accuracy of the target state estimate. Hence, degraded track consistency can mislead those functions and the war fighter about accuracy of each target track. In the development of target trackers, far more attention has been given to improving the accuracy of the estimated target state than in improving the track covariance consistency. this paper addresses covariance compensation to reduce the degradation of consistence due to potential misassociations in measurement fusion using single-frame data association. this compensation approach used is also applicable to other fusion approaches and to tracking with data from a single sensor. this paper presents simplifications in some of the processing of the covariance compensation to reduce the processing complexity, i.e. processor load.

关键词： covariance consistency variance-covariance matrix covariance compensation for misassociations target tracking multiple sensor tracking sensor data fusion track error variance-covariance matrix Bayesian methods uncertainty estimation credibility filtering

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Metrics for evaluating track covariance consistency

Metrics for evaluating track covariance consistency

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19th conference on signal and data processing of small targets

作者： Drummond, Oliver E. Ogle, Terry L. Waugh, Steve CyberRnD Inc Culver City CA 90230 USA Missile Def Agcy Washington DC USA

ISBN: (纸本)9780819468475

the primary components of a target track are the estimated state vector and its error variance-covariance matrix (or simply the covariance). the estimated state indicates the location and motion of the target. the track covariance should indicate the uncertainty or inaccuracy of the state estimate. the covariance is computed by the track processor and may or may not realistically indicate the inaccuracy of the state estimate. Covariance Consistency is the property that a computed variance-covariance matrix realistically represents the covariance of the actual errors of the estimate. the computed covariance of the state estimation error is used in the computations of the data association processing function;consequently, degraded track consistency might cause misassociations (correlation errors) that can substantially degrade track performance. the computed covariance of the state estimation error is also used by downstream functions, such as the network-level resource management functions, to indicate the accuracy of the target state estimate. Hence, degraded track consistency can mislead those functions and the war fighter about how accurate each target track is. In the past, far more attention has been given to improving the accuracy of the estimated target state than in improving the track covariance consistency. this paper addresses performance metrics of covariance consistency. Monte Carlo simulation results illustrate the characteristics of the proposed metrics of covariance consistency.

关键词： covariance consistency variance-covariance matrix target tracking multiple sensor tracking multiple target tracking sensor data fusion Bayesian methods uncertainty estimation credibility filtering

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Track covariance compensation for data misassociations: Alternative data association algorithms - art. no. 696911

Track covariance compensation for data misassociations: Alte...

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20th conference on signal and data processing of small targets

作者： Drummond, Oliver E. CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)9780819471604

the primary components of a target track are the estimated state vector and its error variance-covariance matrix (or simply the covariance). the estimated state indicates the location and motion of the target. the track covariance is intended to indicate the uncertainty or inaccuracy of the target state estimate. the covariance is computed by the track processor and may or may not realistically indicate the inaccuracy of the state estimate. Covariance Consistency is the property that a computed variance-covariance matrix realistically represents the covariance of the actual errors of the estimate. the computed covariance of the state estimation error is used in the computations of the data association processing function and the estimation filter;consequently, degraded track consistency might cause misassociations (correlation errors) and degraded filter processing that can degrade track performance. the computed covariance of the state estimation error is also used by downstream functions, such as the network-level resource management functions, to indicate the accuracy of the target state estimate. Hence, degraded track consistency can mislead those functions and the war fighter about accuracy of each target track. In the development of target trackers, far more attention has been given to improving the accuracy of the estimated target state than in improving the track covariance consistency. this paper addresses covariance compensation to reduce the degradation of consistence due to potential misassociations in measurement fusion using single-frame data association. this compensation approach used is also applicable to other fusion approaches and to tracking with data from a single sensor. this paper also shows how this compensation approach can be applied to a variety of data association algorithms.

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