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Multitarget-multisensor tracking in an urban environment: A closed-loop approach

Multitarget-multisensor tracking in an urban environment: A ...

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20th conference on signal and data processing of small targets

作者： Barbosa, Patricia R. Chong, Edwin K. P. Suvorova, Sofia Moran, Bill Colorado State Univ 1373 Campus Delivery Ft Collins CO 80523 USA Intermap Technol Inc Englewood CO 80112 USA Univ Melbourne Parkville Vic 3010 Australia

ISBN: (纸本)9780819471604

When compared to tracking airborne targets, tracking ground targets on urban terrains brings a new set of challenges. Target mobility is constrained by road networks, and the quality of measurements is affected by dense clutter, multipath, and limited line-of-sight. We investigate the integration of detection, signal processing, tracking, and scheduling by exploiting distinct levels of diversity: (1) spatial diversity through the use of coordinated multistatic radars;(2) waveform diversity by adaptively scheduling the transmitted radar waveform according to the scene conditions;and (3) motion model diversity by using a bank of parallel filters, each one matched to a different maneuvering model. Specifically, at each scan, the waveform that yields the minimum one-step-ahead error covariance matrix determinant is transmitted;the received signal is then matched-filtered, and quadratic curve fitting is applied to extract range and azimuth measurements that are input to the LMIPDA-VSIMM algorithm for data association and filtering. Monte Carlo simulations are used to demonstrate the effectiveness of the proposed system on a realistic urban scenario. A more traditional open-loop system, in which waveforms are scheduled on a round-robin fashion and with no other modes of diversity available, is used as a baseline for comparison. Simulation results show that our closed-loop system significantly outperforms the baseline system, presenting both a reduction on the number of lost tracks, and a reduction on the volume of the estimation uncertainty ellipse. the interdisciplinary nature of this work highlights the challenges involved in designing a closed-loop active sensing platform for next-generation urban tracking systems.

关键词： target tracking detection filtering data association waveform scheduling

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Determining the optimal time frame for multisensor track correlation

Determining the optimal time frame for multisensor track cor...

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20th conference on signal and data processing of small targets

作者： Ehrman, Lisa M. Burns, Paul D. Blair, W. Dale Georgia Inst Technol Sensors & Electromagnet Applicat Lab Atlanta GA 30332 USA

ISBN: (纸本)9780819471604

Conventional algorithms for track association (termed "correlation" by convention) employ algorithms which are applied to all sensor tracks at a specific time. the overall value of sensor networks for data fusion is closely tied to the reliability of correct association of common objects tracked by the sensors. Multisensor architectures consisting of gaps in target coverage requires that tracks must be propagated substantially forward or backward to a common time for correlation. this naturally gives rise to the question: at which time should track correlation be performed? In the conventional approach, a two-sensor correlation problem would be solved by propagating the first sensor's tracks forward to the update time (current time) of the tracks from the second sensor. We question this approach by showing simulation results that indicate that the current time can be the worst time to correlate. In addition, a methodology for calculating the approximate optimal correlation time for linear-Gaussian tracking problems is provided.

关键词： Sensors Mahalanobis distance 3D acquisition Detection and tracking algorithms Missiles Sensor networks data fusion Defense and security Radio propagation Reliability

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Integrated bias removal in passive radar systems

Integrated bias removal in passive radar systems

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20th conference on signal and data processing of small targets

作者： Subramaniam, M. Punithakumar, K. McDonald, M. Kirubarajan, T. McMaster Univ ECE Dept ETF Lab Hamilton ON Canada Def Res Dev Canada Radar Systs Ottawa ON K1A 0K2 Canada

ISBN: (纸本)9780819471604

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.

关键词： passive radar system bias estimation airborne targets directional of arrival. passive coherent location PHD filter

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An advanced missile warning processing suite

An advanced missile warning processing suite

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20th conference on signal and data processing of small targets

作者： Montgomery, Joel B. Sanderson, Richard B. McCalmont, John F. Johnson, R. S. McDermott, D. J. Taylor, M. J. M&M Aviat 2302 Schnebly Rd Spring Valley OH 45370 USA Air Force Res Lab Wright Patterson AFB OH 45433 USA MacAulay Brown Dayton OH 45430 USA Gen Dynam Corp Dayton OH 45430 USA

ISBN: (纸本)9780819471604

Effective missile warning and countermeasures remain an unfulfilled goal for the Air Force and others in the DOD community. To make the expectations a reality, newer sensors exhibiting the required sensitivity, field of regard, and spatial resolution are being developed and transitioned. the largest concern is in the first stage of a missile warning system: detection, in which all targets need to be detected with a high confidence and with very few false alarms. Typical fielded sensors are limited in their detection capability by either lack of sensitivity or by the presence of heavy background clutter, sun glints, and inherent sensor noise. Many threat environments include false alarm sources like burning fuels, flares, exploding ordinance, arc welders, and industrial emitters. Multicolor discrimination has been shown as one of the effective ways to improve the performance of missile warning sensors, particularly for heavy clutter situations. Its utility has been demonstrated in multiple demonstration and fielded systems. New exploitations of background and clutter spectral contents, coupled with advanced spatial and temporal filtering techniques, have resulted in a need to have a new baseline algorithm on which future processing advances may be judged against. this paper describes the AFRL Suite IIIc algorithm chain and its performance against long-range dim targets in clutter.

关键词： infrared background suppression multicolor missile warning point targets discrimination algorithm clutter rejection backgrounds real-time processing parallel processing

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Distributed multiple-hypothesis correlation and feedback with applications to video data

Distributed multiple-hypothesis correlation and feedback wit...

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20th conference on signal and data processing of small targets

作者： Tarplee, Kyle M. Trawick, David J. Herman, Shawn M. Numerica Corp Loveland CO USA

ISBN: (纸本)9780819471604

A common problem in video-based tracking of urban targets is occlusion due to buildings and vehicles. Fortunately, when multiple video sensors are present with enough geometric diversity, track breaks due to temporary,,,e occlusion can be substantially reduced by correlating and fusing source-level track data into system-level tracks. Furthermore, when operating in a communication-constrained environment, it is preferable to transmit track data rather than either raw video data or detection measurements. To avoid statistical correlation due to common prior information, tracklets can be formed from the source tracks prior to transmission to a central command node, which is then responsible for system track maintenance via correlation and fusion. To maximize the operational benefit of the system-level track picture, it should be distributed in an efficient manner to all platforms, especially the local trackers at the sensors. In this paper, we describe a centralized architecture for multi-sensor video tracking that uses tracklet-based feedback to maintain an accurate and complete track picture at all platforms. We will also use challenging synthetic video data to demonstrate that our architecture improves track completeness, enhances track continuity (in the presence of occlusions), and reduces track initiation time at the local trackers.

关键词： urban surveillance video tracking multi-sensor video data centralized track fusion tracklet feedback single integrated track picture

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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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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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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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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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Assessment of Urban-Scale Wireless Networks with a small Number of Measurements

Assessment of Urban-Scale Wireless Networks with a Small Num...

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14th ACM International conference on Mobile Computing and Networking

作者： Robinson, Joshua Swaminathan, Ram Knightly, Edward W. Rice Univ Houston TX 77251 USA HP Labs Palo Alto CA 94301 USA

ISBN: (纸本)9781605583426

In order to evaluate, improve, or expand a deployed, city-wide wireless mesh network, it is necessary to assess the network's spatial performance. In this paper, we present a general framework to accurately predict a network's well-served area, termed the metric region, via a small number of measurements. Assessment of deployed networks must address two key issues: non-uniform physical-layer propagation and high spatial variance in performance. Addressing non-uniformity, our framework estimates a mesh node's metric region via a data-driven sectorization of the region. We find each sector's boundary (radius) with a two-stage process of estimation and then measurement-driven "push-pull" refinement of the estimated boundary. To address high spatial variation, our coverage estimation couples signal strength measurements with terrain information from publicly available digital maps to estimate propagation characteristics between a wireless node and the client's location. To limit measurements and yield connected metric regions, we consider performance metrics (such as signal strength) to be monotonic with distance from the wireless node within each sector. We show that despite measured violations in coverage monotonicity, we obtain high accuracy with this assumption. We validate our estimation and refinement framework with measurements from 30,000 client locations obtained in each of two currently operational mesh networks, Google-WiFi and TFA. We study three illustrative metrics: coverage, modulation rate, and redundancy, and find that to achieve a given accuracy, our framework requires two to five times fewer measurements than grid sampling strategies. Finally, we use the framework to evaluate the two deployments and study the average size and location of their coverage holes as well as the impact of client association policies on load-balancing.

关键词： Mesh Networks Coverage Measurement Assessment Deployment Wireless Access Network signal Strength

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