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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 1999"

共 764 条记录，以下是361-370 订阅

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Comparison of bias removal algorithms in track-to-track association

Comparison of bias removal algorithms in track-to-track asso...

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19th conference on signal and data processing of small targets

作者： Mori, Shozo Chong, Chee BAE Syst Adv Informat Technol 5050 El Camino RealSuite 200 Los Altos CA 94022 USA

ISBN: (纸本)9780819468475

This paper compares the performance of several algorithms for estimating relative sensor biases when two sets of sensor tracks from two sensor systems are to be fused to form system tracks. The primary focus of this paper is the algorithms' performance, particularly in terms of the mean-square estimation error criterion. The efficiency of the algorithms is not our focus for this study. We are especially interested in three estimation algorithms: (1) the joint track-association/relative-bias-estimation maximum a posteriori (MAP) probability-density/probability-mass function algorithm;(2) the marginal MAP probability density estimation algorithm;and (3) the minimum-variance (MV) estimation algorithm. Those algorithms rely on the capability of generating and evaluating multiple significant track-to-track association hypotheses, which may be obtained by any of the recently developed k-best bipartite data assignment algorithms. Several other algorithms that have been considered in the past will also be discussed.

关键词： multiple target tracking hierarchical data fusion track-to-track association relative bias estimation and removal

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Future prospects for algorithm development of tracking related processing - art. no. 66990E

Future prospects for algorithm development of tracking relat...

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19th conference on signal and data processing of small targets

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

ISBN: (纸本)9780819468475

The state of the art of tracking has matured and consequently, the priorities for improved performance and expanded or new processing capabilities have changed. Future directions in algorithm development in tracking and related data processing are not easy to predict with accuracy. The future priorities of development tasks predicted in this presentation are subjective;that is, simply the author's view. While there will continue to be algorithm development to improve many aspects of tracking, the emphasis is expected to change in favor of expanded and new capabilities. This presentation will address not only those tracking algorithms that will have higher priority for continued algorithm development but also those aspects of tracking related processing that are expected to be of special interest. In addition, to facilitate this discussion, the categories of the state of the art of tracking are more finely decomposed. Many aspects of single sensor, multiple target tracking have matured during the last 20 years but room for improvement remains. In contrast, fusion of data from multiple distributed sensors is not as mature and interest is expected to continue to increase. Many fusion systems pose challenges that are not of much concern in tracking with data from a single sensor and algorithm development of those aspects of fusion will continue to be needed. The capability of the many functions and users of the output of trackers needs to be improved and expanded. Consequently, an increase is expected in the need to improve the content and quality of the output of both the single sensor and fusing trackers. The identified aspects of the new needs in tracker output are discussed and a dialog on this topic is encouraged.

关键词： multiple target tracking tracking with data from multiple sensors sensor data fusion network centric processing tracking open issues Bayesian methods Kalman filter

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Tracking dim targets using integrated clutter estimation

Tracking dim targets using integrated clutter estimation

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19th conference on signal and data processing of small targets

作者： Brekke, Edmund Kirubarajan, Thiagalingam Tharmarasa, Ratnasingharn Norwegian Univ Sci & Technol Dept Engn Cybernet NO-7491 Trondheim Norway Univ Grad Ctr NO-2027 Kjeller Norway McMaster Univ Elect & Comp Engn Hamilton ON L8S 4K1 Canada

ISBN: (纸本)9780819468475

In this paper we address the problem of detecting and tracking a single dim target in unknown background noise. Several methodologies have been developed for this problem, including track-before-detect (TBD) methods which work directly on unthresholded sensor data. The utilization of unthresholded data is essential when signal-to-noise ratio (SNR) is low, since the target amplitude may never be strong enough to exceed any reasonable threshold. Several problems arise when working with unthresholded data. Blurring and non-Gaussian noise can easily lead to very complicated likelihood expressions. The back-round noise also needs to be estimated. This estimate is a random variable due to the random nature of the background noise. We propose a recursive TBD method which estimates the background noise as part of its likelihood evaluation. The background noise is estimated by averaging over nearby sensor cells not affected by the target. The uncertainty of this estimate is taken into account by the likelihood evaluation, thereby yielding a more robust TBD method. The method is implemented using sequential Monte Carlo evaluation of the optimal Bayes equations, also known as particle filtering. Simulation results show how our method allows detection and tracking to be carried out in an uncertain environment where current recursive TBD methods fail.

关键词： track-before-detect dim target clutter estimation particle filter

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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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Electromagnetic target recognition with the fused MUSIC spectrum matrix method:: Applications and performance analysis for incomplete frequency data

Electromagnetic target recognition with the fused MUSIC spec...

引用

IEEE 15th signal processing and Communications Applications conference

作者： Secmen, Mustafa Ekmekci, Evren Turhan-Sayan, Goenuel Orta Dogu Tek Univ Elekt & Elekt Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781424407194

The aim of this paper is to apply an electromagnetic target recognition method, which is based on the use of fused MUSIC spectrum matrices, to the case of incomplete frequency domain data. The aforementioned method was suggested recently and succesfully applied to both canonical and complicated targets in the presence of complete frequency domain data [1]. However, most of the real world applications involve the use of severely incomplete frequency data, especially missing low frequency information. In this paper, the performance of mentioned target recognition method is investigated for various incomplete frequency bands and for a group of five small-scale aircraft targets. As a result, the mentioned target recognition method is demonstrated to give still high correct decision rates even when the frequency band is reduced by 300 MHz at low frequency end and at the signal-to-noise ratio is decreased down to -5 dB level.

关键词： Frequency domain analysis

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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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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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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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Robust radar detection of moving ground targets with STAP

Robust radar detection of moving ground targets with STAP

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RADAR 2007 - The Institution of Engineering and Technology International conference on Radar Systems

作者： Kealey, Paul G. Carrington, David M. QinetiQ Ltd. United Kingdom

ISBN: (纸本)9780863418488

Airborne radar sensors are able to detect small and slow moving ground targets using short dwell times by distinguishing the weak target signature from the dominant ground clutter signal. The capability of a Ground Moving Target Indication (GMTI) sensor is principally governed by the Probability of Detection (PD). Other capabilities such as geolocation, tracking and recognition are derived from these detection data. One method used to enhance the PD for platforms with multiple phase centres is Space Time Adaptive processing (STAP). In this paper we report an experimental assessment of a robust STAP algorithm [1, 5, 6] using the QinetiQ Enhanced Surveillance Radar (ESR) data. From our results it is apparent that robustness may be introduced if a selective training methodology is employed to estimate the data covariance. Results are given for both Pre- and Post-Doppler STAP.

关键词： Space time adaptive processing

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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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