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Bayesian tracking with Dempster-Shafer measurements

Bayesian tracking with Dempster-Shafer measurements

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17th conference on signal and data processing of small targets

作者： Mahler, R Lockheed Martin MS2 Tact Syst Eagan MN USA

ISBN: (纸本)0819459186

the theory and practice of single-sensor, single-target tracking is well understood when measurement uncertainties are due entirely to randomness. In this case the Bayes filter and its special cases and approximations, such as the Kalman filter, constitute the foundations of tracking. However, the measurement uncertainties in many observation-types (features, natural-language reports, etc.) can axise from ignorance as well as randomness. Approaches such as the Dempster-Shafer (DS) theory claim to address such information but continue to be controversial, especially in tracking. In other papers this year we have shown that this can be attributed to a lack of formal physical modeling techniques. If familiar measurement models axe extended in a natural way, measurement fusion using DS combination can be subsumed within the Bayesian theory. In this paper we apply these results to introduce the "evidential filter," a special case of the Bayes filter applicable whenever measurement uncertainty can be modeled in DS form. We derive closed-form formulas for the evidential filter;and show that data-update using Dempster's combination is a special case of Bayes' rule. We also briefly show how to incorporate the evidential filter into multitarget tracking techniques.

关键词： Filtering (signal processing) Fuzzy logic Electronic filtering Digital filtering Linear filtering Sensors Motion measurement Electroactive polymers Mathematical modeling Motion models

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A novel 2-D DOA estimation algorithm with superior resolution and reduced sidelobes - art. no. 591302

A novel 2-D DOA estimation algorithm with superior resolutio...

引用

17th conference on signal and data processing of small targets

作者： Marino, CS Chaua, PM Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)0819459186

We present a 2-D Direction of arrival algorithm whose resolution is superior to that of the subspace class of algorithms and sidelobes are reduced compared to most algorithms. the algorithm is based on the 2-D AR Power Spectral Density (2-D ARPSD) applied to a uniformly spaced data set (space-time) which transforms the space-time data to spatial frequency (wavenumber, which is a function of the direction of arrival) and temporal frequency in a high resolution context. this is done by modeling the sensor array data with a 2-D AR model. the 2-D AR parameters are then used in a specialized form of a 2-D FFT to create an enhanced wavenumber-frequency image. A wavenumber vector for a specific narrowband temporal frequency is extracted and compared to other high resolution algorithm such as MUSIC. Our results exhibit superior performance in low SNR and short sample sized scenarios and when mismatch occurs in the subspace techniques. Our technique also exhibits reduced sidelobes as compared with traditional methods.

关键词： Autoregressive models data modeling signal to noise ratio Sensors Interference (communication) Image enhancement Temporal resolution Antennas Computer simulations Spectral resolution

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Comparison of LMITS and MHT algorithms

Comparison of LMITS and MHT algorithms

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17th conference on signal and data processing of small targets

作者： Musicki, D Mallick, M La Scala, B Strange, S Evans, R Univ Melbourne Dept Elect & Elect Engn Melbourne Syst Lab Parkville Vic 3010 Australia

ISBN: (纸本)0819459186

the Multiple Hypotheses Tracking (MHT) algorithm has been shown to have the best tracking performance among existing multi-target tracking algorithms using real world sensors with probability of detection less than unity and in the presence of false alarms. the improved performance of the Multiple Hypotheses Tracking comes at the cost of significantly higher computational complexity. Most Multiple Hypotheses Tracking implementations only form the best global hypothesis. this paper compares the Linear Multitarget Integrated Track Splitting (LMITS) tracking algorithm with the Multiple Hypotheses Tracking algorithm. LMITS has a simpler structure than Multiple Hypotheses Tracking as it decouples local hypotheses and avoids the measurement to multi-track allocation entirely. the number of LMITS hypotheses equals the sum of the number of local hypotheses added to the number of initiation hypotheses. thus LMITS can retain a deeper hypotheses subtree which can result in better performance. We compare tracking performances of LMITS and MHT algorithms using simulated data for multiple maneuvering targets in heavy and non-uniform clutter.

关键词： multi-target multi sensor tracking multiple hypotheses tracking (MHT) linear multitarget (LM) integrated track splitting (ITS) data association

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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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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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small moving object detection using adaptive subband decomposition and fractional lower order statistics in video sequences

Small moving object detection using adaptive subband decompo...

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13th conference on signal and data processing of small targets

作者： Bagci, AM Yardimci, Y Çetin, AE Bilkent Univ Dept Elect & Elect Engn Ankara Turkey

ISBN: (纸本)0819441872

In this paper, a small moving object method detection method in video sequences is described. In the first step, the camera motion is eliminated using motion compensation. An adaptive subband decomposition structure is then used to analyze the motion compensated image. In the highband subimages moving objects appear as outliers and they are detected using a statistical detection test based on lower order statistics. It turns out that in general, the distribution of the residual error image pixels is almost Gaussian. On the other hand, the distribution of the pixels in the residual image deviates from Gaussianity in the existence of outliers. By detecting the regions containing outliers the boundaries of the moving objects are estimated. Simulation examples are presented.

关键词： moving object detection adaptive subband decomposition wavelet transform lower order statistics

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Detection of small periodic binary signals in image sequences: Moving signal sources

Detection of small periodic binary signals in image sequence...

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13th conference on signal and data processing of small targets

作者： Feldman, G Bar, D Tugendhaft, I Soreq Nucl Res Ctr IL-81800 Yavne Israel

ISBN: (纸本)0819441872

An algorithm developed for detection, localization and tracking of periodic signals, that appear as few-pixel blobs in image sequences and have a characteristic binary pattern in the temporal domain, is described. It is the further development of our original algorithin (1) that extends its capabilities to the detection of moving targets and also improves its performance in some cases. We sketch those stages of the algorithm that were already described and discussed in (1). then we describe in full detail the new features of our algorithm, namely: 1) A search for signal sources that move with a relatively-slowly-varying velocity. this velocity is adaptively estimated and re-estimated in order to maximize the correlation with the time-domain pattern. While the first estimate is performed at pixel level, the re-estimation ( when tracking detected signals) is performed at the blob level. 2) Re-estimation of the spatial domain background around the already-detected blob, yielding a more precise estimate of the blob. the new algorithm was tested by processing simulated, as well as real, image sequences. the results are discussed. the principal conclusion is that all good features of our original algorithm, namely: efficient detection of visible signals, reasonable detection of invisible signals, insensitivity to local motions in a video, to camera motion, to intensity changes and to any weak flickering of background, remain valid. However, we are required to increase slightly the minimal length of the time-domain correlation (thus, the delay in detection) at the same false alarm probability. Finally, we also consider the problem of the most suitable temporal-domain pattern for signals to be detected.

关键词： image sequence signal detection image registration pattern background subtraction correlation

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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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the evidential reasoning tracker: Further steps towards a unified theory of sensor fusion

The evidential reasoning tracker: Further steps towards a un...

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13th conference on signal and data processing of small targets

作者： Mahalanabis, A Lobbia, RN Nguyen, QB Boeing Co Boeing Phantom Works Div Seattle WA 98124 USA

ISBN: (纸本)0819441872

the discipline of Sensor and data Fusion is increasingly characterized by a set of disparate tools and principles. An initial consideration of these principles belies any theoretical coherence or commonality. Yet secondary inspection may begin to reveal a fundamental basis that ties together these algorithms. this paper represents the second in a multi-part discussion that attempts to explore this common basis. In particular, while our first discussion centered around the use of a traditional state estimation technique (Kalman Filtering) to perform decision-level identity fusion, in the paper we take the opposite approach, using a traditional decision-level ID technique (Dempster-Shafer Calculus) to do state estimation.

关键词： Sensor data fusion

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Nonlinear estimation techniques for impact point prediction of ballistic targets - art. no. 62360C

Nonlinear estimation techniques for impact point prediction ...

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18th conference on signal and data processing of small targets

作者： Hardiman, David F. Kerce, J. Clayton Brown, George C. USA Redstone Arsenal AL 35898 USA

ISBN: (纸本)0819462926

this paper considers three nonlinear estimation algorithms for impact point prediction (IPP) of ballistic targets. the paper assumes measurements are available from a 3D surveillance radar or phased array radar over some portion of the ballistic trajectory. the ballistic target (BT) is tracked using an extended Kalman filter (EKF), an unscented Kalman filter (UKF), and a particle filter (PF). With the track estimate as an initial condition, the equations of motion for the BT are integrated to obtain a prediction of the impact point. this paper compares the performance of the EKF, UKF, and a particular choice of PF for impact point prediction. the traditional Extended Kalman Filter equations are based on a first-order Taylor, series approximation of the nonlinear transformations (expanded about the latest state estimate). Both the Unscented Kalman Filter and the Particle Filter allow nonlinear systems to be modeled without prior linearizion. the primary focus of the analysis presented in this paper is comparing the performance and accuracy of the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), and the chosen Particle Filter implementation for impact point prediction. the three filtering techniques are compared to the theoretical Cramer-Rao lower bounds (CRLB) of estimation error.

关键词： estimation non-linear dynamic system Cramer-Rao lower bound (CRLB) Kalman filter

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