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Analysis and optimization of a class of nonlinear detection filters

Analysis and optimization of a class of nonlinear detection ...

引用

13th conference on signal and data processing of small targets

作者： Singer, PF Raytheon Co El Segundo CA 90245 USA

ISBN: (纸本)0819441872

It is well known that the matched filter is the optimal linear detection filter but, this allows for the possible existence of a nonlinear detection filter with better performance. this paper considers the class of nonlinear detection filters that are composed of a linear filter followed by an arbitrary point process. the result is general enough to include detection paradigm in which the signal model is not additive. the ROC (receiver operating characteristic) curve of a general point process is analyzed. this analysis reveals that nonreversibility and not nonlinearity of the point process is responsible for the improvement of the ROC curve. that is, an reversible point process, either linear or nonlinear, leaves the ROC curve unchanged. However, a nonreversible point process will alter the ROC curve. this result is used to define a canonical ROC curve which is then utilized to derive the optimal point process, Several simple forms of the point process are considered first then the general optimal point process is derived. the technique is illustrated with several examples. Results for the special cases of unimodal signal densities receive particular attention.

关键词： detection filter nonlinear nonreversible receiver operating characteristic signal model

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Passive tracking with sensors of opportunity using passive coherent location - art. no. 69691F

Passive tracking with sensors of opportunity using passive c...

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20th conference on signal and data processing of small targets

作者： Subramaniam, Mahes tharmarasa, R. McDonald, Mike Kirubarajan, T. McMaster Univ ECE Dept ETF Lab Hamilton ON Canada

ISBN: (纸本)9780819471604

Passive coherent location (PCL), which uses the commercial signals as illuminators of opportunity, is an emerging technology in air defense systems. the advantages of PCL are low cost, low vulnerability to electronic counter measures, early detection of stealthy targets and low-altitude detection. However, limitations of PCL include lack of control over illuminators, poor bearing accuracy, time-varying sensor parameters and limited observability. In this paper, multiple target tracking using PCL with high bearing error is considered. In this case, the challenge is to handle high nonlinearity due to high measurement error. In this paper, we implement the converted measurement Kalman filter, unscented Kalman filter and particle filter based PHD filter for PCL radar measurements and compare their performances.

关键词： passive radar system passive coherent location multitarget tracking data association covert tracking sensors of opportunity

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Adaptive sensor management in target tracking

Adaptive sensor management in target tracking

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14th conference on signal and data processing of small targets

作者： Sworder, DD Boyd, JE Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)0819444782

Model-based. path estimation algorithms are commonly derived from a linear kinematic equation and an expansive observation model. the premise that the measurement region is all-inclusive simplifies the performance analysis, but it does not describe the relevant characteristics of some sensors. Most sensors have an active region. If the region contains, the target. a noisy location measurement is made. Alternatively, no location measurement is returned. there is a clear tradeoff between measurement acuity and broad coverage. A sensor that adapts its active region to the most likely target location should achieve higher quality tracking and identification. However. effective utilization of this flexibility requires an accurate determination of the conditional distribution of the target-state. In this paper, the Gaussian Wavelet Estimator is used in an adaptive algorithm for sensor management. It is shown that the adaptive window provides performance that is superior to that achieved using a fixed window of comparable cover probability.

关键词： nonlinear filters target tracking data fusion

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New approach to detect dim moving point targets based on motion analysis

New approach to detect dim moving point targets based on mot...

引用

13th conference on signal and data processing of small targets

作者： Dong, NL Jin, G Qi, B Ma, JG Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China

ISBN: (纸本)0819441872

Dim point targets detection in highly cluttered backgrounds is a challenging problem. In this paper we describe the development and the implementation of a new effective methodology to detect moving point targets in infrared or visual images. the new approach is based on mathematical morphology and motion analysis. First, the image is filtered by means of gray-scale morphology in order to reject background objects. then, with the residual image a motion analysis based on trajectory conjunction is carried out to extract the potential point like moving targets. In the motion analysis stage we define a function to describe the characteristics of moving targets, and by this means a strategy is constructed to judge whether or not a given area contains the potential moving target. through motion analysis, the non-target points are kicked out while the true target points are detected out after merely several frames of image. this approach doesn't depend on threshold techniques and requires no assumptions about the behavior of the target motion. the only limitation is that the target's speed doesn't exceed several pixels per frame. A test study based on available database is presented, and the results so far indicate that this approach to detect moving point target is highly effective.

关键词： low contrast point target detection nonlinear image processing motion analysis mathematical morphology small target

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On track and tracklet fusion filtering

On track and tracklet fusion filtering

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14th conference on signal and data processing of small targets

作者： Drummond, OE CyberRnD Inc Culvar City CA 90230 USA

ISBN: (纸本)0819444782

Track and tracklet fusion filtering is complicated because the estimation errors of tracks from two sources for the same target may be cross-correlated. this cross-correlation of these errors should be taken into account when designing the filter used to combine the track data. this paper addresses the various track and tracklet fusion methods and their impact on communications load and tracking characteristics. In track and tracklet fusion a sequence of measurements is processed at the sensor or platform level to form tracks;then sensor level track data (in the form of tracks or tracklets) for a target is distributed and fused with each other or with a global track. Track Fusion and Tracklet Fusion are also sometimes called Hierarchical Fusion, Federated Fusion, or Distributed Fusion. Track data can include features or other information useful for target classification. One characteristic of track and tracklet fusion that distinguishes one method from another is whether the local track data is combined or if global tracks are maintained and updated using the sensor track data. Another important issue is whether there is filter process noise to accommodate target maneuvers. Some filtering methods are designed for maneuvering targets and others are not. this paper enumerates the various track and tracklet fusion methods for processing data from distributed sensors and their impact on filter performance and communications load.

关键词： target tracking multiple sensor tracking track fusion multiple target tracking sensor data fusion Bayesian methods tracklet distributed tracking estimation and filtering

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An MDL approach for multiple low observable track initiation

An MDL approach for multiple low observable track initiation

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14th conference on signal and data processing of small targets

作者： Chen, H Kirubarajan, T Bar-Shalom, Y Pattipati, KR Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)0819444782

In this paper the track initiation problem is formulated as multiple composite hypothesis testing using maximum likelihood estimation with probabilistic data association (ML-PDA), an algorithm known to work under very low SNR conditions. this algorithm does not have to make a decision as to which measurement is target originated. the hypothesis testing is based on the minimum description length (MDL) criterion. We first review some well-known approaches for statistical model selection and the advantage of the MDL criterion. then we present an approximate penalty in accounting for the model complexity to simplify the calculation of MDL. Finally, we apply the. MDL approach for the detection and initiation of tracks of incoming tactical ballistic missiles in the exo-atmospheric phase using a surface based electronically scanned array (ESA) radar. the targets are characterized by low SNR, which leads to low detection probability and high false alarm rate. the target acquisition problem is formulated using a batch of radar scans to detect the presence of up to two targets. the ML-PDA estimator is used to initiate the tracks assuming the target trajectories follow a deterministic state propagation. the approximate MDL criterion is used to determine the number of valid tracks in a surveillance region. the detector and estimator are shown to be effective even at 4.4 dB average SNR in a resolution cell (at the end of the signal processing chain).

关键词： track initiation minimum description length maximum likelihood estimator probabilistic data association model selection

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Performance evaluation of time-weighted backvalues least squares vs. NOGA track estimators via sensor data fusion and track fusion for small target detection applications - art. no. 59130Z

Performance evaluation of time-weighted backvalues least squ...

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17th conference on signal and data processing of small targets

作者： Imam, N Barhen, J Glover, CW Oak Ridge Natl Lab Comp Sci & Math Div Ctr Engn Sci Adv Res Oak Ridge TN 37831 USA

ISBN: (纸本)0819459186

the rapid growth and increasing sophistication of airborne surveillance technology have spurred intense research efforts in the development and implementation of tracking algorithms capable of processing a large number of targets using multi-sensor data. In this paper, a, novel tracking algorithm, the NOGA tracker, is presented and compared with the more conventional Time-Weighted Backvalues Least Squares (TWBLS) estimator for accuracy (numerical and phenomenological), ease of implementation, and time performance. the NOGA tracker combines model predictions and sensor measurements to produce best estimates for quantities of interest. the state estimator model used for NOGA is a simple second order auto-regression which is combined with an uncertainty reduction scheme involving nonlinear Lagrange optimization process in which the inverse of a global covariance matrix is used as the natural metric for the Bayesian inference that underlies the combining process. the NOGA tracker explicitly incorporates sensor and model uncertainties in the estimation process, and uses model sensitivities to propagate the associated covariance matrices accurately and in a systematic way.

关键词： Uncertainty reduction track fusion sensor fusion multi-sensor tracking auto-regressive

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the method of range profile for step frequency MMW radar based on wavelet transform power spectrum estimator

The method of range profile for step frequency MMW radar bas...

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13th conference on signal and data processing of small targets

作者： Li, YH Gao, DT Shen, QH Li, XG Nanjing Univ Dept Elect Sci & Technol Nanjing 210093 Peoples R China

ISBN: (纸本)0819441872

the method of range profile for step frequency MMW radar targets based on wavelet transform power spectrum estimator is studied. We show how the Fourier power spectrum can be detected by using the wavelet function coefficients (WFC) of the DWT. this method can successfully measure the power spectrum in samples for which traditional methods often fail because the sample are finite sized, have a complex geometry, or are varyingly sampled. We demonstrate that the spectrum features, such as the power law index, the magnitude, and the typical scales can be determined by the DWT reconstructed spectrum. We apply this method to the practical step frequency MMW radar target echo signals, and on the condition of the same sampling frequency and sampling data length, it can achieve one dimensional range profile with profile's resolution superior to FFT's, so the one dimensional range profile of targets can be analyzed with high resolution, the detail algorithm of range profiles spectrum estimation based on wavelet transforming multirange cells is proposed. Compare with FFT algorithm, using wavelet spectrum estimator of short data series, we can achieves high resolution, high accuracy, and low SNR threshold. the Experiment results make clear that the DWT estimator is a sensitive tool in range profile of step frequency MMW radar.

关键词： wavelet transform multiresolution analysis spectrum estimator radar signal image processing

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A Bayesian network tracking database

A Bayesian network tracking database

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16th conference on signal and data processing of small targets

作者： Obermeyer, FH Poore, AB Numer Corp Ft Collins CO USA

ISBN: (纸本)081945351X

Most maximum likelihood (ML) trackers based on measurement fusion (measurement-to-measurement or measurement-to-track) or track-to-track fusion produce a single data association hypothesis together with kinematic track state estimates. Uncertainty in the track states due to process and measurement noise is represented by covariance matrices, however uncertainty in the data association is either entirely neglected or representative of only limited types of association uncertainty. this paper presents a Bayesian-Network uncertainty management system for use in conjunction with maximum-likelihood trackers. the system, termed the Bayesian Network Tracking database (BNTD) comprises algorithms for interactive access, whereby expectation values of arbitrary track properties can be calculated over all association hypotheses, and algorithms and data-structures for long-term storage, whereby the complete set of association hypotheses can be efficiently approximated, even over long time intervals. A conjoined MLE/BNTD system is thus capable of supporting target identification (ID), feature-aided tracking, and long-term track maintenance (LTTM).

关键词： Bayesian network tracking database data association multiple hypothesis tracking uncertainty management

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Joint MAP bias estimation and data association: Simulations

Joint MAP bias estimation and data association: Simulations

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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. Algorithms for this class of problems have been developed in a companion paper of the authors. this paper presents simulations that compare the "all-pairs" heuristic, the k-best heuristic, and a partial A*-based branch and bound algorithm. the combination of the latter two algorithms is an excellent candidate for use in a realtime system. For an optimal algorithm that also computes the k-best solutions of the joint MAP bias estimation problem and data association problem, we investigate a branch and bound framework that employs either a depth-first algorithm or an A*-search procedure. In addition, we demonstrate the improvements due to a new gating procedure.

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

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