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An efficient one-scan-back PDAF for target tracking in clutter

An efficient one-scan-back PDAF for target tracking in clutt...

引用

13th conference on signal and data processing of small targets

作者： Zwaga, JH Driessen, H THALES Nederland NL-7554 RR Hengelo Netherlands

ISBN: (纸本)0819441872

In this paper we present an efficient one-scan-back Probabilistic data Association Filter (PDAF). Regarding the general case of an N-scan-back PDAF, it has been noted in the literature that with each additional scan back, there is a considerable increase in computational load while the amount of improvement in tracking performance diminishes [1], [2]. We therefore have designed a filter that aims to benefit at a minimal increase in computational cost from the one-scan-back architecture that effectively rules out unlikely measurement pairings. In this filter, we use the measurements in the previous scan only to produce better weights for the measurements in the present scan. thus, as compared to a 'full' one-scan-back PDAF, we considerably reduce the number of updating and merging steps each scan. For the proposed filter, and the closely related 'standard' (zero-scan-back) PDAF [3] and 'full' one-scan-back PDAF, we provide the theoretical background, numerical implementation, and simulation results.

关键词： target tracking probabilistic data association clutter

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Analysis and optimization of a class of nonlinear detection filters

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

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

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

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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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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...

引用

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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Reducing MHT computational requirements through use of Cheap JPDA methods

Reducing MHT computational requirements through use of Cheap...

引用

13th conference on signal and data processing of small targets

作者： Quevedo, H Blackman, S Nichols, T Dempster, R Wenski, R Raytheon Syst Co El Segundo CA 90245 USA

ISBN: (纸本)0819441872

Hypothesis formation is a major computational burden for any multiple hypotheses tracking (MHT) method. In particular, a track-oriented MHT method defines compatible tracks to be tracks not sharing common observations and then re-forms hypotheses from compatible tracks after each new scan of data is received. the Cheap Joint Probabilistic data Association (CJPDA) method provides an efficient means for computing approximate hypothesis probabilities. this paper presents a method for extending CJPDA calculations in order to eliminate low probability track branches in a track-oriented MHT method. the method is tested using IRST data. this approach reduces the number of tracks in a cluster and the resultant computations required for hypothesis formation. It is also suggested that the use of CJPDA methods can reduce assignment matrix sizes and resultant computations for the hypothesis-oriented (Reid's algorithm) MHT implementation.

关键词： multi target tracking (MTT) multiple hypothesis tracker (MHT) Cheap Joint Probabilistic data Association (CJPDA) infrared search and track (IRST)

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the target profile identification of step frequency MMW radar based on wavelet neural network

The target profile identification of step frequency MMW rada...

引用

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

With the increased availability of coherent wide band radar, there has been a renewed interest in the target recognition of MMW frequency step radar. A large bandwidth gives high resolution in range which means target recognition may be possible. In this paper, by integrating wavelet with neural network, a new adaptive wavelet function neural network is proposed. An artificial neural network with wavelet as weight coefficients is developed for pattern recognition. It is inspired by wavelet transform theory and feed forward neural network. the good localization characteristics of wavelet functions in both time and frequency space allow hierarchical multi-resolution learning of input-output data mapping. the wavelet shapes are adaptively computed to minimize an energy function for a specific application of radar targets. the mathematical frame of the neural network is introduced and error back propagation algorithm is used. the procedure of using wavelet neural network for identification is described in detail. Based on the target specific information offered by the range profiles of step frequency MMW radar targets, the wavelet neural network is applied to recognition of three kinds of practical radar targets. We find that we can reliably distinguish for three targets over a range of aspect angle. Experiment results indicate that the new feature vector in low dimension is valuable for target recognition, the wavelet neural network has faster convergence speed and higher correct recognition rate and the noise resistance character is good.

关键词： wavelet neural network multi-resolution analysis target identification radar signal image processing

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Network MFA tracking architectures

Network MFA tracking architectures

引用

13th conference on signal and data processing of small targets

作者： Lu, SH Poore, AB Suchomel, BJ Colorado State Univ Dept Math Ft Collins CO 80523 USA

ISBN: (纸本)0819441872

this work presents some initial steps in the development of a decentralized network centric multiple. frame assignment (MFA) class of architecture algorithms that affordably preserves the quality of a centralized architecture across a network of platforms while managing communication loading and achieving a consistent air picture on entities of interest for each platform. In particular, this work discusses four architectures, namely, Centralized, Network MFA Centralized, Network MEA, on Local data and Network Tracks, and Network MFA on All data and Network Tracks and the results of extensive computations with the architectures.

关键词： centralized architecture network MFA centralized network MFA on local data and network tracks network MFA on all data and network tracks

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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...

引用

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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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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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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