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检索条件"任意字段=16th Conference on Signal and Data Processing of Small Targets"

共 487 条记录，以下是151-160 订阅

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An MDL approach for multiple low observable track initiation

An MDL approach for multiple low observable track initiation

引用

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...

引用

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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On track and tracklet fusion filtering

On track and tracklet fusion filtering

引用

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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On expectation maximization applied to GMTI convoy tracking

On expectation maximization applied to GMTI convoy tracking

引用

14th conference on signal and data processing of small targets

作者： Koch, W FGAN FKIE D 53343 Wachtberg Neuenahrer Strasse 20 Germany

ISBN: (纸本)0819444782

Collectively moving ground targets are typical of a military ground situation and have to be treated as separate aggregated entities. For a long-range ground surveillance application with airborne GMTI radar we in particular address the task of track maintenance for ground moving convoys consisting of a small number of individual vehicles. In the proposed approach the identity of the individual vehicles within the convoy is no longer stressed. their kinematical state vectors are rather treated as internal degrees of freedom characterizing the convoy. which is considered as a collective unit. In this context, the Expectation Maximization technique (EM), originally developed for incomplete data problems in statistical inference and first applied to tracking applications by STREIT et al. [1, 2], seems to be a promising approach. We suggest to embed the EM algorithm into a more traditional Bayesian tracking framework for dealing with false or unwanted sensor returns. the proposed distinction between 'external' and 'internal' data association conflicts (i.e. those among the convoy vehicles) should also enable the application of sequential track extraction techniques introduced by VAN KEUK [3] for aircraft formations, providing estimates of the number of the individual convoy vehicles involved. Even with sophisticated signal processing methods (STAP: Space-Time Adaptive processing). ground moving vehicles can well be masked by the sensor specific clutter notch (Doppler blinding). this physical phenomenon results in interfering fading effects, which can well last over a longer series of sensor updates and therefore will seriously affect the track quality unless properly handled. Moreover, for ground moving convoys the phenomenon of Doppler blindness often superposes the effects induced by the finite resolution capability of the sensor. In many practical cases a separate modeling of resolution phenomena for convoy targets can therefore be omitted, provided the GMTI detectio

关键词： Ground Moving targets (GMT) Expectation Maximization (EM) Bayesian/Probabilistic Multiple Hypothesis Tracking (PMHT, MHT, PDA) Minimum Detectable Velocity (MDV) Doppler blinding sensor resolution phenomena

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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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An algorithm benchmark data suite for chemical and biological (chem/bio) defense applications

An algorithm benchmark data suite for chemical and biologica...

引用

20th conference on signal and data processing of small targets

作者： Slamani, Mohamed-Adel Fisk, Brian Chyba, thomas Emge, Darren Waugh, Steve ITT Corp Adv Engn & Sci Div 2560 Huntington Ave Alexandria VA 22303 USA Edgewood Chem Biolog Ctr Aberdeen Proving Ground MD 21010 USA Def Threat Reduct Agcy Ft Belvoir VA 22060 USA

ISBN: (纸本)9780819471604

A Chem/Bio Defense Algorithm Benchmark is proposed as a way to leverage algorithm expertise and apply it to high fidelity Chem/Bio challenge problems in a high fidelity simulation environment. Initially intended to provide risk mitigation to the DTRA-sponsored US Army CUGR ACTD, its intent is to enable the assessment and transition of algorithms to support P3I of future spiral updates. the key chemical sensor in the CUGR ACTD is the Joint Contaminated Surface Detector (JCSD), a short-range stand-off Raman spectroscopy sensor for tactical in-the-field applications. the significant challenges in discriminating chemical signatures in such a system include, but are not limited to, complex background clutter and low signal to noise ratios (SNR). this paper will present an overview of the Chem-Bio Defense Algorithm Benchmark, and the JCSD Challenge Problem specifically.

关键词： signal to noise ratio

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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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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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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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