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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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A study of factors affecting multiple target tracking with a pixelized sensor - art. no. 59130L

A study of factors affecting multiple target tracking with a...

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17th conference on signal and data processing of small targets

作者： Morelande, MR Kreucher, CM Kastella, K Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3010 Australia

ISBN: (纸本)0819459186

Factors affecting the performance of an algorithm for tracking multiple targets observed using a pixelized sensor are studied. A pixelized sensor divides the surveillance region into a grid of cells with targets generating returns on the grid according to some known probabilistic model. In previous work an efficient particle filtering algorithm was developed for multiple target tracking using such a sensor. this algorithm is the focus of the study. the performance of the algorithm is affected by several considerations. the pixelized sensor model can be used with either thresholded or non-thresholded measurements. While it is known that information is lost when measurements are thresholded, quantitative results have not been established. the development of a tractable algorithm requires that closely-spaced targets are processed jointly while targets which are far apart are processed separately. Selection of the clustering distance involves a trade-off between performance and computational expense. A final issue concerns the computation of the proposal density used in the particle filter. Variations in a certain parameter enable a trade-off between performance and computational expense. the various issues are studied using a mixture of theoretical results and Monte Carlo simulations.

关键词： Particles Detection and tracking algorithms signal to noise ratio Sensors Algorithm development Monte Carlo methods Surveillance Particle filters Target detection Solids

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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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Multi-targets Miss Distance Measurement using Sequential Range-Doppler Image

Multi-Targets Miss Distance Measurement using Sequential Ran...

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2012 IEEE 11th International conference on signal processing(ICSP 2012)

作者： Dingwei Feng Siliang Wu Guohua Wei School of Information and Electronics Beijing Institute of Technology

ISBN: (纸本)9781467321969

A new approach is described for combining a series of sequential range and Doppler data from single radar platform to perform multi-targets miss distance measurement (MTMDM). In MTMDM, the difference between targets is mainly in range domain, not velocity. this difference leads to resemblance or aliasing in time curves of multi-targets Doppler frequency. Moreover, the miss distance measurement (MDM) using only distance information has some limitations such as larger measurement errors in case of small miss distance. In the new approach, the information of range and Doppler frequency will be first extracted using the centroid estimation method from range-Doppler image. then the extracted information will be jointly used for multi-targets data association, tracking as well as MDM. the data association and fusion of range and Doppler can distinguish multi-targets, and improve the accuracy of the MDM more effectively. the experimental results will prove the efficiency of the proposed method.

关键词： multi-targets miss distance measurement range-Doppler image data fusion

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A training method to improve the effect of small target detection

A training method to improve the effect of small target dete...

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2022 International conference on Image, signal processing, and Pattern Recognition, ISPP 2022

作者： Chen, Boxu Yu, Lixin Meng, Xiao Hua, Yang Beijing Microelectronic Technology Institute Beijing100076 China

ISBN: (纸本)9781510654846

Deep learning target detection has always been a major research direction in the field of artificial intelligence. Its research results are widely used in the fields of automatic driving, security system and medical treatment. this paper proposes a method to improve the detection effect of small targets, which realizes the detection of objects of different scales in the input image, especially to improve the detection effect of small-scale targets. Before the collected data set is sent to the neural network for training, it is first divided into three different scales according to the size of the target to be detected in the image of the data set. then one or several images in the large target data set are stitched, the images in the small target data set are enlarged, and the above two types of images are used to form a new data set. Finally, the new data set and the original data set are sent to the neural network for training. In this paper, the YOLOX target detection network is used for verification. the results show that the detection effect of the network obtained by this method on small targets has been improved, and the missed detection rate has been reduced from 31.2% to 27.5%. At the same time, the detection effect of large and medium-sized targets has not been sacrificed. © 2022 spie.

关键词： Image enhancement

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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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Joint MAP bias estimation and data association: Simulations

Joint MAP bias estimation and data association: Simulations

引用

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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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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Integrated bias removal in passive radar systems

Integrated bias removal in passive radar systems

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20th conference on signal and data processing of small targets

作者： Subramaniam, M. Punithakumar, K. McDonald, M. Kirubarajan, T. McMaster Univ ECE Dept ETF Lab Hamilton ON Canada Def Res Dev Canada Radar Systs Ottawa ON K1A 0K2 Canada

ISBN: (纸本)9780819471604

A passive coherent location (PCL) system exploits the ambient FM radio or television signals from powerful local transmitters, which makes it ideal for covert tracking. In a passive radar system, also known as PCL system, a variety of measurements can be used to estimate target states such as direction of arrival (DOA), time difference of arrival (TDOA) or Doppler shift. Noise and the precision of DOA estimation are main issues in a PCL system and methods such as conventional beam forming (CBF) algorithm, algebraic constant modulus algorithm (ACMA) are widely analyzed in literature to address them. In practical systems, although it is necessary to reduce the directional ambiguities, the placement of receivers closed to each other results in larger bias in the estimation of DOA of signals, especially when the targets move off bore-sight. this phenomenon leads to degradation in the performance of the tracking algorithm. In this paper, we present a method for removing the bias in DOA to alleviate the aforementioned problem. the simulation results are presented to show the effectiveness of the proposed algorithm with an example of tracking airborne targets.

关键词： passive radar system bias estimation airborne targets directional of arrival. passive coherent location PHD filter

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Adaptive mean and variance filter for detection of dim point-like targets

Adaptive mean and variance filter for detection of dim point...

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14th conference on signal and data processing of small targets

作者： Lim, ET Shue, L Venkarteswarlu, R Nanyang Technol Univ Ctr Signal Proc Singapore 639798 Singapore

ISBN: (纸本)0819444782

the problem of detecting small target in IR imagery has attracted much research effort over the past few decades. As opposed to early detection algorithms which detect targets spatially in each image and then apply tracking algorithm, more recent approaches have used multiple frames to incorporate temporal as well as spatial information. they often referred to as "track before detect" algorithms. this approach has shown promising results particularly for detection of dim point-like targets. However, the computationally complexity has prohibited practical usage for such algorithms. this paper presents an adaptive, recursive and computation efficient detection method. this detection algorithm updates parameters and detects occurrence of targets as new frame arrived without storing previous frames, thus achieved recursiveness. Besides, the target temporal intensity change is modeled by two Gaussian distribution with different mean and variance. the derivation of this generalized model has taken account of the wide variation of target speed, therefore detects wider range of targets.

关键词： small target temporal filter triple temporal filter continuous wavelet transform

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