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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 1999"

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Detection and acquisition of small targets with low signal-to-clutter ratio

Detection and acquisition of small targets with low signal-t...

引用

conference on signal and data processing of small targets 1999

作者： Zhu, ZF Liang, HC Pan, AJ Song, B Xu, GX Ni, GQ Beijing Inst Environm Features Beijing 100854 Peoples R China

ISBN: (纸本)0819432954

Of particular interest are efficient algorithms of detection and acquisition of small targets with low signal-to-clutter ratio (SCR) for reality applications. Some algorithms like matched filtering (MF), dynamic programming (DPA) and multistage hypothesis test (MSHT) have been proposed. Although some experiments proved that these algorithms have good target detection performance, it is difficult to implement by hardware because of more computational requirement. In this paper one simple and useful algorithm of the detection and acquisition for small targets with low SCR is presented. input image is preprocessed with using background normalization and background removal, and SCR of targets of difference value image outputted is improved. After image segmentation with an adaptive threshold is finished, the target detection is performed by continuous multi-frame detection method using spatial-temporal continuous motion features of targets. The targets are identified with multistage continuous filtering and a main target selected is acquired and tracked with the window. Experiments proved that the algorithm could detect small targets with SCR equal to 3 in cloud background. The method has been used for infrared tracking system in field and good tracking performance is acquired.

关键词： target detection target acquisition small target background removal

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small extending target precision tracking with dual-infrared imaging sensors

Small extending target precision tracking with dual-infrared...

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conference on signal and data processing of small targets 1999

作者： Liang, HC Ni, GQ Zhu, ZF Xu, GX Pan, AJ Song, B Beijing Inst Technol Dept Optoelect Engn Beijing 100081 Peoples R China

ISBN: (纸本)0819432954

It is a challenging problem to track point source targets and small extending ones in clutter. Only using single-image sensor is usually not adequate to meet the requirement of a multi-target tracking (MTT) system. Carrying out the measurement through two infrared waveband image sensors, and fusing efficiently measurement data of both sensors, we can track small extending targets precisely and improve the performance of the MTT system. In this paper, the multi-target tracking method based on 3 similar to 5 mu m and 8 similar to 12 mu m infrared image sensors is introduced. The mission scenario is thought as tracking air target. The algorithm of the target image centroid estimator is discussed. In order to improve the precision of target tracking, the data fusion algorithm with Kalman filter is described for the target vector feature. The simulation results indicate that the target tracking precision can be improved by tracking small extending targets with dual-infrared image sensors.

关键词： data fusion target tracking Kalman filter small extending target infrared imaging

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Ground-target tracking with dual-image sensor airborne system

Ground-target tracking with dual-image sensor airborne syste...

引用

conference on signal and data processing of small targets 1999

作者： Liang, HC Ni, GQ Zhu, ZF Xu, GX Yu, YL Song, B Pan, AJ Beijing Inst Technol Dept Optoelect Engn Beijing 1000081 Peoples R China

ISBN: (纸本)0819432954

The ground-target tracking with dual-image sensor is discussed in this paper. The mission scenario is that fighter and helicopter track tactical ground targets such as tank, armored car and articulated vehicle. The airborne system which using a video camera and an 8 similar to 12 mu m infrared imager and processing with parallel DSP can track and identify ground-target in real time. In this paper the sensor platform (aircraft and gimbal) motion parameters are defined, the transformation matrix between the coordinate frames and sensor platform motion compensation algorithm are given. The algorithm of extraction image feature is introduced. According to the SNR of the target measured, two belief regions of target measurement are established I the data fusion algorithm of dual-imaging sensor measurements is given with the voting regulation.

关键词： data fusion target tracking infrared imaging motion compensation

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Detection of small objects using adaptive wavelet-based template matching

Detection of small objects using adaptive wavelet-based temp...

引用

conference on signal and data processing of small targets 1999

作者： Hewer, G Kenney, C Hanson, G Kuo, W Peterson, L USN Air Warfare Ctr China Lake CA 93555 USA

ISBN: (纸本)0819432954

Wavelet-based detection algorithms are developed for detecting small targets in non-Gaussian clutter. A wavelet transform is applied to reduce spatial correlation of the clutter. An adaptive matched filter is applied in the wavelet transform domain which uses estimated covariance matrices derived from the wavelet coefficients. Two problems hinder the use of covariance estimates for background clutter removal: slow computational speed and induced false alarms resulting from nearly singular covariance estimates due to small sample sizes. The issue of speed is dealt with by evaluating the covariance matrices on a sparse grid followed by low order interpolation. To control the problem of bad covariance estimates we filter the grid generated covariance matrices to remove outliers using peer group averaging (PGA). This procedure removes the false alarm problem associated with nearly singular covariance estimates without degrading the overall performance of the clutter removal process.

关键词： wavelets matched filtering covariance estimation peer group averaging

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Observability of sensor biases using multiple track reports

Observability of sensor biases using multiple track reports

引用

conference on signal and data processing of small targets 1999

作者： Easthope, PF Adv Syst Architectures Ltd Alton GU34 4PU Hants England

ISBN: (纸本)0819432954

This paper examines the determination of sensor biases by comparing multiple track outputs from spatially disparate platforms. In many cases, this can be achieved using least-squares methods, minimising the summed-squares of track differences. This method, while simple, is shown to provide valuable information concerning the observability of a given type of bias. Observability is here defined as permitting (in principle) the determination of a unique value for that bias, for each sensor in a pair (to take the simplest case), given sufficient measurement pairs. For the purposes of this study, the types of bias are divided into three main classes: measurement, own-position and alignment. In general, only members of the first group are individually observable, while for the other two classes observable combinations of the individual biases can be derived. The least-squares method also provides an effective method for assessing observability in practice (for example, in certain sensor-track configurations), and in determining which estimated biases are most reliable in such circumstances.

关键词： Sensors Filtering (signal processing) Radar Algorithm development Berkelium Error analysis Matrices Ruthenium Time metrology signal processing

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Optimal nonlinear filtering for track-before-detect in IR image sequences

Optimal nonlinear filtering for track-before-detect in IR im...

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conference on signal and data processing of small targets 1999

作者： Rozovskii, B Petrov, A Univ So Calif Ctr Appl Math Sci Los Angeles CA 90089 USA

ISBN: (纸本)0819432954

The 3D matched filter proposed by Reed et al.(18) and its generalizations (e.g. banks of assumed velocity filters (BAVF)) provide a powerful processing technique for detecting moving low observable targets. This technique is a centerpiece of various track-before-detect (TBD) systems. However, the 3D matched filter was designed for constant velocity targets and its applicability to more complicated patterns of target dynamics is not obvious. In this paper the 3D matched filter and BAVF are extended to the case of switching multiple models of target dynamics. We demonstrate that the 3D matched filtering can be cast into a general framework of optimal spatio-temporal nonlinear filtering for hidden Markov models. A robust and computationally efficient Bayesian algorithm for detection and tracking of low observable agile targets in Infrared Search and Track (IRST) systems is presented. The proposed algorithm is fully sequential. It facilitates optimal fusion of sensor measurements and prior information regarding possible threats. The algorithm is implemented as an TBD subsystem for IRST, however the general methodology is equally applicable for other imaging sensors (SAR, HRRR, etc.).

关键词： Detection and tracking algorithms Nonlinear filtering 3D acquisition signal to noise ratio 3D modeling Sensors Switching Target detection Infrared search and track Optimal filtering

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Adaptive sequential algorithms for detecting targets in a heavy IR clutter

Adaptive sequential algorithms for detecting targets in a he...

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conference on signal and data processing of small targets 1999

作者： Tartakovsky, A Kligys, S Petrov, A Univ So Calif Ctr Appl Math Sci Los Angeles CA 90089 USA

ISBN: (纸本)0819432954

Cruise missiles over land and sea cluttered background are serious threats to search and track systems. In general, these threats are stealth in both the infrared and radio frequency bands. That is, their thermal infrared signature and their radar cross section can be quite small. This paper discusses adaptive sequential detection methods which exploit 'track-before-detect' technology for detecting low-SNR targets in Infrared Search and Track (IRST) systems. Despite the fact that we focus on an IRST against cruise missiles over land and sea cluttered backgrounds, the results are applicable to other sensors and other kinds of targets. The developed algorithms allow us to detect a priori unknown number of targets that appear and disappear at unknown points in time. In contrast to the traditional Wald's method the problem of detection of a target with unknown moment of appearance is formulated and solved as a quickest detection problem (change-point detection problem). This problem involves optimization of a trade-off between the detection delay and the false alarm rate. The developed sequential algorithm detects a target with as small average delay as possible (after it appears) under the constraint on the frequency of false alarms. In addition, the algorithm detects target disappearance - also with the smallest delay, which makes it possible to almost immediately interrupt tracking when target disappears. The decision statistics use the results of 'track-before-detect' - the estimates of target's spatial location based on the optimal spatial-temporal nonlinear filtering. Simulation results using real (starring) IRST data are presented. These results show that performance is high even for very low SNR targets.

关键词： IRST sequential detection tracking track-before-detect adaptive detection algorithms

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Simulation-based optimal filter for manoeuvring target tracking

Simulation-based optimal filter for manoeuvring target track...

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conference on signal and data processing of small targets 1999

作者： Doucet, A Gordon, NJ Univ Cambridge Signal Proc Grp Cambridge CB2 1PZ England

ISBN: (纸本)0819432954

Whilst single model filters are sufficient for tracking targets having fixed kinematic behaviour, manoeuvring targets require the use of multiple models. Jump Markov linear systems (JMLS), i.e. linear systems whose parameters evolve with time according to a finite state-space Markov chain, have been used in these situations with great success. However, it is well-known that performing optimal estimation for JMLS involves a prohibitive computational cost exponential in the number of observations. Many approximate methods have been proposed in the literature to circumvent this including the well-known GPB and IMM algorithms. These methods are computationally cheap but at the cost of being suboptimal. Efficient off-line methods have recently been proposed based on Markov chain Monte Carlo (MCMC) algorithms that out-perform recent methods based on the Expectation-Maximization algorithms. However, realistic tracking systems need on-line techniques. In this paper, we propose an original on-line Monte Carlo (MC) filtering algorithm to perform optimal state estimation of JMLS. The approach taken is loosely based on the bootstrap filter which, whilst being a powerful general algorithm in its original form, does not make the most of the structure of JMLS. The proposed algorithm exploits this structure and leads to a significant performance improvement.

关键词： particle filter Markov chain Monte Carlo switching state space model Bayesian methods target tracking

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New method for detection of dim point-targets in infrared images

New method for detection of dim point-targets in infrared im...

引用

conference on signal and data processing of small targets 1999

作者： New, WL Tan, MH Er, MH Ronda, V Nanyang Technol Univ Sch EEE Singapore 639798 Singapore

ISBN: (纸本)0819432954

There are several methods reported in the literature for detecting dim targets against slowly moving clutter. However, each method has its own advantages and disadvantages. The challenge lies in reducing the 'false alarm rate' to an acceptable level. 'False alarm rate' defined in case of a significant size of the target in a frame may not be applicable to point-targets. This paper presents a new method for the detection of dim point-targets in the presence of the evolving clouds and heavy background clutter. Choosing a threshold for achieving constant false alarm rate is always a tricky problem. Too less a threshold may ensure detection of target pixels. But this will result in too many false targets, which limit the performance of the post-processor to trace out target paths. Too high a threshold result in fewer false alarms but the targets may also be missing out. Based on off-line studies, it has been found that a 'desirable condition' is required to limit the number of accumulated pixels not to exceed 8% of the total image size for post-processing. This paper presents a method based on random and correlated noises which in turn selects an auto threshold that leads to the 'desirable condition' for the postprocessor. An effort has been made to derive an empirical formula based on random and correlated noises to obtain an auto threshold value that achieves the desirable condition. Then the incoming frames of data are then processed by a constant threshold and accumulated as total number of pixels. At the same time the track record of pixels along with frame numbers are recorded. The post-processor to filter out the false alarms uses this information. One advantage of this method is that there is no need to store all the frames to obtain the desired information. The algorithm has been tested with the available database and the results are very promising. It is assumed that most of the targets occupy a couple of pixels. Head-on moving and manoeuvring targets are not

关键词： point-target random noise correlated noise false alarm rate

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Does extra information always help in data fusion?

Does extra information always help in data fusion?

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conference on signal and data processing of small targets 1999

作者： Perrella, A Glover, C Waugh, S Inst Def Analyses Alexandria VA 22311 USA

ISBN: (纸本)0819432954

Adding new sensor metric information into a data fusion process does not always improve performance and can sometimes produce poorer results.(1.2) References 1 and 2 used examples to shaw that-in some instances and contrary to expectation-adding new information resulted in poorer rather than improved performance, even though the information itself was correct. Correct is being used here to describe data that may be in error because of sensor deficiencies (e.g., large random or bias errors in azimuth, elevation, or range) but whose error characteristics are accurately described and known to the fusion process. In other words, the fusion process is not being lied to by misrepresentation of the data quality. In this sense, an individual data point may be inaccurate, but the fusion process is capable of properly weighting that point in an optimal sense so that its statistical inaccuracy does not damage the final product any more than a data point from a better sensor that has less statistical inaccuracy. In a multiple-sensor fusion process, these kinds of results have been cited as reasons for not using data from poorer quality sensors for fear of diluting the performance of the better quality sensors. This paper explores the counterintuitive findings from these referenced examples and evaluates under what conditions lesser quality sensors or sensors that mistakenly overestimate their own data quality should be allowed to contribute to a sensor fusion process.

关键词： data fusion multiple-sensor fusion skeptical fusion

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