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International Conference on Radar Systems

作者： Zhiqiang Shao Ding Cao Shenghua Zhou Hongwei Liu Xiaoyang Xie Qiang Li Meiqi Liu Huikai Zang Xidian University Xian Shaanxi CN China Academy of Launch Vehicle Technology Beijing CN

ISBN: (纸本)9781785616723

Distributed radar has multiple widely separated radar sites connected to a fusion centre with communication links. Signal fusion based detection may achieve the optimal detection performance but needs a huge bandwidth. In this paper, we study a quantization based detection strategy that would first quantize local observations before transmitted to a fusion centre for detection. The quantization rule are designed according to a prescribed fusion rule, under constrain that all events assigned H_1 after quantization should have the same likelihood ratio. The method has a low computation cost and low performance loss, as verified by numerical results.

关键词： Distributed radar Distributed detection quantization for detection CFAR Data fusion

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Distributed structures, sequential optimization, and quantization for detection

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2008年第4期56卷 1740-1745页

作者： Lexa, Michael A. Johnson, Don H. Rice Univ Dept Elect & Comp Engn Houston TX 77251 USA

In the design of distributed quantization systems, one inevitably confronts two types of constraints-those imposed by a distributed system's structure and those imposed by how the distributed system is optimized. Structural constraints are inherent properties of any distributed quantization system and are normally summarized by functional relationships defining the inputs and outputs of their component quantizers. The use of suboptimal optimization methods are often necessitated by the computational complexity encountered in distributed problems. This correspondence briefly explores the impact and interplay of these two types of constraints in the context of distributed quantization for detection. We introduce two structures that exploit interquantizer communications and that represent extremes in terms of their structural constraints. We then develop a sequential optimization scheme that maximizes the Kullback-Leibler divergence, takes advantage of statistical dependencies in the distributed system's output variables, and leads to simple parameterizations of the component quantization rules. We present an illustrative example from which we draw insights into how these constraints influence the quantization boundaries and affect performance relative to lower and upper bounds.

关键词： distributed detection Kullback-Leibler divergence quantization for detection sequential optimization

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Distributed random signal detection with multibit sensor decisions

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IEEE TRANSACTIONS ON INFORMATION THEORY 1998年第2期44卷 516-524页

作者： Blum, RS Deans, MC Lehigh Univ Dept Elect Engn & Comp Sci Bethlehem PA 18015 USA

Distributed detection of weak random signals in additive, possibly non-Gaussian, noise is considered for cases with multibit sensor decisions. Signal-to-noise ratios are assumed unknown and the signals at the different sensors may be statistically dependent. Analytical expressions are provided that describe the best way to fuse the quantized observations for cases with any given number of sensors. The best schemes for originally quantizing the observations at each sensor are also studied for the case of an asymptotically large number of sensors. These schemes are shown to minimize the mean-squared error between the best weak-signal test statistic based on unquantized observations and the best weak-signal test statistic based on quantized observations. Analytical expressions describing optimum sensor quantizers are provided. The approach used to obtain these expressions insures these sensor quantizers give good performance for cases with a finite number of sensors. A novel iterative technique to search for optimum sensor quantizers efficiently is described. Numerical solutions are presented, some of which involve cases where the best schemes for independent signal observations are shown to be suboptimum.

关键词： asymptotically optimum quantization decentralized detection distributed detection distributed quantization quantization for detection

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quantization IN MULTISENSOR RANDOM SIGNAL-detection

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IEEE TRANSACTIONS ON INFORMATION THEORY 1995年第1期41卷 204-215页

作者： BLUM, RS Department of Electrical Engineering and Computer Science Lehigh University Bethlehem PA USA

Optimum detection schemes based on quantized data are of great interest in radar and sonar applications. The design and properties of multisensor schemes are considered here for detection of weak random signals in additive, possibly non-Gaussian, noise. Signal-to-noise ratios are assumed unknown and the signals at the different sensors may be statistically dependent. Analytical expressions describing the best way to fuse the quantized observations for cases with any given observation sample size are provided. The best schemes for originally quantizing the observations are also studied for the case of asymptotically large observation sample sizes. These schemes are shown to minimize the mean-squared error between the best weak-signal test statistic based on unquantized observations and the best weak-signal test statistic based on quantized observations (under signal absent). Numerical results indicate it is sometimes best for each quantizer to use different size alphabets when a quantizer is located at each sensor.

关键词： quantization for detection ASYMPTOTICALLY OPTIMUM quantization DISTRIBUTED quantization DISTRIBUTED detection DECENTRALIZED detection

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ASYMPTOTICALLY OPTIMUM quantization WITH TIME-INVARIANT BREAKPOINTS FOR SIGNAL-detection

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IEEE TRANSACTIONS ON INFORMATION THEORY 1991年第2期37卷 402-407页

作者： BLUM, RS KASSAM, SA UNIV PENN DEPT ELECT ENGNPHILADELPHIAPA 19104

The nonlinear equations whose solution determines the locally optimum detection quantizer design are derived for a general parametric detection problem where the breakpoints are constrained to be time invariant. These quantizers maximize the efficacy of a test based on quantized data. Some specific optimum detection quantizer problems for the case of time invariant breakpoints have been solved in the past, but only for the special case when the locally optimum nonlinearity factors in a certain way. Examples are given of observation models that do not satisfy these conditions. It is also demonstrated that the locally optimum quantizer design for the time invariant breakpoint constraint is the same as that quantizer design that minimizes the time average mean-square difference between the quantizer and the locally optimum time-varying nonlinearity. A specific result shows that the optimum quantizer is not symmetric for the quadratic detector for random signal in Gaussian noise.

关键词： quantization for detection ASYMPTOTICALLY OPTIMUM quantization TIME INVARIANT BREAKPOINTS

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