Institute for Systems Research
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Item Multi-Sensor Correlation and Quantization in Distributed Detection Systems(1991) Chau, Yawgeng A.; Geraniotis, Evaggelos A.; ISRQuantization and fusion schemes are derived for multi-sensor correlation in distributed K- sensor systems that are used for the detection of weak signals or general signal discrimination from dependent observations. The dependence in the observations across time and sensors is modeled via stationary m - dependent, f - mixing, or r - mixing processes. The observation sequences of the various sensors have identical individual statistics and identical pairwise statistics (symmetric conditions). Each sensor observation is passed through a memoryless non-linearity or quantizer (the same one for all sensors) to form the sensor test statistic; the decision statistics of the various sensors are then passed to the fusion center in an unquantized or binary quantized manner to form the final decision statistic of the fusion center. Based on a common large sample size for each sensor that is necessary for achieving high-quality performance, an asymptotic analysis is applied for the error probabilities of the fusion center. This provides design criteria for the optimal memoryless nonlinearity and quantizer. Optimization of these design criteria yields the optimal nonlinearity or quantizer as solutions to linear integral equations involving the first - and second-order pdfs of the sensor observations describing the individual and pairwise dependence. the analytical results obtained are valid for any number of sensors K. Numerical results based on the simulation of the performance of our schemes with different number of sensors are presented. The performance of the optimal nonlinearities and quantizers is shown to outperform that of nonlinearities or quantizers obtained by ignoring the dependence in sensor observations and to improve as the number of sensors increases.Item Quantization and Fusion for Multi-Sensor Discrimination from Dependent Observations(1991) Chau, Yawgeng A.; Geraniotis, Evaggelos A.; ISRSchemes for quantization and fusion in multi-sensor systems used for discriminating between two sequences of dependent observations are introduced and analyzed. The observation sequences of each sensor under the two hypotheses are arbitrary stationary dependent sequences that can not be modeled as signal in additive noise; the objective of the fusion center is to discriminate between the two hypotheses. These observation models are well motivated by practical multi-sensor target discrimination problems. Two cases are considered: in the first, the observation sequences of the sensors are individually dependent but jointly mutually independent; in the second case, the observation sequences are dependent across both time and sensors. The dependence in the observations across time and/or sensors is modeled by m - dependent, j - mixing or r - mixing processes. The following four quantization/fusion schemes are considered: (a) forming test statistics at the sensors by passing the observations through memoryless nonlinearities, summing them up, and fusing these test statistics without previous quantization; (b) quantizing uniformally (with equidistant breakpoints) each sensor observation and then fusing; (c) quantizing optimally each sensor observation and then fusing; and (d) using the sensor test statistic of (a) to make binary decisions and then fusing the binary decisions. To guarantee high-quality performance, a common large sample size is employed by each sensor and an asymptotic analysis is pursued. Design criteria are developed from the bayesian cost of the fusion center for deriving the optimal memoryless nonlinearities of the sensor test statistics and the sensor quantizer parameters (quantization levels and breakpoints). These design criteria are shown to involve an extension of the generalized signal-to-noise ratio used in single-sensor detection and quantization. The optimal nonlinearities and quantizers are obtained as the solutions of linear coupled or uncoupled integral equations involving the univariate and bivariate probability densities of the sensor observations. Numerical results based on simulation are presented for specific cases of practical interest to compare the relative performance of the four quantization/fusion schemes described above and to establish their superiority to schemes that ignore the dependence across time and/or sensors in the observations.