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Sensor networks have emerged as a fundamentally new tool for monitoring inaccessible environments. Strict limitations on system bandwidth and sensor energy resources motivate the use of data compression at each sensor. Localization of unknown sources is a key application of sensor networks, requiring as an initial step, estimation of the time delay between signals received at different sensors. In this work, joint designs for quantizer-time delay estimator structures are presented. The goal for these new application-specific encoders/estimators is to achieve the best time delay estimate at a given bandwidth budget or latency bound, or minimize the rate required to reach an estimate with desired accuracy. For white sources, the optimal structure is shown to be a maximum-likelihood detector coupled with a maximum mutual information quantizer. Variations of this system are also considered: sequential detection schemes, empirical methods for unknown signal models, and rate- constrained methods. The proposed designs offer gains over those based on classical compression criteria.

Jointly optimized quantization and time delay estimation for sensor networks