Distributed source localization with multiple sensor arrays and frequency-selective spatial coherence

Multiple sensor arrays distributed over a planar region provide the means for highly accurate localization of the (x,y) position of a source. In some applications, such as microphone arrays receiving aeroacoustic signals from ground vehicles, random fluctuations in the air lead to frequency-selective coherence of the signals that arrive at widely separated arrays. We present a performance analysis for localization of a wideband source using multiple sensor arrays. The wavefronts are modeled with perfect spatial coherence over individual arrays and with frequency-selective coherence between distinct arrays. The sensor signals are modeled as wideband Gaussian random processes, and we study the Cramer-Rao bound (CRB) on source localization accuracy for varying levels of signal coherence and for processing schemes with different levels of complexity. We show that significant improvements in source localization accuracy are possible when partial signal coherence from array to array is exploited. Further, we show that a distributed processing scheme involving bearing estimation at the individual arrays and time-delay estimation between pairs of sensors performs nearly as well as the optimum scheme that jointly processes the signals from all sensors. Results based on measured aeroacoustic data are included to illustrate frequency-selective signal coherence at distributed arrays.