Data fusion in wireless sensor array networks with signal and noise correlation mismatch

Data fusion from closely spaced nonuniformly distributed sensor arrays with large sensor count requires a smart mechanism for minimizing information redundancy while maintaining high signal fidelity. In this work, we investigate the detection of multiple signal sources impinging on a nonuniformly spaced array of sensors when the additive noise has local spatial correlation characteristics. In particular, we show that if the signal spatial correlation length does not match the noise correlation length, significant performance loss occurs because of improper subarray selection. We propose to formulate the log-likelihood function (LLF) in the multiresolution domain by spatially whitening the wavelet-transformed observations followed by local universal thresholding. The LLF obtained is independent of the noise covariance term, thereby yielding significant improvement over classical time domain LLF tests. Performance comparison with data averaging and decision fusion detectors is carried out to illustrate the potential advantages of the proposed method.