| Paper: | SAM-P5.5 | ||
| Session: | Sensor Networks | ||
| Time: | Wednesday, May 19, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Sensor Array and Multichannel Signal Processing: Data fusion from multiple sensor types | ||
| Title: | DATA FUSION IN WIRELESS SENSOR ARRAY NETWORKS WITH SIGNAL AND NOISE CORRELATION MISMATCH | ||
| Authors: | Karim Oweiss; Michigan State University | ||
| Abstract: | Data fusion from closely spaced, non-uniformly 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 signals impinging on a non-uniformly 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. | ||
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