论文信息 - Joint Detection and Localization in Sensor Networks Based on Local Decisions

Joint Detection and Localization in Sensor Networks Based on Local Decisions

A generalized likelihood ratio test (GLRT) based decision fusion method that uses quantized data from local sensors is proposed to jointly detect and localize a target in a wireless sensor field. The signal intensity is assumed to be inversely proportional to a power of the distance from the target. The GLRT, its corresponding maximum likelihood (ML) estimator, and the Cramer-Rao lower bound (CRLB) are derived. Simulation results show that this fusion rule has a significantly improved detection performance, compared with the counting rule (for hard local decisions) or the intuitive fusion rules based on the average of sensor data (for soft local decisions).

P. Varshney | R. Niu

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