Distributed Detection And Estimation Fusion By Maximizing Expected Utility

In this paper, we address the problem of distributed joint detection and estimation, in which numbers of sensor nodes are employed to detect signal-presence or absence, and estimate the unknown parameter associated with the decided hypothesis. Due to the limited bandwidth, each local sensor quantizes its original measurement into one bit of information, and the final global decision is then made based on the quantized data set at the fusion center (FC). Firstly, the multi-sensor joint likelihood function under either hypothesis is evaluated by assuming the data transmission channel between the FC and local sensors are perfect or imperfect, respectively. The expected utility is then introduced to assess the joint performance for distributed detection and estimation tasks. Finally, an optimal estimation receiver operating curve (EROC-opt) decision scheme is employed to accomplish the distributed joint detection and estimation. Performance comparisons with the centralized scheme without quantization and the generalized likelihood ratio test (GLRT) are made to show the superiority of the proposed approach.