Signal Fusion-based Detection with an Intuitive Weighting Method

In a distributed multiple-input multiple-output (MIMO) radar, signal fusion based detection can achieve the best detection performance but the global test may be a complicated combination of local observations, such that a closed-form expression of the false alarm rate may be hard to develop, especially for heterogeneous local observations. In this paper, we study how to impose better linear weights on local test statistics for a global test resulting in a better detection performance. Since the weights of the generalized likelihood ratio test (GLRT) have been proved to be suboptimal, an intuitive and tractable weighting method is presented that weights the local test statistics according to the estimated channel signal-to-noise ratios (SNRs) and the power of the local test statistics measured by the detection probability curve. With a linear combination of local test statistics, the false alarm rates can be computed conveniently. This method is also applicable to heterogeneous local observations. Numerical results for the performance of the distributed cell-averaging constant false alarm rate (CA-CFAR) algorithm and the distributed GLRT algorithm indicate that the proposed weighting method can outperform conventional algorithms.