Multi-Sensor Multi-Frame Detection Based on Posterior Probability Density Fusion

Multi-frame detection (MFD) and multi-sensor fusion are two popular methods of target detection and estimation which can improve the performance by increasing the number of measurement samples. In this paper, we combine these two methods together, proposing a novel multi-sensor multi-frame detection (MS-MFD) method. On the one hand, MS-MFD can make use of the target information as much as possible through the multi-frame integration. On the other hand, it can acquire the target space-diversity gain by jointly processing the measurement samples on different observation orientations, providing more accurate estimates. In particular, the proposed method consists of two steps. First, it conducts the MFD processing in each sensor node, computing the local multi-frame jointly posterior probability density. Then, it transmits the local densities to the fusion center for further processing, calculating the global target estimates. Furthermore, in order to improve the implementation efficiency of MS-MFD, a Gaussian Mixture model based method is proposed to approximate the distribution of local posterior probability density, so that the transmission costs of local posterior probability density can be significantly reduced. It is demonstrated by simulations that the proposed methods show superior performance.