This paper focuses on the problem of moving human target detection in foliage environment, which is a challenge in a radar system. As a matter of fact, owing to the rough surfaces of trunks, branches, and leaves, there is always a lot of multipath clutter remaining which will severely influence the detection performance. In the study, a method combined with entropy weighted coherent integration (EWCI) and the Hough transform is put forward. The method can effectively suppress not only the stationary clutter but also the multipath clutter. The nonline-of-sight (NLOS) foliage-penetration measurements are set up and the results prove the superiority of our method.
Inspired by recent advances in multistatic radar systems, the problem of sensor placement is investigated. Our study is motivated by the fact that it is not always clear what the placement of the radars giving the best performance for the targets of interest might be. To account for the issue, we derive the multistatic Cramér-Rao lower bounds (CRLBs) for range and velocity estimation as the fitness function. Then we use genetic algorithms (GAs) to perform the optimized sensor placement as one of global optimization. The simulation example indicates that our proposed approach is a flexible and effective tool and is capable of suggesting optimal sensor placement strategies to meet required radar performance goals.
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