Ballistic Target Recognition Based on 4-D Point Cloud Using Randomized Stepped Frequency Radar

TLDR: In this paper , the authors proposed a BTR scheme, which characterizes the micromotion features with a higher dimensional representation, i.e., the time-range-velocity-power 4-D point cloud, using the randomized stepped frequency radar.

Abstract: Ballistic target recognition (BTR) is critical to the ballistic missile defense system. The challenge of this task is to distinguish warheads from numerous unknown confusing targets within a short observing time. The micromotion feature is proved to be effective for this task. However, traditional methods need a long observing time to acquire enough information for the recognition because of using low-dimensional features. In addition, these model-driven methods cannot handle irregular ballistic targets, such as debris. In this article, we propose a BTR scheme, which characterizes the micromotion features with a higher dimensional representation, i.e., the time–range–velocity–power 4-D point cloud, using the randomized stepped frequency radar. The higher dimensional information contained in the 4-D point cloud can reduce the required observing time. Besides, this scheme combines the model-driven method with a data-driven deep neural network to meet the challenge of model mismatch caused by irregular targets. As a result, the proposed BTR scheme is time efficient and robust, which has been proved on an electromagnetic simulation dataset.