Optimal allocation of time resources for phased array radar multi-target tracking based on BP neural network
Bangjun Lei, C. Jiarui, T. Qing, Z. Jindong, T. Tingbao · IET conference proceedings. · 2021
Aiming at the different threat levels under phased array radar multi-target tracking, the Bayesian Cramer-Rao Lower Bound (BCRLB) of the target position estimation is used as the allocation criterion, this paper establishes a multi-target tracking time resource allocation optimization model based on threat degree. The model based on threat degree to track the target can be divided into two categories, different types with different time resource allocation method. Due to the time-consuming operation of this model and optimization algorithm, this paper also proposes a multi-target tracking time resource fitting method based on BP neural network. Computer simulation shows that the model and method can keep the target tracking in the best state, and the BP neural network will reduce the time consumption by thousands of times.