In the localization of a distributed radar cluster under far-field and short-baseline conditions with a configuration that varies with time, traditional translational maneuvering strategies have limited capability to expand the observation angle. This makes it difficult to effectively improve the observation geometry, and can easily lead to the amplification of localization errors. Targeting this problem, this work proposes a localization method with configuration adjustment based on Fisher information matrix (FIM) prediction. Without requiring prior information of the true target position, the proposed method predicts the FIMs of candidate configurations based on the current target position estimate. It evaluates these configurations by minimizing the localization uncertainty along the worst direction, thereby enabling adaptive adjustment of the cluster configuration. Furthermore, an in-place rotational strategy is introduced to enhance angular diversity, and a Gauss–Newton iterative solution is developed by incorporating temporal prior information to improve the stability of nonlinear localization. Simulation results show that the proposed method can effectively improve the observation geometry under far-field and short-baseline conditions, and reduce abnormal jumps caused by noise. Compared with the traditional translational maneuvering strategy, the proposed method reduces the localization error by more than 70%.
Dai et al. (Sat,) studied this question.