LAUSR

In the domain of radar countermeasures, existing multitarget tracking algorithms often struggle with tracking multiple maneuvering targets in dense scenarios. To address this issue, this article proposes a hybrid-driven algorithm that integrates both data and models. Initially, an enhanced joint probabilistic data association (JPDA) framework is proposed by incorporating target position and attribute measurements. In this algorithm, the position and attribute measurements associated with the targets are modeled as Gaussian and multi-Bernoulli distributions, respectively. The probability of each association event is then derived using the probability density function of the joint distribution. Subsequently, a deep learning feature extraction network [convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM)] is designed to extract target attribute measurement features from multiple dimensions. This network generates the probability density function of the multi-Bernoulli distribution, thereby facilitating a data-driven association task. Finally, the associated target measurements are input into an interacting multiple model (IMM) unbiased converted Kalman filter to estimate the states of maneuvering targets. The proposed algorithm’s effectiveness, convergence, robustness, and practical applicability are validated through simulation scenarios involving multiple maneuvering target tracking (MMTT), utilizing both the F16 six-degree-of-freedom dynamic model and a kinematic model.