The problem of poor real-time capability, a large number of traversals and the long operation time of the MUSIC algorithm bring great difficulties to the application of DOA estimation. For the above problems, this paper fuses MUSIC algorithm and genetic algorithm and proposes an improved genetic MUSIC algorithm (GA-MUSIC). Also, improvement strategies are proposed for the three operators of selection, crossover, and variation of the genetic algorithm. The GA-MUSIC was simulated for DOA estimation to verify the effectiveness of this algorithm. The research shows that the GA-MUSIC proposed in this paper has the best operational performance at different precisions and SNRs. This algorithm can effectively reduce the iterative computation of the algorithm with better real-time performance, and the search success rate is above 95% while ensuring accuracy. Furthermore, this paper corroborates that the proposed improved genetic MUSIC algorithm can achieve better performance in single signals and incoherent multi-signal sources. It can be seen that the algorithm in this paper can effectively solve the problems of low success rates of DOA estimation searches and poor real-time performance and has a significant reference value for practical engineering applications.

In underwater target tracking, complex environmental interference and sensor anomalies introduce outliers into observations, leading to suboptimal state estimation. To address this challenge, a robust Kalman filter (SGαSSWMRKF) based on sliding window median and Sub-Gaussian α-stable (SGαS) distribution is proposed. The SGαS distribution models measurement noise in underwater tracking, with the Inverse Wishart distribution as the prior for measurement noise covariance. For the lack of a closed-form expression of the SGαS mixture probability density function, an estimator integrating Gamma Series approximation and Gauss-Laguerre Quadrature is developed to adaptively update asymptotic residual variance. Additionally, a median-based adaptive sliding window method with dynamically adjusted size computes the smoothed target state posterior, balancing real-time performance and estimation accuracy. Leveraging the Variational Bayes framework, SGαSSWMRKF suppresses outlier interference by iteratively updating the target state, measurement noise covariance, auxiliary random variables, and degrees of freedom parameters. Simulation results demonstrate that under an unknown non-stationary HMN environment, the SGαSSWMRKF outperforms the SWRKF filter. Specifically, it reduces the Average Root Mean Square Error by 16.74%, 8.14%, and 7.19% in distance, bearing, and velocity, respectively. Consequently, more accurate state estimation is achieved with SGαSSWMRKF. Additionally, experiments on a publicly available real-world dataset demonstrate that SGαSSWMRKF enables more accurate state estimation.