针对重拖尾海杂波下的对海探测问题,提出了一种基于多帧杂波稀疏度估计和幅值辅助概率假设密度的海面目标跟踪算法。首先,提出重拖尾海杂波下的多帧杂波稀疏度估计技术,利用高斯混合后验强度和多帧杂波累积剔除源自幸存目标和新生目标的测量,实时估计空间分布未知且时变的重拖尾海杂波密度;然后,将杂波密度估计参数传递到跟踪器中,并建立带幅值的量测似然函数,在概率假设密度算法框架下进行状态更新,并将更新后的高斯混合后验强度用于下一帧的杂波密度与目标状态估计;最后,进行了仿真试验,仿真结果验证了所提算法的有效性。

Aiming at the maritime detection problem under heavy-tailed sea clutter, a maritime multiple-targets tracking algorithm based on multi-frame clutter sparsity estimation and amplitude-aided probability hypothesis density is proposed. Firstly, a multiframe clutter sparsity estimation technique under heavy-tailed sea clutter is proposed, which uses Gaussian mixture posterior intensity and multi-frame clutter accumulation to eliminate the measurements from surviving targets and new targets, and then estimates the density of heavy-tailed sea clutter with unknown spatial distribution and time-varying in real time. Secondly, the clutter density estimation parameters are fed to the tracker, and a measurement likelihood function with amplitude is established. The state is updated under the framework of the probability hypothesis density algorithm, and the updated Gaussian mixture posterior intensity is used to estimate the sea clutter density and the target state in next scan. Finally, a simulation is carried out, and the simulation results verify the effectiveness of the proposed algorithm.

TN953

国家自然科学基金青年资助项目(61901151);浙江省自然科学基金重点资助项目(LZ20F010002)