This paper addresses the detection and tracking of multiple fluctuating targets for a track-before-detect algorithm based on the Multi-Bernoulli (MB-TBD) filter in surveillance radar systems. MB-TBD usually considers target amplitude information and ignores the fact that radar measurements are complex-valued. In this paper, we first propose to utilize phase information to improve the discrimination of targets from noise. More precisely, complex likelihood ratios are used instead of squared modulus measurements likelihood ratios for fluctuations of types Swerling 0, 1, 3. Secondly, the traditional MB-TBD filter cannot solve the problem of coexistence between targets with stronger amplitude and weaker amplitude when multiple fluctuating targets are moving. To address this limitation, an adaptive birth distribution based on joint successive target cancellation and measurement likelihood ratio driven is proposed. Moreover, in order to reduce computational complexity, the Bernoulli components of the same targets are merged after the MB-TBD updating. Finally, the proposed algorithm is implemented using Sequential Monte Carlo technology. The simulation results show that in challenging scenarios, the performance of the improved algorithm is better than the traditional algorithm, and it has a good application prospect.

本文针对监视雷达系统中基于多伯努利 (MB-TBD) 滤波器的检测前跟踪算法对多个波动目标的检测和跟踪进行了研究。MB-TBD 通常考虑目标幅度信息而忽略雷达测量是复值的事实。在本文中，我们首先提出利用相位信息来提高目标与噪声的区分度。更准确地说，对于 Swerling 0、1、3 类型的波动，使用复似然比而不是平方模测量似然比。其次，传统的 MB-TBD 滤波器无法解决多个目标在多个时幅值较大和幅值较弱的共存问题。波动的目标正在移动。为了解决这个限制，提出了一种基于联合连续目标消除和测量似然比驱动的自适应出生分布。此外，为了降低计算复杂度，相同目标的伯努利分量在MB-TBD更新后进行合并。最后，使用顺序蒙特卡罗技术实现了所提出的算法。仿真结果表明，在具有挑战性的场景下，改进算法的性能优于传统算法，具有良好的应用前景。