A novel robust particle filtering algorithm is proposed for updating both the waveform and noise parameter for tracking accuracy simultaneously and adaptively. The approach is a significant step for cognitive radar towards more robust tracking in random dynamic systems with unknown statistics. Meanwhile, as an intelligent sensor, it would be most desirable for cognitive radar to develop the application of a traditional filter to be adaptive and to expand the adaptation to a wider scope. In this paper, after analysis of the Bayesian bounds and the corresponding cost function design, we propose the cognitive radar tracking method based on a particle filter by completely reconstructing the propagation and the update process with a cognitive structure. Moreover, we develop the cost-reference particle filter based on optimizing the cost function design according to the complicated system or environment with unknown statistics. With this method, the update of the estimation cost and variance arrives at the approximate optimization, and the estimation error can be more adjacent to corresponding low bounds. Simulations about the tracking implementation in unknown noise are utilized to demonstrate the superiority of the proposed algorithm to the existing methods in traditional radar.

中文翻译：

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具有未知统计信息的认知雷达跟踪系统的成本参考粒子滤波器。

提出了一种新颖的鲁棒粒子滤波算法，用于同时自适应地更新波形和噪声参数，以跟踪精度。该方法是认知雷达朝着统计未知的随机动态系统中更强大的跟踪迈出的重要一步。同时，作为一种智能传感器，认知雷达最需要开发一种传统的自适应滤波器，并将其扩展到更大的范围。本文在分析了贝叶斯边界和相应的代价函数设计之后，通过用认知结构完全重建传播和更新过程，提出了一种基于粒子滤波的认知雷达跟踪方法。此外，我们根据复杂的系统或统计未知的环境，通过优化成本函数设计，开发了成本参考粒子滤波器。利用这种方法，估计成本和方差的更新达到了近似优化，并且估计误差可以更靠近相应的下界。通过对未知噪声中跟踪实现的仿真，证明了该算法优于传统雷达中现有方法的优越性。