针对复杂动态环境下仿生偏振罗盘的非平稳噪声干扰与方位角边界模糊问题，开展自主航向估计算法研究。提出圆周变分贝叶斯强跟踪容积卡尔曼滤波算法。观测建模中引入冯·米塞斯分布进行方向统计学建模，从理论上消除角度缠绕引发的对跖歧义。滤波框架中深度耦合变分贝叶斯推理与强跟踪理论：利用前者在线自适应估计时变观测噪声协方差，抑制环境干扰；结合后者引入渐消因子保持残差正交，提升系统应对环境突变和载体机动时的瞬态响应能力。实验结果表明，该算法能实现多模态仿生线索的无缝融合，在强干扰动态场景下将航向估计误差稳定在0.6°以内。该架构突破了传统滤波处理周期性边界和非平稳噪声的局限，为高可靠仿生导航提供了鲁棒的基础计算框架。

To address non-stationary noise interference and azimuth boundary ambiguity of bionic polarization compasses in complex dynamic environments, research on an autonomous heading estimation algorithm is conducted. A Circular-Variational Bayesian Strong-Tracking Cubature Kalman Filter algorithm is proposed. In observation modeling, the von Mises distribution is introduced for directional statistical modeling, theoretically eliminating antipodal ambiguity caused by angle wrapping. Within the filtering framework, Variational Bayesian (VB) inference and Strong Tracking (ST) theory are deeply coupled: the former is utilized to adaptively estimate time-varying observation noise covariance online to suppress environmental interference; the latter introduces fading factors to maintain residual orthogonality, enhancing transient responsiveness during sudden environmental changes and carrier maneuvers. Experimental results demonstrate that the algorithm achieves seamless fusion of multi-modal bionic cues, stabilizing the heading estimation error within 0.6° under strong interference scenarios. This architecture overcomes the limitations of traditional filtering in handling periodic boundaries and non-stationary noise, providing a robust computational framework for highly reliable bionic navigation.