针对红外搜索跟踪系统（IRST）双机协同被动探测定位作战使用中，机动目标建模与实际运动失配造成定位误差偏大的问题，研究了一种基于曲线模型的自适应滤波新方法。该方法改进了传统方法根据方向角估计转弯率以及基于帧间插分线加速度估计切向加速度的思路，将转弯率及线加速度联合作为状态变量进行了状态扩维，并推导了扩维后的过程噪声协方差表达式，在缓解传统两层滤波结构带来的计算量大问题外，也提高了切向加速度的估计精度。另外基于反正切函数的值域，结合方向角在四象限间的转移关系，优化了方向角的设计。通过IRST双机协同仿真实例，验证了所提方法对机动目标的适应性更强、目标定位精度更高。
When Infrared Search and Track (IRST) System works in double station cooperative detection and positioning in operational use, it is difficult to match a maneuvering target model with the actual target motion, which worses position estimation error. Therefore, an adaptive filtering algorithm based on curvilinear model is proposed. The method here shifts the thinking that turning angular velocity is estimated by direction angle and tangential acceleration is estimated by inserted acceleration in traditional method. In this paper, turning angular velocity and target acceleration is both treated as augmented state variable and its corresponding process noise covariance expression is derived, which greatly decreases the calculation burden of traditional two-layer filtering structure, as well as improving the estimation accuracy to tangential acceleration. In the meanwhile, the paper optimizes the heading angle calculation method based on library function "atan2()" in embedded software and its transfer relationship between four quadrants. It demonstrates that the proposed method has the better adaptation to maneuvering target and has better positioning accuracy through double IRST cooperative simulation instance.
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