In target tracking applications, the Doppler measurement contains information of the target range rate, which has the potential capability to improve the tracking performance. However, the nonlinear degree between the measurement and the target state increases with the introduction of the Doppler measurement. Therefore, target tracking in the Doppler radar is a nonlinear filtering problem. In order to handle this problem, the Kalman filter form of best linear unbiased estimation (BLUE) with position measurements is proposed, which is combined with the sequential filtering algorithm to handle the Doppler measurement further, where the statistic characteristic of the converted measurement error is calculated based on the predicted information in the sequential filter. Moreover, the algorithm is extended to the maneuvering target tracking case, where the interacting multiple model (IMM) algorithm is used as the basic framework and the model probabilities are updated according to the BLUE position filter and the sequential filter, and the final estimation is a weighted sum of the outputs from the sequential filters and the model probabilities. Simulation results show that compared with existing approaches, the proposed algorithm can realize target tracking with preferable tracking precision and the extended method can achieve effective maneuvering target tracking.

中文翻译：

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多普勒雷达中基于最佳无偏线性估计的解相关无偏顺序滤波

在目标跟踪应用中，多普勒测量包含目标测距率的信息，它具有改善跟踪性能的潜在能力。然而，随着多普勒测量的引入，测量和目标状态之间的非线性程度增加。因此，多普勒雷达中的目标跟踪是一个非线性滤波问题。为了解决这个问题，提出了带有位置测量的最佳线性无偏估计（BLUE）的卡尔曼滤波形式，结合顺序滤波算法进一步处理多普勒测量，其中转换后的测量误差的统计特性为根据顺序滤波器中的预测信息计算。此外，该算法已扩展到机动目标的跟踪情况，其中使用交互多模型（IMM）算法作为基本框架，并根据BLUE位置滤波器和顺序滤波器更新模型概率，最终估计值是顺序滤波器和模型输出的加权和概率。仿真结果表明，与现有方法相比，该算法可以实现较好的目标跟踪精度，扩展后的方法可以实现有效的机动目标跟踪。