增广误差模型算法在目标跟踪中的应用

针对目标机动运行过程中,滤波模型与机动状态模型失配的问题,提出了一种新的增广状态误差滤波模型。不同于现有增广方案,该模型从模型失配所致状态滤波误差的角度出发,将状态估计误差增广为一状态量,通过滤波估计后用其校正原状态量。算法分析表明,该增广滤波模型具有自适应调节多重渐消因子的等效特性,增强了对目标的跟踪能力。基于该增广状态误差滤波模型,给出了滤波算法设计并进行了仿真实验。实验结果表明,基于该模型的滤波算法在对机动目标进行跟踪时具有更强的鲁棒性。

Augmented Bias Model Algorithm with Application to Target Tracking

A new augmented bias model is put forward for the target tracking regarding the problems of mismatches between filtering model and maneuvering state model during the course of target maneuvering. Unlike the current augmented models, in the proposed model, in the view of the state estimating error caused by model mismatches, the state error is considered as an augmented state vector. Then the state error is estimated to correct the original state. The analysis of the proposed augmented model algorithm shows that the filter contains the equivalent adaptive multiple fading factors, which enhances the tracking ability. The filter is designed for augmented bias model and a mathematical simulation is carried out. The simulation results show that the filtering algorithm based on this model has strong robustness in tracking maneuvering targets.