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在实际目标跟踪系统中,测量设备都存在系统误差,会导致跟踪滤波精度显著下降。针对多测速系统,对其测速系统误差进行了简化数学建模;然后将其增广为状态变量,应用扩维无迹卡尔曼滤波对目标运动状态和系统误差进行联合估计,以实时校准系统误差、提高状态估计精度。在存在主副站2类系统误差的条件下,设定恒定和线性时变2类系统误差场景,对算法进行仿真分析。仿真结果表明,算法在2类系统误差情形下都能有效校准系统误差,位置、速度滤波精度可提高80%以上;尤其是当系统误差恒定时,算法可完全消除系统误差的影响。
In the actual target tracking system, there are systematic errors in the measurement equipment, which will lead to a significant decrease in tracking filtering accuracy. Aiming at the multi-speed system, a simplified mathematical model of the speed system error is proposed. Then, the system is augmented to a state variable. Jointly estimating the motion state and system error of the target with the unscented Kalman filter is used to calibrate the system error in real time , To improve the state estimation accuracy. Under the conditions of system error of class 2 of master and slave, two types of system error scenarios of constant and linear time-varying are set, and the algorithm is simulated and analyzed. The simulation results show that the algorithm can effectively calibrate the system error under the condition of two types of systematic errors, and the precision of position and velocity filtering can be improved by more than 80%. Especially when the system error is constant, the algorithm can completely eliminate the influence of system error.