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在组合系统运用 Kalman 滤波器技术时,准确的系统模型和可靠的观测数据是保证其性能的重要因素,否则将大大降低 Kalman 滤波器的估计精度,甚至导致滤波器发散。为解决上述 Kalman 应用中的实际问题,提出了一种新颖的基于进化人工神经网络技术的自适应 Kalman 滤波器。仿真试验表明该算法可以在系统模型不准确时、甚至外部观测数据短暂中断时,仍能保证 Kalman 滤波器的性能。
When the combined system uses Kalman filter technology, the accurate system model and reliable observational data are important factors to ensure its performance. Otherwise, the estimation accuracy of Kalman filter will be greatly reduced and even the filter will diverge. In order to solve the above practical problems in Kalman applications, a novel adaptive Kalman filter based on evolutionary artificial neural network is proposed. Simulation results show that this algorithm can guarantee the performance of Kalman filter even when the system model is inaccurate, even when the external observation data is briefly interrupted.