Ultra wide band indoor location algorithm based on maximum correlation entropy Kalman filter
With the continuous development of wireless communication technology,indoor positioning technology has gradually become the focus of attention.However,in a complex indoor environment,such as non-Gaussian noise,traditional ultra wide band(UWB)localization algorithms often have problems such as low positioning accuracy and poor robustness.The proposed algorithm introduces the maximum correntropy criterion(MCC)into the cost function of the Kalman filter(KF)algorithm,and models the measurement noise,so it can assign a small weight to the anomaly measurement to reduce its influence on the state estimation,which is more robust than the traditional Kalman filter algorithm.In the simulation experiment,multiple base stations are used to locate the moving target.The results show that the new algorithm can effectively improve the accuracy and robustness of indoor positioning compared with Kalman filter and unscented Kalman filter(UKF)in a non-Gaussian environment.
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维普
