基于UWB的加权自适应卡尔曼滤波室内定位算法

UWB-based weighted adaptive Kalman filtering indoor positioning algorithm

针对传统卡尔曼滤波(KF)定位算法在室内复杂环境下精确度低、抗干扰能力和稳定性差等问题,提出了一种基于超宽带(UWB)的加权自适应卡尔曼滤波(WKF)—到达时间差(TDOA)定位算法。首先,建立四锚UWB定位系统,引入无线时钟同步技术消除时钟误差;其次,对原始数据进行误差补偿,减少由人体反射和多径效应等影响因素引起的定位误差;然后,通过递归更新噪声协方差并动态调整权值以增强滤波器的稳定性;最终实现移动目标的动态实时定位。实验结果表明:所提算法可以减少由多径效应引起的定位误差,实现移动目标的动态实时高精度定位,与现有的其他定位方法相比,有效地提升了定位精度和鲁棒性。

Aiming at the problems of low accuracy, poor anti-interference ability, and poor stability of traditional indoor Kalman filter positioning algorithm in complex indoor environments, a weighted adaptive Kalman filtering(WKF)time difference of arrival(TDOA)positioning algorithm, based on ultra-wideband(UWB)is proposed.Firstly, establish a four-anchor ultra-bandwidth positioning system, introduce wireless clock synchronization technology to eliminate clock errors.Secondly, compensate the original data to reduce positioning error caused by human reflection and multipath effect and other influencing factors.Then, recursively update the noise covariance and dynamically adjust the weights to enhance the stability of the filter.Finally, dynamic real-time positioning of moving target is realized.The results show that WKF-TDOA can reduce positioning errors caused by multipath effect, and achieve dynamic real-time high-precision positioning of moving targets.Compared with other existing positioning methods, the experimental results show that this method has higher precision and better robustness.