基于改进自适应卡尔曼滤波算法的温室UWB定位技术

UWB Greenhouse Positioning Technology Based on Improved Adaptive Kalman Filter Algorithm

摘要:

针对农业温室环境中，由于超宽带（Ultra -wideband, UWB）定位技术干扰免疫差和统计特性未知而面临定位精度不足的问题，本文提出一种基于改进自适应卡尔曼滤波（Improved adaptive Kalman filter，IAKF）算法的UWB定位技术。首先，引入异常检测机制，以识别滤波过程中的发散现象；进而，通过实时更新量测噪声协方差矩阵，抑制滤波发散，在噪声强波动情况下增强算法适应性；同时，开展3种不同环境噪声下仿真定位试验，对比分析UWB、IAKF、自适应卡尔曼滤波（Adaptive Kalman filter，AKF）及卡尔曼滤波（Kalman filter，KF）算法性能。仿真结果表明，IAKF算法展现出更强的适应性及鲁棒性。以自主开发农用履带车辆为定位载体，于农业温室环境中开展UWB定位试验。试验结果表明，温室环境中，履带车辆在视距（Line of sight，LOS）和非视距（Non line of sight，NLOS）场景下，较AKF和KF算法，IAKF算法定位精度分别提高22.2%、13.0%和20.0%、15.4%。

Abstract:

Aiming to address the issue of insufficient positioning accuracy of ultra-wideband (UWB) positioning technology in agricultural greenhouse environments, caused by poor interference immunity and unknown statistical characteristics, a UWB positioning technology was proposed-based on an improved adaptive Kalman filter (IAKF) algorithm. Firstly, an anomaly detection mechanism was introduced to identify divergence phenomena during the filtering process. Subsequently, the measurement noise covariance matrix was updated in real-time to suppress filter divergence and enhance the algorithm’s adaptability in the presence of strong noise fluctuations. Simulation positioning experiments under three different noise environments were conducted to compare and analyze the performance of UWB, IAKF, adaptive Kalman filter (AKF), and Kalman filter (KF) algorithms. The simulation results showed that the IAKF algorithm exhibited stronger adaptability and robustness. Finally, using a self-developed agricultural tracked vehicle as the positioning carrier, UWB positioning experiments were conducted in the greenhouse environment. The experimental results indicated that in the greenhouse environment, the positioning accuracy of the tracked vehicle using the IAKF algorithm was improved by 22.2% and 13.0% in-line of sight (LOS) and 20.0% and 15.4% in non-line of sight (NLOS) scenarios compared with that of the AKF and KF algorithms, respectively.