Xu Sheng, Liu Qianyun, Lin Min, Wang Qing, Chen Kaile
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Advanced SoC and IoT Technology Laboratory (ASITLAB), Shanghai University, Shanghai 200444, China.
Sensors (Basel). 2025 Jan 26;25(3):740. doi: 10.3390/s25030740.
In complex indoor environments, target tracking performance is impacted by non-line-of sight (NLOS) noises and other measurement errors. In order to fix NLOS errors, a double extended Kalman filter (DEKF) algorithm is proposed, which refers to a kind of cascaded structure composed of two Kalman filters. In the proposed algorithm, the first filter is a classic Kalman filter (KF) and the second is an extended Kalman filter (EKF). Time of arrival (TOA) measurements collected by multiple stationary ultra-wideband (UWB) sensors are used. The residual errors between the measured TOA and that of the first KF are predicted, and the covariance of the first KF is adjusted correspondingly. Then, we use the estimated distance state of the first KF as a measurement vector for the second EKF in order to obtain a smoother observation. One of the advantages of the proposed algorithm is that it is able to perform target tracking with good accuracy even without or with only one LOS TOA measurement for a period of time without prior information about the NLOS noise, which may be difficult to obtain in practical applications. Another advantage is that the accuracy does not greatly decrease when NLOS noises exist for a long period of time. Finally, the proposed DEKF can maintain the high-precision positioning characteristics in both the constant velocity (CV) model and the constant acceleration (CA) model in the LOS/NLOS environment. Our simulation and experimental results show that the proposed algorithm performs much better than other algorithms in SOTA, particularly in severe mixed LOS/NLOS environments.
在复杂的室内环境中，目标跟踪性能会受到非视距（NLOS）噪声和其他测量误差的影响。为了解决NLOS误差问题，提出了一种双扩展卡尔曼滤波器（DEKF）算法，它是一种由两个卡尔曼滤波器组成的级联结构。在所提出的算法中，第一个滤波器是经典卡尔曼滤波器（KF），第二个是扩展卡尔曼滤波器（EKF）。使用多个固定超宽带（UWB）传感器收集的到达时间（TOA）测量值。预测测量的TOA与第一个KF的TOA之间的残差误差，并相应地调整第一个KF的协方差。然后，我们将第一个KF的估计距离状态用作第二个EKF的测量向量，以获得更平滑的观测值。所提出算法的优点之一是，即使在没有NLOS噪声的先验信息（这在实际应用中可能难以获得）的情况下，或者在一段时间内只有一个视距（LOS）TOA测量值的情况下，它也能够以良好的精度执行目标跟踪。另一个优点是，当NLOS噪声长时间存在时，精度不会大幅下降。最后，所提出的DEKF在LOS/NLOS环境中的匀速（CV）模型和匀加速（CA）模型中都能保持高精度定位特性。我们的仿真和实验结果表明，所提出的算法在SOTA中比其他算法表现得更好，特别是在严重的混合LOS/NLOS环境中。
