Yang Bo, Qiu Quanwei, Han Qing-Long, Yang Fuwen
IEEE Trans Cybern. 2022 Feb;52(2):727-737. doi: 10.1109/TCYB.2020.2983544. Epub 2022 Feb 16.
Most of the existing localization schemes necessitate a priori statistical characteristic of measurement noise, which may be unrealistic in practical applications. This article addresses the problem of indoor localization by implementing distributed set-membership filtering based on a received signal strength indicator (RSSI) under unknown-but-bounded process and measurement noises. First, the transmit power and the path-loss exponent are estimated by a novel least-squares curve fitting (LSCF) method in RSSI-based localization. Since the localization process of trilateration is susceptible to inaccuracy caused by the noise-affected distance measurements, a convex optimization method is then developed to obtain the state ellipsoid estimation under the unknown-but-bounded noises. Third, a recursive algorithm is established to compute the global ellipsoid that guarantees to locate the true target at every time step. Finally, experimental validation is presented to demonstrate the accuracy and effectiveness of the proposed set-membership filtering method for indoor localization.
现有的大多数定位方案都需要测量噪声的先验统计特性，这在实际应用中可能不切实际。本文通过在未知但有界的过程和测量噪声下，基于接收信号强度指示符（RSSI）实现分布式集员滤波，解决了室内定位问题。首先，在基于RSSI的定位中，通过一种新颖的最小二乘曲线拟合（LSCF）方法估计发射功率和路径损耗指数。由于三边测量的定位过程容易受到噪声影响的距离测量所导致的不准确性，因此开发了一种凸优化方法，以在未知但有界的噪声下获得状态椭球估计。第三，建立了一种递归算法来计算全局椭球，以确保在每个时间步长都能定位到真实目标。最后，通过实验验证来证明所提出的集员滤波方法用于室内定位的准确性和有效性。
