无线电工程

针对接收信号强度指示(Received Signal Strength Index, RSSI)定位易受到环境因素的影响，提出了一种基于RSSI扩展卡尔曼滤波的改进蛇定位算法(RSSI Extended Kalman Filter-based Improved Snake Optimization Localization Algorithm, RSSI-EISL)。该算法利用扩展卡尔曼滤波(Extended Kalman Filter, EKF)模型对RSSI信号值进行平滑处理，使其能够抑制噪声和异常值对估计结果的影响，从而提高测距的准确性和鲁棒性。通过引入Levy飞行和非线性收敛因子的改进蛇优化算法(Improved Snake Optimization Algorithm, ISO),提升了蛇优化算法(Snake Optimization Algorithm, SO)的寻优能力，使之能够更加准确地计算出待测节点的坐标。根据仿真结果显示，相较于基于RSSI最小二乘定位算法(RSSI Ordinary Least Squares Localization Algorithm, ROL)、基于RSSI EKF的灰狼定位算法(RSSI Extended Kalman Filter-based Grey Wolf Optimization Algorithm, REGL)和基于RSSI EKF的蛇定位算法(RSSI EKF-based Snake Optimization Localization Algorithm, RESL),RSSI-EISL的定位精度分别提高了26.4%、8.75%和5.6%,算法的收敛速度和全局搜索能力也有所提升。

In order to address the impact of environmental factors on the Received Signal Strength Index(RSSI) for localization, a based RSSI Extended Kalman Filter Improved Snake Optimization Localization Algorithm(RSSI-EISL) is proposed. This algorithm utilizes the Extended Kalman Filter(EKF) model to smooth the RSSI signal values and suppress the influence of noise and outliers on the estimation results, thereby improving the accuracy and robustness of distance measurement. By introducing the Improved Snake Optimization Algorithm(ISO) with Levy flight and nonlinear convergence factor, the ability of the Snake Optimization Algorithm(SO) is enhanced, enabling more accurate calculation of the coordinates of the nodes to be measured. According to simulation results, the proposed RSSI-EISL improves the localization accuracy by about 26.4%, 8.75%, and 5.6% compared to RSSI Ordinary Least Squares Localization Algorithm(ROL), RSSI Extended Kalman Filter-based Grey Wolf Optimization Algorithm(REGL), and RSSI EKF-based Snake Optimization Localization Algorithm(RESL) algorithms, respectively, while the convergence speed and global search capability of the algorithm are also improved.