To reduce the influence of received signal strength indication (RSSI) on ranging error, as well as the influence of least squares support vector regression (LSSVR) on localization algorithm, a node threedimensional localization algorithm based on RSSI and LSSVR parameter optimization is proposed. First, the RSSI average values at 1-25m in four different directions are collected by experiments and the weighted recursive mean optimization method is used to optimize the values of RF factor and propagation factor. Then, the parameters of RBF kernel function and grid width of LSSVR are optimized. Finally, the RSSI range values are used as the input of LSSVR localization model, and the LSSVR regression model is used to solve, in this way, the location estimation of unknown WSN nodes is realized. The simulation results show that the average localization error of the algorithm without parameter optimization is 21.82%, and the localization error of the algorithm after parameter optimization is 11.70%, which has higher localization accuracy. At the same time, a node three dimensional localization experiment platform was built to verify the proposed algorithm in the actual environment, and the test results verified the effectiveness and superiority of the proposed algorithm.
To reduce the influence of non-line-of-sight (NLOS) errors in the ultra-wideband (UWB) positioning process, a UWB positioning algorithm based on fuzzy inference and adaptive anti-NLOS Kalman filtering (KF) was proposed in this paper. First of all, the NLOS errors of the channel impulse response (CIR) signal characteristics were estimated by the fuzzy inference algorithm and then initially mitigated. Next, an adaptive anti-NLOS KF algorithm was developed to perform a second mitigation on the ranging errors after mitigation of the NLOS errors with the fuzzy inference, thereby further raising the range estimation accuracy. At last, the range estimation information after error mitigation was taken as the ranging information of the LS positioning algorithm for target localization. In the static positioning experiment, the probability of producing an error range of less than 19.1 cm with the positioning algorithm combining fuzzy inference with adaptive anti-NLOS KF was 0.93, which was much better than the positioning algorithm based on fuzzy inference and the adaptive anti-NLOS KF positioning algorithm. In the dynamic positioning experiment, compared with the adaptive anti-NLOS KF positioning algorithm, the RMSE was reduced by 43.31% in the overall positioning. Furthermore, compared with those of the positioning algorithm based on fuzzy inference, the RMSEs in overall positioning were lowered by 12.89%. The positioning accuracy was improved significantly.
In this paper, a moving target tracking (MTT) algorithm based on the improved resampling particle filter (IRPF) was put forward for the reduced accuracy of particle filter (PF) due to the lack of particle diversity resulting from traditional resampling methods. In this algorithm, the influences of the likelihood probability distribution of particles on the PF accuracy were firstly analyzed to stratify the adaptive regions of particles, and a particle diversity measurement index based on stratification was proposed. After that, a threshold was set for the particle diversity after resampling. If the particle diversity failed to reach the set threshold, all new particles would be subjected to a Gaussian random walk in a preset variance matrix to improve the particle diversity. Finally, the performance of related algorithms was tested in both simulation environment and actual indoor ultrawideband (UWB) nonline-of-sight (NLOS) environment. The experimental results revealed that the nonlinear target state estimation accuracy was maximally and minimally improved by 12.83% and 1.97%, respectively, in the simulation environment, and the root mean square error (RMSE) of MTT was reduced from 17.131 cm to 10.471 cm in actual UWB NLOS environment, indicating that the IRPF algorithm can enhance the target estimation accuracy and state tracking capability, manifesting better filter performance.
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