As a paradigm of the new-generation indoor positioning technology, ultra-wideband (UWB) technology is often combined with the inertial navigation system (INS) in practical applications to solve the non-line-of-sight (NLOS) error issue in positioning. However, the centralized information processing method fails to effectively distinguish the sources of NLOS errors. To ensure positioning accuracy, additional anchor nodes need to be deployed, which leads to redundancy of positioning anchor nodes, and further results in information waste and increased costs. Aiming at the problems of NLOS error identification and elimination in indoor positioning, this paper proposed a UWB/INS indoor positioning method based on self-reset genetic particle filtering (SGPF). With the SGPF algorithm as its core, this method traces the source of NLOS errors in measured values using the estimated values of the INS system, so as to improve the tracking stability under NLOS environments. The method first groups physical anchor nodes and divides likelihood regions in combination with virtual anchor nodes. Then, based on the preliminary estimation of the INS, it identifies high-probability regions through an NLOS error identification strategy, while eliminating NLOS anchor node groups and their corresponding measured values. Finally, it judges the state of the particle set by combining the number of effective particles, determines whether to enable genetic resampling to optimize particle diversity, and ultimately improves the robustness of the algorithm. The SGPF algorithm integrates the structural advantages of the standard particle filter (PF) and genetic algorithms, and can effectively alleviate the problems of particle degradation and impoverishment and achieve higher robustness with a smaller number of particles and lower time consumption. Experimental results show that: under line-of-sight environments, the SGPF algorithm requires only 30% of the number of particles used in the PF algorithm to achieve the same positioning effect, and its calculation time is much lower than that of the traditional genetic particle filter algorithm; under NLOS environments, the SGPF algorithm has an average positioning error of 0.0552 m. Compared to traditional particle filter and traditional genetic particle filter algorithms, the localization error is reduced by 56.98% and 48.94%respectively.

As a commonly used non-Gaussian nonlinear filtering method, particle filter has been successfully applied in various engineering fields. However, the traditional resampling method leads to the problem of particle depletion, which seriously reduces the accuracy and robustness of the filter estimation. This paper proposed a self-reset particle filter method that combines tracking failure detection and enhanced differential evolution optimization. Firstly, the filter estimation value is preliminarily checked by the tracking failure identification method, and the optimization strategy is not enabled during normal tracking, and the algorithm performance is consistent with the standard particle filter. When the tracking fails, the particle set is reset by differential optimization. During the reset process, the upper and lower bounds of particle confidence interval are set to prevent the particles from being over-concentrated, and the multiple optimization of the particles is avoided by combining the test indication value to reduce the estimation time of the algorithm. The simulation results show that the proposed algorithm inherits the advantages of standard particle filter and differential evolution particle filter through dynamic adjustment, and it effectively improves the robustness and estimation accuracy of the filter estimation. It can avoid using the optimization strategy to reduce the overall time complexity of the algorithm when the filter is successful, and enable the differential optimization strategy to self-reset when the filter fails. In addition, under the same positioning accuracy, the number of particles required by the algorithm is lower than that of standard particle filter, and the overall time consumption is lower than differential evolution particle filter, which also works well when modeling is uncertain.

The standard particle filter has the problem of particle impoverishment, while dealing with nonlinear problems requires many particles to achieve the required estimation accuracy, so the standard particle filter reduces the comprehensive performance of algorithm. This paper proposed a hybrid multi-strategy optimization particle filtering algorithm, which combines Levy flight strategy, differential evolution algorithm and success history strategy. The method firstly defines the basic framework of the sample set with Levy flight strategy, and optimizes the low-weight invalid particles with the differential evolution algorithm. Then the successful history strategy was used to adjust the parameters adaptively, to dynamically adjust the algorithm’s optimum length, so as to guide more particles to the high likelihood region. Simulation results show that the proposed algorithm can effectively improve the particle diversity and filtering accuracy, enhance the particle impoverish problem under low measurement noise, and reduce the number of particles required for nonlinear system estimation.