An improved filtering algorithm for indoor localization based on DE-PSO-BPNN

• Published in: Journal of intelligent & fuzzy systems Vol. 44; no. 6; pp. 9513 - 9525
• Main Authors: Wang, Encheng, Liu, Xiufeng, Wan, Jiyin
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.06.2023; Sage Publications Ltd
• Subjects: Accuracy; Algorithms; Artificial neural networks; Back propagation networks; Clustering; Evolutionary algorithms; Evolutionary computation; Global optimization; Kalman filters; Local optimization; Localization; Neural networks; Optimization; Particle swarm optimization; Preprocessing; Signal strength; DBSCAN-KF; RSSI; Kalman filtering; DE-PSO-BP; Indoor localization
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-230178

Among the indoor localization algorithms, the algorithm based on traditional Back Propagation Neural Network (BPNN) has the problems of slow convergence and easy to fall into local optimum. It is difficult to apply the algorithm in noisy environments. Therefore, in this paper, we propose a novel indoor localization algorithm where the whole localization process is divided into two parts: data preprocessing and localization output. Data preprocessing means using filtering algorithm to process the Received Signal Strength Indication (RSSI) sequence. It is considered that the initial value of the received sequence has a significant impact on the performance of Kalman Filter (KF). An improved Kalman Filtering algorithm (DBSCAN-KF) is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. First, the RSSI values that are seriously disturbed by noise in the sequence are removed using the DBSCAN algorithm, and then the RSSI sequences are processed using KF so that the RSSI values can be closer to the theoretical values. The localization output part is to reduce the localization error caused by the BPNN. In this paper, the Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm are combined, and the Differential Evolution Particle Swarm Optimization (DE-PSO) algorithm is proposed. The BPNN weights and thresholds are optimized in parallel, which improves the speed and ability of global optimization search and further avoids the shortcomings of traditional BPNNs that are prone to fall into local optimization in the training process. Experimental results show that the BPNN localization algorithm based on DBSCAN-KF improves the average localization accuracy by 0.26m compared with the BPNN localization algorithm without filtering. After filtering, the localization algorithm based on DE-PSO improved BPNN (DE-PSO-BP) improves the average localization accuracy by about 24% compared with the localization algorithm based on DE-PSO-BP. The localization algorithm based on DE-PSO-BP improves the average localization accuracy by about 61% compared with the traditional BPNN.