Indoor Geomagnetic Matching Location Based on Iterative Local Search and Improved Particle Swarm Fusion

In the complex indoor environment, geomagnetic matching is an effective way to realize indoor positioning of mobile robots. Aiming at the problem that the application of Particle Swarm Optimization (PSO) algorithm leads to the decline of geomagnetic matching accuracy, stability and convergence speed, an Iterated Local Search-Improved Particle Swarm Optimization (ILS-IPSO) algorithm is proposed. By analyzing the time-frequency characteristics and data distribution characteristics of geomagnetic survey data, geomagnetic data preprocessing and geomagnetic reference map construction are carried out. By introducing 3${\bm{\sigma }}$ contour Search domain constraint in the particle swarm optimization process, the weight factor, learning factors, step control factor of PSO algorithm are optimized, and finally the Local disturbance and search are implemented in combination with Iterated Local Search (ILS) algorithm. The experimental results show that the average matching accuracy error of ILS-IPSO is reduced to 0.0508 m, and the standard deviation of matching error is reduced to 0.0198m. Compared with PSO, Linear Dynamic time-varying Inertial Weight Particle Swarm Optimization algorithm (LDIW-PSO) and Cosine Decreasing Inertia Weight Particle Swarm Optimization algorithm (CDIW-PSO) algorithms, the average matching accuracy is increased by 94.72%, 92.37% and 87.98%, the standard deviation of matching error decreased by 87.13%, 83.26% and 89.81% respectively. The optimal fitness of ILS-IPSO algorithm is increased by 79.51%, 61.81% and 57.06%, and the iteration efficiency is increased by 69.23%, 55.56% and 33.33%, respectively. This method performs well in the accuracy, stability and convergence of geomagnetic positioning, and can be widely used in the field of indoor positioning.