A combined DASSA-BP and TSRK-MinMax algorithm for high accuracy beacon indoor positioning

Beacon indoor positioning methods with Bluetooth has attracted the interest of researchers by its low cost, low energy consumption, and easy implementation. The key two steps of positioning in wireless systems are inference the accurate distance from the received signal strength indication (RSSI), and computing accurate location information from the inferred distance. The noise and multi-paths of the wireless channel will lead to complex nonlinearity relationship between RSSI and distance, which is difficult to modeled directly by the simple functions. Back Propagation (BP) neural networks can be used to construct ranging models with RSSI, but it will easily fall into local optimization which will lead to inaccurate. Besides, the MinMax localization algorithm used in the computing location information is easily affected by the fluctuation of the range value then will affect the localization accuracy. In this paper, a combined dynamic adaptive sparrow search (DASSA) with two-step residual network optimized MinMax (TSRK-MinMax) algorithm is proposed to improve the accuracy of beacon indoor localization. First, we use a dynamic adaptive sparrow search (DASSA) algorithm to optimize BP neural network to improve the ranging accuracy. Next, we utilize a K-Nearest Neighbor(KNN) algorithm to select the base stations which can receive the best signal. Then a two-step residual network are used to optimized MinMax algorithm (TSRK-MinMax) which leads to accuracy localization. The experimental results show that the overall localization error is reduced by 11.7%, which effectively improves the accuracy and robustness of the Beacon indoor localization.