Research of RSSI Indoor Ranging Algorithm Based on Sparrow Search Algorithm and BP Neural Network

Traditional methods for location estimation using Received Signal Strength Indication (RSSI) rely on the log-normal shadow model to formulate the range measurement model. However, the parameter selection in this method is usually based on empirical data, which is easily affected by environmental factors, resulting in a decrease in the accuracy of ranging. In order to improve the ranging accuracy and reduce the impact of RSSI fluctuation, we proposed a new ranging method to develop a powerful ranging model using sparrow search algorithm and back propagation neural network (SSA-BP). In this method, the RSSI value of the target node is first initially normalized and then input into the SSA-BP ranging model to output the distance between the target node and the anchor node. Experimental results show that compared with the traditional BP algorithm and genetic algorithm (GA), the SSA-BP algorithm has faster convergence speed and higher ranging accuracy.