Optimized Wireless Sensor Network Positioning Technology

On the basis of research on traditional wireless location, while not high for the existing positioning accuracy of neural network algorithm, we propose a new type of location-based algorithm PSO-BP network. In order to improve the accuracy of the system first Kalman filtering algorithm, and then by means of a PSO-BP algorithm BP initial weights and thresholds to optimize and compare existing RSSI algorithm to analyze the performance of different algorithms. Fixed BP neural network weights depends on the non-linear gradient value, easy to form a local minimum, while learning more frequently. Experimental results show that the improved PSO-BP algorithm based on the error back propagation adjustment weights on the use of learning mechanism correction weights improved PSO algorithm, an increase of BP algorithm convergence speed and global convergence, improved BP network learning ability.