Abstract
The Indoor positioning based on the ZigBee received signal strength index has attracted more and more researchers’ attention and, due to its low cost, low hardware power consumption and easy implementation. However, because of multipath effects and shadow effects, traditional indoor positioning algorithms cannot obtain good positioning effects. In order to improve the accuracy of ZigBee indoor positioning, this paper proposes an indoor positioning algorithm of annealing algorithm (SA) and genetic algorithm (GA) optimized neural network (SAGA-BP), and the superiority of this algorithm is proved through simulation and experiment. First, establish the position relationship between the received signal strength indicator(RSSI) and the target position, and arrange the node network structure model to collect signals to establish a fingerprint database. Then use the mechanism of the annealing algorithm combined with the genetic algorithm to optimize the initial weight and initial threshold of the neural network algorithm, so that it can quickly jump out of the local optimal solution and achieve high-precision positioning. Experiments have proved the effectiveness of the positioning algorithm. Compared with BP and GA-BP algorithms, SAGA-BP positioning algorithm has an average error of 0.75m for RSSI signals after acquisition and processing, and an average error of BP positioning algorithm is 1.24m. The average error of GA-BP algorithm is 0.98m. Thus, the SAGA-BP algorithm has higher positioning accuracy.
Highlights
Group positioning algorithm [13] and neural network positioning algorithm [14], etc
With the continuous the average positioning error of non-training points is 0.22 m,and development of social modernization and the increasing number the positioning range is small; Zhenbao Yu et al [16] proposed the of large-scale buildings, people spend more than 80% of their time in indoor environments, and the demand for indoor location services is increasing
Since GPS(Global Positioning System) signals cannot be positioning, this paper proposes the SAGA-BP neural network received in an indoor environment, various indoor positioning indoor positioning algorithm
Summary
Group positioning algorithm [13] and neural network positioning algorithm [14], etc. In the neural network optimization positioning. When the genetic algorithm optimizes the neural network, the received signal strength received by the receiving terminal, global optimal solution that converges has some shortcomings denoted as A; Pr(d0 ) is the reference distance received such as local optimal solutions and relatively low accuracy. This Indication of received signal strength; in order to make the paper adds the annealing algorithm mechanism to make it more calculated n a positive value, the first "+" on the right side of the accurate to obtain the global optimal weights and thresholds equal sign in equation (19) is changed to "-", n is the signal path.
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