A Node Accelerated Gradient Descent Locating Method

Abstract

The invention provides a node acceleration gradient descent positioning method and belongs to the field of network positioning. According to the method, an iterative thought is introduced, optimization momentum is continuously updated, so that a minimum loss function is achieved, and therefore a corresponding unknown base station coordinate is obtained; a threshold value is added, so that the probability that the algorithm is caught in local optimum is reduced. Through simulation comparison analysis and compared with particle swarm optimization and a random gradient method, the node acceleration gradient descent positioning method has significant advantages in positioning precision and efficiency.

Description

technical field [0001] The invention relates to a node accelerated gradient descent positioning method, which belongs to the field of network positioning. Background technique [0002] In the sensor network positioning problem, the traditional positioning method using Received Signal Strength Indicator (RSSI) has the problems of unstable received signal and low positioning accuracy, which can no longer meet the needs of the rapid development of wireless sensor network technology. Higher requirements for positioning accuracy and positioning efficiency. The positioning algorithm based on ranging has higher positioning accuracy than the non-ranging positioning algorithm. As a representative of indirect ranging, the RSSI positioning algorithm has become a research hotspot due to its ease of implementation that does not depend on peripheral hardware. But on the other hand, since it is susceptible to interference by wireless signal strength, the instability in the measurement pr...

AI Technical Summary

Problems solved by technology

In the preprocessing link, methods such as increasing the measurement frequency of channel parameters, using filters to process RSSI data, or performing Gaussian fitting on the signal transmission model are used to improve the accuracy of the model, so as to achieve the purpose of accurate distance estimation; while in positioning In the process of solving, for the sensor network with few reference base stations, the maximum likelihood estimation technique is used to solve the unknown base station coordinates in the simultaneous equations. At present, there is a positioning algorithm based on the maximum likelihood solution, which uses the twice weighted minimum The square method obtains more accurate positioning coordinates, but it does not consider the influence of the attenuation coefficient of the channel model on the signal transmission distance, and relies too much on prior knowledge to reduce measurement errors

Aiming at the lack of consideration of environmental factors, "Weighted Probabilistic Positioning Algorithm Based on RSSI Ranging" (published source: Journal of Electronic Measurement and Instrumentation, 2014, 28(10): 1123-1129.) proposes a weighted probability positioning based on RSSI Algorithm, to a certain extent, emphasizes the influence of the environment on the positioning model, but it has the disadvantage that once the environmental parameters are determined, it will not be adjusted. not obvious

[0004] Although the positioning accuracy can be improved by adding reference base stations, as the number of base stations increases, the error accumulation of RSSI values ​​​​between base stations will also reduce the positioning accuracy of unknown base stations.

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