Self-adaptive positioning method and system based on resonance gradient method of received signal strength indicator (RSSI)

Abstract

The invention discloses a self-adaptive positioning method based on a conjugate gradient method of a received signal strength indicator (RSSI). The method comprises the following steps of: firstly, performing Gauss processing on a measured RSSI value to eliminate the influence of environmental factors; secondly, weighting the measured RSSI value; and finally, searching an optimal unknown node coordinate through the conjugate gradient method. A positioning system automatically detects the RSSI value at fixed time, so positioning parameters can be automatically adjusted and repositioned. The positioning system comprises at least three reference node modules, a to-be-positioned module, a gateway node module and a personal computer (PC), wherein the reference node modules are arranged in a detection area and used for providing position information; the gateway node module is connected with the to-be-positioned module and used for establishing a network and transmitting information; and the PC is connected with the gateway node module and used for determining the position of the to-be-positioned module according to the information which is sent by the gateway node module.

Description

technical field [0001] The invention relates to the field of positioning, in particular to an RSSI-based resonance gradient method self-adaptive positioning method and system. Background technique [0002] In recent years, with the rapid development of Micro-Electro-Mechanical System (Micro-Electro-Mechanism System, MEMS), System on Chip (SOC, System on Chip), wireless communication and low-power embedded technology, wireless sensor network (Wireless Sensor Network) has been born. Networks, WSN), and brought about a revolution in information perception with its low power consumption, low cost, distributed and self-organizing characteristics. As one of the key technologies of wireless sensor networks, node localization technology has become an important research direction and research topic. [0003] According to whether it is necessary to obtain the distance or angle information between the node to be positioned and the reference node, the positioning algorithms currently u...

AI Technical Summary

Problems solved by technology

The disadvantage of this algorithm is that if the external environment changes, a large amount of neural network training needs to be re-trained; in addition, the algorithm does not have a good adaptive positioning function

The disadvantages of this system are: GPS cannot realize indoor and underground positioning, which has great limitations on practical applications; the use of GSM modules for information transmission will generate costs and depend on the GSM network

[0008] (1) Without a good adaptive positioning function, when the external environment changes, a large amount of neural network training needs to be re-trained;

[0009] (2) The positioning of indoor and underground places cannot be realized, and there are great restrictions on practical applications

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