A Multi-target Precise Intelligent Positioning and Tracking Method Applicable to Supervision Sites

Abstract

The invention discloses a multi-target precise intelligent positioning and tracking method suitable for supervision places, which includes the following steps: collecting signal strength information of points of interest in the positioning area, and preprocessing the signal strength information to convert it into conforming depth The format of the LSTM network model; extract the local features of the preprocessed data to obtain a richer information representation of the original data, and input the deep LSTM network model to train the model; collect real-time location information of the supervision site, and pre-process the information Processing, converting it into a format that conforms to the deep LSTM network model; input the preprocessed data into the trained deep LSTM network model, and send the output of the deep LSTM network model to the fully connected layer to obtain a more differentiated representation form; finally use the linear regression layer to get the final location map, and output the location information. The method disclosed by the invention can effectively improve the positioning accuracy and improve the positioning speed.

Description

technical field [0001] The invention relates to a positioning and tracking method, in particular to a multi-target precise intelligent positioning and tracking method suitable for supervision places. Background technique [0002] Obtaining robust and accurate positioning in closed environments such as regulatory sites has always been the goal of researchers, and people spend most of their time in indoor environments. Therefore, the research on indoor positioning has always been a research topic in the industry. hotspot. In recent years, the proliferation of smartphones and other mobile devices has enabled many location-based services and provided a set of embedded sensors that can be used for indoor positioning. Due to the widespread popularity of AP access points and the vast majority of devices supporting the IEEE802.11 standard, many positioning systems based on received signal strength and channel state information technologies have been proposed. [0003] The location...

AI Technical Summary

Problems solved by technology

[0004] The existing indoor positioning system schemes mainly have the following defects: (1) In the indoor environment, the signal propagation faces shadow and multipath effects, and the signal strength measurement value has a lot of interference. In order to obtain satisfactory positioning performance, a large amount of data needs to be collected. Traditional Algorithms have great limitations in processing large and complex data

(2) Existing indoor positioning algorithms face problems such as low signal sampling rate and high packet loss rate. In many cases, due to insufficient collection of signal strength in the positioning environment, accurate and effective online positioning cannot be performed

(3) The existing indoor positioning system algorithm has high computational complexity, and requires a large amount of computing resources when faced with large data input

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