Energy saving-type Internet of things target tracking method

Abstract

The invention discloses an energy saving-type Internet of things target tracking method, which belongs to the technical field of Internet of things target tracking methods. The method particularly comprises steps: sparse representation for moving target sensing information changes by a node in a sensing area is built, observation and sampling on the moving target sensing information by the node are used, and sensing differential information is reconstructed; a sensing differential method converted by a background differential method in a video tracking technology is then used for processing, the sensing area of the Internet of things is seen as a video monitoring area, moving of the target in the sensing area is seen as moving of a monitored target in the video, the sensing differential information and the initial sensing information are used to determine current effective tracking information, and a moving target is detected. A compressed sensing method is adopted to sample and reconstruct the sensing information, the sensing differential method is adopted for target tracking, and an energy saving-type and high-precision Internet of things target tracking method is provided. The method can be applied to various industries such as security and theft prevention, environmental monitoring, military tracking, and cargo warehousing, and has a wide application prospect.

Description

technical field [0001] The present invention specifically relates to an energy-saving Internet of Things object tracking method, and belongs to the technical field of Internet of Things object tracking methods. Background technique [0002] The general flow chart of IoT target tracking technology is as follows: figure 1 As shown, the target tracking technology mainly involves several aspects such as node scheduling strategy, target state estimation, target prediction method, and network topology. Most of the existing IoT tracking technologies use distance-based positioning methods, such as position calculation based on time of arrival T0A, time difference of arrival TDOA, angle of arrival AOA or received signal strength indicator RSSI. None of these methods consider reducing the sampling amount of data in the network through compressive sensing methods, thereby reducing network energy consumption. Compressed sensing technology can reconstruct the original signal by samplin...

AI Technical Summary

Problems solved by technology

However, this method does not combine the application of the perceptual difference method, nor does it convert the RSSI-based target tracking problem into a compressed sensing problem, and has disadvantages such as uneven node dormancy and low tracking accuracy of moving targets.

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