Device-free localization method based on space migration compressive sensing

Abstract

The invention discloses a device-free localization method based on space migration compressive sensing, and belongs to the field of device-free localization. The method comprises the following steps: deploying sensor nodes; collecting matrixes at reference positions in a sample area and a to-be-monitored area to obtain a migration function; migrating the sensing matrix in the sample area and a measurement vector in the to-be-monitored area according to the migration function to obtain a migrated sensing matrix and a migrated measurement vector; and recovering the position of a target by adopting the theory of compressive sensing according to the migrated sensing matrix and the migrated measurement vector. According to the invention, the sensing matrix in the sample area and the measurement vector in the to-be-monitored area are migrated, and the position information of the target in the monitored area is determined by a compressive sensing localization method. Therefore, human consumption and communication cost brought by sensing matrix rebuilding for the to-be-monitored area are avoided, and the feasibility of realizing localization of different areas through compressive sensing is improved.

Description

technical field [0001] The invention relates to the field of passive positioning, in particular to a passive positioning method based on spatial migration compressed sensing. Background technique [0002] In recent years, the passive positioning (Device Free Localization, referred to as DFL) technology does not require users to wear any wireless devices and does not require users to actively participate in the positioning process, which has attracted great attention from academia and industry. The mainstream passive positioning method is to use the target to be located to locate the disturbance of the wireless signal in the monitoring area. It generally has two steps: in the training phase, based on the "Received Signal Strength" (Received Signal Strength, referred to as RSS) and "target position ” relationship to establish a positioning model (prior knowledge base); in the positioning phase, the location of the target is determined by matching the real-time RSS value with t...

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Problems solved by technology

However, when the monitoring area changes, it is also necessary to construct a perception matrix for different areas, so it cannot solve the problem of high manpower consumption

[0008] To sum up, these three types of positioning methods do not take into account the problem of monitoring area changes, that is, the positioning model established for a given area cannot be used for new areas of different sizes

Moreover, it is very unrealistic to establish a corresponding positioning model for all regions of different sizes in reality.

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