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Convex optimization and graph theory-combined generalized rank target sensing network spatio-temporal data positioning method

A technology of spatio-temporal data and positioning method, applied in positioning, measuring devices, instruments, etc., can solve the problems of inapplicability of brute force search, the increase of computational complexity of the correlation of measured values, etc., and achieve the effect of improving the level of practicality and improving the quality of tracking.

Inactive Publication Date: 2019-03-01
ZHENGZHOU UNIVERSITY OF AERONAUTICS
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Problems solved by technology

The measurement value correlation problem has exponentially increasing computational complexity with the number of sensor nodes, therefore, brute force search is obviously not applicable

Method used

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  • Convex optimization and graph theory-combined generalized rank target sensing network spatio-temporal data positioning method
  • Convex optimization and graph theory-combined generalized rank target sensing network spatio-temporal data positioning method
  • Convex optimization and graph theory-combined generalized rank target sensing network spatio-temporal data positioning method

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Embodiment Construction

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0015] refer to figure 1 , the embodiment of the present invention provides a generalized rank target sensor network spatio-temporal data positioning method based on convex optimization fusion graph theory, the method mainly includes the following steps:

[0016] Using SDR and S-process convex optimization technology to model and analyze TDOA measurement values, transform nonlinear non-convex problem modeling into SDP convex optimization problem, and use conve...

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Abstract

The invention discloses a convex optimization and graph theory-combined generalized rank target sensing network spatio-temporal data positioning method and belongs to the technical field of wireless sensor networks. According to the method, an SDR and S-process convex optimization technology is adopted to perform modeling analysis; a nonlinear non-convex problem is modeled into an SDP convex optimization problem; convex optimization and a graph theory are combined to transform a target-measurement correlation unknown problem into a standard weighted complete bigraph (SWCB) matching problem, aconvex optimization positioning model is constructed; and a linear programming technique is adopted to perform positioning solving according to a transformation result. Experiments show that the performance of the method is significantly better than that of a conventional positioning algorithm under low-SNR and small-snapshot number scenarios; the algorithm has obvious advantages in tracking performance and can significantly improve the quality of multi-target tracking; the practical level of the spatio-temporal data mining target sensing method is improved; and theoretical support can be provided for solving the target sensing positioning problem of the spatio-temporal data of the sensor networks.

Description

technical field [0001] The invention relates to the technical field of wireless sensor networks, in particular to a generalized rank target sensor network spatio-temporal data positioning method based on convex optimization fusion graph theory. Background technique [0002] The perception and localization of unknown targets in Wireless Sensor Network (WSN) is a hot issue in the field of spatio-temporal data mining and remote sensing measurement. It plays an important role in resource exploration, environmental monitoring, intelligent building, urban transportation, space exploration, safety monitoring and many other fields. The sensor network space-time data environment has unique difficulties in realizing generalized rank target positioning. First of all, medium randomness, dynamic target mobility, and non-uniform disturbance interference in complex spatiotemporal data scenarios of sensor networks have the characteristics of time-varying, space-varying, dynamic, and multi-...

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Application Information

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IPC IPC(8): G01S5/02
CPCG01S5/02G01S5/0278
Inventor 周鹏赵青刘兆瑜张宏亮刘战合乔恒恒陆欣月王艳艳谈欣孙浩赵瑞鑫
Owner ZHENGZHOU UNIVERSITY OF AERONAUTICS
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