TOA-based sensor position error suppression method in asynchronous network

Abstract

The invention discloses a TOA-based sensor position error suppression method in an asynchronous network. The method comprises the following steps: establishing a measurement model of a distance measurement value obtained by a sensor, and combining an actual coordinate position model of the sensor in a reference coordinate system and a robust least square criterion to obtain a non-convex positioning problem for solving a target source position and initial sending time; importing a relaxation variable and an auxiliary variable into a non-convex positioning problem, combining an S-program and a positive semidefinite relaxation technology, and relaxing the non-convex positioning problem into a positive semidefinite programming problem for solving a target source position and initial sending time; solving the positive semidefinite programming problem for solving the position of the target source and the initial sending time by adopting an interior point method to obtain an estimated value of the position of the target source. The method has the advantages that the influence of sensor position errors can be effectively reduced, the performance is still stable under the condition of largesensor position errors, and the positioning precision is high.

Description

technical field [0001] The invention relates to an error suppression technology, in particular to a TOA-based sensor position error suppression method in an asynchronous network. Background technique [0002] Target source localization has broad application prospects, such as water quality monitoring, target tracking, and emergency rescue, etc., so it has attracted extensive attention from the research community. In a wireless sensor network (WSN), target source localization is performed by multiple sensors, which determine the position of the target source by collecting measurements that carry target source location information. Commonly used measurements are Time of Arrival (TOA), Time Difference of Arrival (TDOA), Received Signal Strength (RSS), and Angle of Arrival (AOA). Among them, the target source localization algorithm based on TOA has the advantages of high positioning accuracy, so it has attracted the attention of researchers. However, the target source localizat...

AI Technical Summary

Problems solved by technology

Among them, the target source localization algorithm based on TOA has the advantages of high positioning accuracy, so it has attracted the attention of researchers. However, the target source localization algorithm based on TOA requires the sensor and the target source to be synchronized in time. It is guaranteed that the algorithm will not be affected by the initial sending time of the signal sent by the target source, but this synchronization process will consume a lot of execution costs. In order to save costs, at present, the joint estimation of the position of the target source and the initial sending time of the signal is often used. algorithm

[0003] In addition to the problem of time synchronization, in practice, due to the uncertainty in the deployment process of the sensor or the delay in the update of the sensor position, the position of the sensor cannot be obtained accurately. The researchers comprehensively considered these issues and proposed a series of A more realistic and cost-effective target source location algorithm

[0004] Yanbin Zou et al. proposed a target location algorithm based on maximum likelihood estimation (ML) in IEEE Communications Letters (Institute of Electrical and Electronics Engineers (IEEE) Communication Letters), which uses semi-positive definite relaxation techniques to approximate the solution based on maximum likelihood Estimation (ML) of the object localization problem, however the performance of the algorithm degrades sharply in the case of large sensor position errors

Enyang Xu et al. proposed two robust localization algorithms in IEEE Transactions on Signal Processing (Institute of Electrical and Electronics Engineers (IEEE) Signal Processing Transactions). These two target localization problems can be solved by semi-positive definite relaxation technology and S-program Approximate solution. Experiments show that the performance of these two algorithms is still stable in the case of large sensor position errors. However, the positioning performance of these two algorithms still has room for improvement.

Images