Convex optimization and multi-target particle swarm-based UWB positioning method

Abstract

The invention relates to a convex optimization and multi-target particle swarm-based UWB positioning method and belongs to the field of wireless communication networks. When that non-line-of-sight errors exist in TDOA measurement values and cannot be directly identified is assumed, a grouping strategy is put forward for subjecting the TDOA measurement values to random grouping operation, that the number of measurement values in each group satisfies minimum requirements for TDOA positioning, and grouping measurement value data is used as input data for a multi-target particle swarm optimization algorithm. Semi-definite programming relaxation is adopted for converting non-convex and non-linear TDOA problems into convex optimization problems, and a convex optimization object function is used as a fitness function for a multi-target particle swarm algorithm. Fitness values can be calculated conveniently and rapidly, effects exerted on positioning precision by the non-line-of-sight errors can be dispersed, range finding errors caused by non-line-of-sight interference during UWB signal transmission processes can be reduced and even eliminated, and effects exerted on positioning performance by selection of reference base stations can be prevented.

Description

technical field [0001] The invention belongs to the field of wireless communication networks, and relates to a UWB positioning method integrating convex optimization and multi-objective particle swarm. Background technique [0002] Compared with traditional indoor positioning technology, such as infrared positioning technology, Bluetooth technology, ZigBee technology, radio frequency identification technology, etc., UWB positioning technology has high multi-path resolution, strong penetrating power, low power consumption, easy integration, high Positioning performance and other advantages have become the most widely used wireless communication technology for positioning. [0003] With the continuous development of human society, more and more optimization problems need to be solved. Optimization plays a central role in both operations research and management science. It usually refers to maximizing or minimizing a multivariate function, and Satisfy some equality or inequali...

AI Technical Summary

Problems solved by technology

The traditional least squares algorithm to solve the TDOA problem belongs to the first category. By adding an intermediate variable and ignoring the relationship between it and other variables, the nonlinear equations are linearized. In the scene where the error is small, this method can The positioning effect is good, but it assumes that the data matrix is ​​accurate, that is, the error only exists in the measured value matrix. Therefore, the method is unstable when the error is slightly large, and it is generally only suitable for initial value calculation.

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