RSS-based two-dimensional wireless sensor network semi-definite programming positioning algorithm

Abstract

The invention relates to an RSS (Received Signal Strength)-based two-dimensional wireless sensor network semi-definite programming positioning algorithm which comprises the following steps: supposing that no non-line-of-sight communication exists, completing measurement of a signal energy parameter by establishing an energy signal transmission model represented by the following formula; under a condition that energy signal measurement noise complies with Gaussian probability distribution, converting a wireless sensor network target node positioning problem into an MLE (Maximum Likelihood Estimation) mathematical optimization problem for solving; establishing an optimal target function; and converting the optimization problem into a constrained optimization problem by introducing a redundant variable, and then further converting the obtained constrained optimization problem into an SDP (Semi-Definite Programming) convex optimization problem for solving by applying an SDR (Semi-Definite Relaxation) method. The algorithm disclosed by the invention can improve the positioning precision of a target node.

Description

technical field [0001] The invention belongs to the field of wireless sensor network positioning, and relates to the application of semi-definite programming (SDP) convex optimization method in the positioning of two-dimensional wireless sensor network target nodes. Background technique [0002] In recent years, wireless sensor networks (WSNs) have been rapidly developed in various fields such as object tracking, intrusion detection, energy-efficient routing, underground and underwater surveying, and so on. In wireless sensor networks, sensor nodes are usually arranged in a certain area to obtain relevant data such as environmental temperature, pressure, humidity, wind speed, etc. These data and target node location information are helpful for the design of intelligent systems. Therefore, in wireless sensor networks, the acquisition of node location information is very important. [0003] Wireless sensor network (WSNs) positioning can be divided into ranging-based positioni...

AI Technical Summary

Problems solved by technology

In order to solve the MLE optimization problem, the DFP method proposed in [8] can be used, or the traditional Newton iterative method can be used to solve the problem. However, due to the high nonlinearity and non-convexity of the objective function of MLE, the iterative method can solve Not only will the positioning error accumulate, but also it is easy to converge to the local optimal solution, and the global optimal solution of the problem cannot be obtained

In order to solve this problem, Z.Ding et al. transformed the MLE optimization problem into a semidefinite programming (SDP) problem by applying the minimization maxima criterion and the semidefinite relaxation technique (SDR). This method can improve the positioning accuracy, but its positioning robustness is poor; in order to improve the robustness of target node positioning, SlavisaTomic et al. approximated the MLE problem, and then transformed it into a second-order cone programming (SOCP) problem for solution. However, when When the target node is outside the convex set formed by the reference nodes, the localization performance of the algorithm degrades

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