A wireless network location technology based on particle-assisted random search

Abstract

The invention discloses a wireless network positioning technology based on particle-assisted random search, which is used for the position related to the position of the positioned target established after receiving received signal energy parameters and coordinate position parameters from various reference nodes for the positioned target , and optimize the overall positioning objective function used to locate its own position, so as to improve the positioning accuracy. The algorithm of the invention can prevent the particle search from falling into the local optimal solution, and can still find the global optimal solution when the initial coverage of the search particles does not cover the global optimal solution. When the statistical information of the system is known, the uncertainty of the reference signal or the prior information of the system parameters can be included; at the same time, the reputation of the search particle and the detection particle is equivalent to the probability measure, and the global optimal solution found is Statistical mean square error minimum solution. The use of importance sampling particles can resist strong noise interference and distortion of nonlinear observation function, which makes the search technique more robust. Suitable for optimization of non-convex and non-concave objective functions under any criterion.

Description

technical field [0001] The invention relates to wireless network positioning, in particular to an optimization method of a non-convex and non-concave objective function under nonlinear non-Gaussian conditions in wireless positioning. Background technique [0002] The optimization of non-convex and non-concave objective functions is a very important and open problem in the fields of wireless network positioning, statistical signal processing, wireless mobile communication, computer science and even basic mathematics. In the field of signal and communication applications, many problems can be transformed into parameter estimation and optimization problems, such as signal detection, time-varying channel estimation, frequency offset estimation, sparse signal reconstruction, adaptive filtering, decoding, wireless positioning and tracking Wait. However, due to the existence of non-ideal system factors (such as nonlinearity of system functions, Gaussian / non-Gaussian environmental ...

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Problems solved by technology

Similarly, T.Stoyanova et al adopted a similar idea in the 2014 document "RSS-based localization for wireless sensor networks in practice." Proc.of 2014 9th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2014, however Since the local search of its search particles only depends on the historical trajectory of its search particles, when the initial distribution of search particles does not cover the global optimum, its algorithm can only converge to the local optimum of the initial coverage, not the global optimum. best

[0008] A comprehensive analysis of the current research results around the optimization of complex non-convex and non-concave objective functions and parameter estimation problems at home and abroad, there are a lot of results that can be used for reference; however, most of the current research results are only for a certain problem in a specific situation Solve the influence of system nonlinearity, reference uncertainty, non-Gaussian interference, etc. on the optimization problem, and then did not propose a systematic and reliable framework for solving the problem, in order to expect to search for the global optimal solution, and give the target variable Optimal estimation for improved positioning accuracy and reliability

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