Wireless network positioning technology based on particle auxiliary random search

Abstract

The invention discloses a wireless network positioning technology based on particle auxiliary random search, and is used for performing optimization processing on a positioning general objective function which is established after the positioned targets receive receiving signal energy parameters from all reference nodes and coordinate position parameters thereof, correlated to the position of the positioned targets and used for positioning location of the positioned targets so that positioning precision can be enhanced. Particle search can be prevented from being trapped in a local optimal solution. When initial coverage range of search particles does not cover a global optimal solution, the global optimal solution can still be found. When statistical information of the system is known, uncertainty of a reference signal or prior information of the system parameters can be included; meanwhile, the credibility of the search particles and detection particles is equivalent to probability measure, and the found global optimal solution is the mean square error minimal solution in the statistical sense. High-noise interference and distortion of a non-linear observation function can be resisted by using importance sampling particles so that the search technology is enabled to be more robust. The wireless network positioning technology based on the particle auxiliary random search is suitable for optimization of a non-convex non-concave objective function under any criteria.

Description

Technical field [0001] The present invention relates to wireless network positioning, and particularly relates to an optimization method for non-convex and non-concave objective functions under nonlinear and 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 communications, 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 non-linear system function, Gaussian / non-Gaussian e...

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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 network in practice." Proc. of 20149th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2014. However, since the local search of its search particles only depends on its search particle Therefore, when the initial distribution of search particles does not cover the global optimum, the algorithm can only converge to the local optimum of the initial coverage, not the global optimum

[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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