A UWB Localization Method Based on Information Sharing Harris Eagle Optimization

Abstract

The present invention relates to an ultra-wideband positioning method based on information sharing Harris Eagle optimization. Optimal individual; Step 4: Information sharing update; Step 5: Calculate the escape energy by the sine and cosine disturbance term escape energy calculation method, and judge |E|>1, if it is true, go to Step 6, otherwise go to Step 7; Step 6: Search phase Search and update, and then perform step 8; step 7: development phase, perform development update; step 8: calculate fitness, greedy mechanism selects the optimal individual to update the prey position. The invention improves on the existing Harris Eagle optimization ultra-wideband positioning method, the method introduces an information sharing mechanism to increase population diversity, improves the calculation method of escape energy, balances the search and development stages, and effectively improves the node positioning accuracy .

Description

technical field [0001] The invention relates to the technical field of wireless positioning, in particular to an ultra-wideband positioning method based on Harris Eagle optimization of information sharing. Background technique [0002] Ultra Wide Band (UWB) technology appeared in the 1960s. At that time, it was mainly used in the military field due to its low power and good concealment. Technology has developed rapidly and has been continuously applied in the civil field, especially the positioning of wireless networks. [0003] The positioning method is very important in positioning. The node positioning method is to estimate the position of the unknown node through communication with other nodes based on the reference node with known position information. The existing positioning methods can be roughly divided into two categories: range-based and range-free. The positioning algorithm based on ranging is performed by measuring the actual distance between nodes, and the po...

AI Technical Summary

Problems solved by technology

[0004] Due to the high time resolution and high positioning accuracy of UWB signals, it is suitable for time-based positioning methods. Time-based algorithms are a typical ranging-based positioning method. The basic idea is to represent the distance from an unknown node to a reference node The signal propagation time is multiplied by the speed of light. Under ideal circumstances, the positioning accuracy of the time-based algorithm is very high, but the actual environment is complex, and the delay and non-line-of-sight error in the signal propagation process will have a great impact on positioning, resulting in inaccurate positioning results.

[0005] In practical applications, ultra-wideband positioning is mostly used in complex indoor environments. The monitoring area usually has obstacles that affect wireless signal transmission such as walls, tables, and doors. The obstruction of ultra-wideband signals by obstacles will shorten the ranging time. Too long will cause errors in the distance between nodes. This error has a great impact on the positioning accuracy of traditional positioning algorithms. At present, many scientific researchers use intelligent optimization algorithms such as particle swarms to improve positioning accuracy.

Harris Hawks optimization algorithm (Harris Hawks optimization, HHO) is a new type of bionic intelligence optimization algorithm proposed by Heidari et al. in 2019 inspired by the living habits of Harris Hawks. The algorithm was published in a title of Future generation computersystems journal In the article for Harris hawks optimization: Algorithm and applications, the algorithm uses gradient-free search, which has a strong global search capability and requires few parameters to be adjusted. It has been applied in many fields, but in the application of positioning field, the traditional Harris The eagle algorithm still has certain limitations. The algorithm does not fully simulate the characteristics of the Harris eagle. In the search phase, the Harris eagle perches on the tree to find prey through two strategies. Although these two exploration strategies improve the diversity of the population , but did not make full use of the information sharing ability between the Harris Eagles, the algorithm search ability is not high, and it is easy to fall into the local optimal solution. At the same time, the algorithm can only enter the development stage in the later stage of iteration, which reduces the search ability and convergence speed of the algorithm. The positioning accuracy of the algorithm is limited

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