Method for iteratively positioning sound source target of wireless sensor network

Abstract

The invention discloses a method for iteratively positioning a sound source target of a wireless sensor network. According to the method, a plurality of wireless sonic sensor node modules and collecting nodes are adopted to construct a target positioning wireless sensor network, on the basis of target sound signal strength data received by nodes of a sonic sensor, by utilizing a particle filter to iteratively estimate the probability distribution of a target position parameter, and estimation of a target position is obtained. According to the method, by the adoption of an auxiliary particle filter combined with a gauss mixed kernel smoothing method, iterative estimation is carried out on the target position, the collecting nodes only process observing data of one sensor node each time, thus the bandwidth limitation and the frequency restriction of the wireless sensor network are broken through, and energy consumption of a network is lowered. By the adoption of the method, higher target positioning precision is obtained via integration of multi-sensor information, and the requirement on timeliness of target positioning can be satisfied under physical restrictions of energy, bandwidth and the like of a sensor network.

Description

technical field [0001] The invention relates to the technical field of wireless sensor networks, in particular to a new wireless sensor network iterative sound source target positioning method. Background technique [0002] Wireless sensor networks are composed of low-cost densely deployed sensor nodes, which integrate perception and wireless communication. The application fields of wireless sensor network target location include battlefield surveillance, logistics management, security and so on. At present, many target location methods have been proposed at home and abroad. [0003] Internationally, X.Sheng et al. proposed the maximum likelihood sound source localization algorithm under the wireless sensor network, but this method requires a lot of network communication and calculation, because the sink node only has limited bandwidth and energy, and the maximum likelihood estimation Numerical algorithms are difficult to converge, so this method is not feasible in practice...

AI Technical Summary

Problems solved by technology

[0005] The common disadvantages of the above-mentioned existing methods are: large amount of computation, serious degradation of the positioning algorithm, resulting in large positioning errors and inaccurate positioning

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