Method for removing interference noise from moving object locating in wireless sensor network

Abstract

The invention relates to a method for removing interference noise from moving object locating in a wireless sensor network (WSN), providing a prerequisite foundation to the design of a wave filter capable of accurately and quickly locating the actual position of a moving object in the environment that the wireless sensor network is interfered by noise, and according to the requirements of the wireless sensor network to the locating precision of the moving object, a UKF wave filtering algorithm is applied to partial particles in a UPF algorithm so as to obtain a better importance function. Foroccasions requiring high precision, more particles of the UKF wave filtering algorithm are applied, but for occasions requiring relatively low precision, fewer particles of the UKF wave filtering algorithm are applied, thereby providing the prerequisite foundation to the design of the wave filter capable of accurately and quickly locating the actual position of the moving object in the environment that the wireless sensor network is interfered by the noise.

Description

Interference Noise Denoising Method for Moving Target Location in Wireless Sensor Networks technical field The invention relates to an interference noise denoising method for moving target positioning in a wireless sensor network (WSN), which is a filter used for the wireless sensor network to accurately and quickly locate the actual position of a moving target in an environment disturbed by noise. The device design provides a premise basis, and belongs to the technical field of digital signal processing applied in wireless sensor networks. Background technique Particle Filter (PF) algorithm and Unscented Kalman Filter (UKF) algorithm are effective measures to restore the original signal in the signal disturbed by noise. The key to the PF algorithm is to choose a reasonable importance sampling function. The common selection method is to use the prior probability distribution of the state as the importance sampling function. It largely depends on the system model. If the m...

AI Technical Summary

Problems solved by technology

Technical problem: In order to consider the influence of the current measured value and prior probability on the posterior probability distribution and reduce the amount of calculation, the present invention proposes a method for denoising interference noise for moving target positioning in a wireless sensor network. accuracy while reducing the amount of calculation, thereby improving the real-time performance of the filtering process

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