Wavelet Autocorrelation and Denoising of Spread Spectrum Signal for Narrowband Wireless Indoor Positioning

Abstract

Abstract. Channel noise is a key factor that affects the measurement accuracy of narrowband wireless indoor positioning. To reduce noise effects, it is often required to effectively filter the received spread spectrum signals. However, current filtering methods in the time or frequency domain can only filter high frequency band noise, making the measurement accuracy low. In this paper, a low frequency band noise reduction algorithm based on the threshold segmentation of the rectangular window is proposed. The method is based on thresholding on time-frequency wavelet domain by variance analysis and signal despreading is achieved by wavelet autocorrelation at low frequency band after denoising with reference to locally generated spread spectrum signal without added noise at the same subband. Simulation results show that compared with other filtering methods, the proposed method by wavelet autocorrelation and denoising can eliminate the noise effects at the receiver, leading to improving the positioning accuracy significantly.