Research On Narrow-band Microwave Indoor Positioning Technology In UHF Band

The demand for indoor high precision positioning has increased rapidly in many public services.In particular,centimeter-level precision positioning technology based on microwave transmission has been sought for the past years.However,current indoor positioning systems with either short-range positioning technology based on wireless local area networks(WLAN)and ZigBee achieving meter-level accuracy,or ultra-wide band(UWB)and 60 GHz communication technology achieving high precision but with high cost required,could not meet the need of indoor wireless positioning.The positioning technology is often used to calculate the distance between the base station and the transmitter,use triangulation algorithm to determine the location of the transmitter.Higher accuracy of distance measurement results in higher accuracy of positioning.In microwave transmission,the distance can be obtained according to the transmission time,so the positioning accuracy can be determined by the time measurement.Spread spectrum technique is often employed to achieve high precision positioning by high accuracy of timing measurement and anti-jam characteristics,and the time precision is related to the symbol width,the narrower the symbol width,the higher the accuracy.This can lead to very high sampling rate and high hardware cost,and make the implementation of positioning system difficult and limit its application.As phase measurement is more sensitive to the distance of microwave transmission than timing measurement,indoor positioning system can measure coherent phase by autocorrelation of received spread spectrum signals from base stations,to accurately calculate the time difference of arrival.The sampling rate required here can be low thus leading to low implementation cost.So,we propose wavelet autocorrelation phase measurement of spread spectrum signal transmitted by carrier frequency lower than 1 GHz with low cost requirement to achieve high precision.However,by the experiment,it is found that noise has a great influence on accuracy,so it is necessary to denoise the received signal.However,current filtering methods in the time or frequency domain can only filter high frequency band noise,making the measurement accuracy low.In this thesis,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 timefrequency 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.