Drone Signal TDOA Estimation Algorithm Based on an Improved Wavelet Threshold Denoising Technique

In the passive detection and positioning technology of UAV based on the principle of time difference of arrival (TDOA), the existing time-difference estimation method which relies on signal cross-correlation puts forward high requirements for waveform correlation of received signals. Noise interference in the received signal will lead to a large error in time delay estimation, which will seriously affect the positioning accuracy. To solve this problem, an improved wavelet threshold estimation method for UAV signal arrival time difference is proposed in this paper. In order to solve the problem of signal correlation and noise in traditional TDOA estimation method, wavelet denoising technology is used to pre-process the received signal and improve the accuracy of TDOA estimation. In addition, a new soft threshold function is introduced, which is improved on the traditional threshold function, reduces the oscillation of the hard threshold function and the constant error of the soft threshold function, while maintaining the local characteristics of the signal. The effectiveness of the proposed method is verified by simulation and measured data. The results show that the improved wavelet threshold denoising method has better denoising performance and significantly improves the stability and accuracy of TDOA estimation.