Edge feature extraction-based pulse signal arrival time and pulse width estimation method

Abstract

The invention provides an edge feature extraction-based pulse signal arrival time and pulse width estimation method. The method comprises the steps of extracting a pulse signal envelope from a pulse signal sampling data sequence, and based on this, performing normalization and difference value sequence calculation on the pulse envelope; and then calculating comprehensive evaluation factors of rising and falling edges of a pulse signal, and estimating arrival time and pulse width of the pulse signal. According to the method, through evaluation factors of front and back edge features of a pulse,the front and back edge features of the pulse are fully utilized; accurate estimation of the arrival time and the pulse width of the sonar pulse signal can be realized with a relatively small calculation amount; and the method is high in engineering practicality and suitable for real-time signal processing.

Description

technical field [0001] The invention belongs to the technical field of signal processing, in particular to a pulse signal arrival time and pulse width estimation method based on edge feature extraction. Background technique [0002] The arrival time and pulse width of the pulse signal are important parameters to characterize the time-domain characteristics of the pulse signal. Estimating the arrival time and pulse width of the pulse signal containing noise is an important research topic in the field of signal processing. It is used in sonar, radar and electronic warfare And other fields have important theoretical and application value, especially in the radar and sonar signal processing occupies a more prominent position. [0003] At present, scholars at home and abroad have proposed many pulse signal arrival time and pulse width estimation methods, mainly time domain energy detection method, short time Fourier transform (Short TimeFourier Transform, STFT), segmented FFT met...

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Problems solved by technology

Among them, the time-domain energy detection method is the most widely applicable without any prior conditions, and can approach the Cramer-Rao Low Bound (CRLB) of parameter estimation under the condition of high signal-to-noise ratio, but this method requires high signal-to-noise ratio , and it is a nonlinear method, which has small signal suppression effect; the short-time Fourier transform method is simple to implement and has high robustness, but the estimation accuracy of this method depends on the time step of STFT, which is suitable for the estimation of arrival time and pulse width In occasions where the accuracy is not high; the segmented STFT method first performs FFT on the received signal segment, detects the signal and determines the segment where the starting point of the signal is located, then squares the FFT data of this segment, and then performs FFT to obtain the pole of the modulus. The large value is used as the estimated value of the arrival time, but the square operation introduces nonlinear operation, and the performance is not good when the signal-to-noise ratio is low; the Haar wavelet transform edge detection method uses the correlation detection algorithm to roughly estimate the start and end time of the signal. Under the condition of estimating the signal frequency and converting it to the baseband, Haar wavelet transform is performed on the baseband signal at a certain scale, and the peak position of the wavelet transform modulus is detected as the precise estimation of the arrival time. This method makes full use of the Haar wavelet Transformation has the ability of edge detection and locating signal mutation points, but the estimation of pulse signal arrival time and pulse width is a typical double-step edge detection problem, and the parameter estimation accuracy is very sensitive to the rationality of wavelet scale selection; autocorrelation algorithm Utilizing the time correlation difference between pulse signal and broadband noise, the purpose of suppressing noise and detecting signal is achieved through correlation calculation. It is suitable for the estimation of arrival time and pulse width of frequency modulated pulse signal, but the autocorrelation peak of single frequency pulse signal is not sharp enough. , it is difficult to detect the autocorrelation peak, and the method has poor applicability to the estimation of the arrival time and pulse width of the single-frequency pulse signal

[0004] The pulse envelope edge detection method makes full use of the transient characteristics of the pulse envelope edge. There are two deficiencies in this method: (1) directly using the Hilbert transform to extract the pulse signal envelope, there is almost no processing gain, and the signal-to-noise ratio of the pulse signal is high; (2) directly using the pulse signal envelope The first-order or second-order difference is used as an evaluation factor for the transient characteristics of the front and rear edges of the pulse signal envelope, and the front and rear edges of the pulse signal are extracted. When the pulse signal propagates through the channel, there will be multi-path or reverberation interference in the received signal, so that the pulse signal envelope When distortion occurs, this method is no longer applicable

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