Proc. SPIE. 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis
KEYWORDS: Signal to noise ratio, Fourier transforms, Computer simulations, Signal processing, Fractional fourier transform, Automatic target recognition, Defense technologies, Signal detection, Commercial off the shelf technology, Time-frequency analysis
The fractional Fourier transform (FRFT), which is a generalization of the classical Fourier transform (FT), plays an important role in many areas of signal processing and optics. Many properties of this transform are well known. In the field of signal processing, the chirp signal has a good energy concentration in the fractional Fourier domain (FRFD) by choosing an appropriate fractional order, but the study of the fractional energy spectrum integral (FESI) is still missing. The purpose of this paper is to derive the FESI of the FRFT of chirp signal, from which an important property of the chirp signal’s FRFT is discovered that the FESI reaches the valley value at the rotation angle where the FRFT reaches the peak value, and this provides a new approach to detect and estimate the parameter of the chirp signal.
To solve the problem of detecting the space-borne radar linear frequency modulated (LFM) pulse signal contaminated by spurious clutter and interference under a low signal-to-noise ratio (SNR), a detection and parameter estimation algorithm based on the time-frequency image enhancement and Hough transform (HT) is proposed. First, short-time Fourier transform (STFT) is carried out on the space-borne radar LFM pulse signal with the Gaussian window to acquire the time-frequency spectrum; the spectrum is then converted into the time-frequency image. Second, in order to observe the weak signal’s details from the image, contrast stretching is implemented on the time-frequency image and the trailing induced by the spurious clutter is eliminated to strengthen the chirp line of the LFM signal. Third, the line is detected through HT on the enhanced time-frequency image, and the coordinates and gray values of the pixels passed through by the line are extracted in the time-frequency image; then these gray values are filtered by the median filter before being binarized with the gray threshold; the anti-pulse-splitting mechanism is adopted to determine the start and the end of the chirp line segment. Measured data experiments show that the method can effectively detect the space-borne radar LFM pulse signal under a low SNR environment, determine the pulse’s arrival time and end time, estimate its parameters, and is superior to direct STFT-Radon transform and direct STFT-HT.
Proc. SPIE. 9252, Millimetre Wave and Terahertz Sensors and Technology VII
KEYWORDS: Radar, Signal to noise ratio, Detection and tracking algorithms, Fourier transforms, Interference (communication), Monte Carlo methods, Frequency modulation, Fermium, Signal detection, Time-frequency analysis
Aiming at solving the problem of detecting the wideband chirp signals under low Signal-to-Noise Ratio (SNR)
condition, an effective signal detection algorithm based on Short-Time-Fourier-Transform (STFT) is proposed.
Considering the characteristic of dispersion of noise spectrum and concentration of chirp spectrum, STFT is performed
on chirp signals with Gauss window by fixed step, and these frequencies of peak spectrum obtained from every STFT are
in correspondence to the time of every stepped window. Then, the frequencies are binarized and the approach similar to
mnk method in time domain is used to detect the chirp pulse signal and determine the coarse starting time and ending
time. Finally, the data segments, where the former starting time and ending time locate, are subdivided into many
segments evenly, on which the STFT is implemented respectively. By that, the precise starting and ending time are
attained. Simulations shows that when the SNR is higher than -28dB, the detection probability is not less than 99% and
false alarm probability is zero, and also good estimation accuracy of starting and ending time is acquired. The algorithm
is easy to realize and surpasses FFT in computation when the width of STFT window and step length are selected
properly, so the presented algorithm has good engineering value.