Target detection in forward scattering radar

This paper analyses electromagnetic signal scattered from the target crossing the Forward Scattering Radar (FSR) system baseline. The aim of the analysis was to extract the Doppler signal of a target under the influence of high ground clutter and noise interference. The extraction was used for the automatic target detection (ATD) in the FSR system. Two extraction methods, namely Hilbert Transform and Wavelet Technique, were analyzed. The detection using the Hilbert Transform is only applicable for some conditions; however, the detection using the Wavelet Technique is more robust to any clutter and noise level. From 55 sets of signal, only 4% of false alarm was detected or occurred when the Wavelet Technique was applied as a detection scheme. Two sets of field experimentation were carried out and the target's signal under the influence of high clutter had successfully been detected using the proposed method

Ground Target Detection in Forward Scattering Radar Using Hilbert Transform and Wavelet Techniques

This thesis analyzed the electromagnetic signal scattered from the target crossing the Forward Scattering Radar system baseline. The aim of the analysis was to extract the Doppler signal of the target, under the influence of high ground clutter and noise interference. The scattered Doppler signal was processed by the proposed signal processing techniques to predict the existence of a target for the automatic target detection (ATD) in the FSR system. This thesis is dedicated to the detection of ground target, and for this purpose, a typical car was used as target. Two signal processing techniques, namely Hilbert Transform and Wavelet Technique, were used for target detection. The results gathered in this study showed that the detection using Hilbert Transform was only applicable for some conditions and it was used to confirm the wavelet efficiency in the detection process. Similarly, it was also found that the detection using Wavelet Technique became more robust to higher clutter and noise level. At the worst condition of the scenario, the successful detection rate is more than 75%. This good result suggest that the transmit signal can be as low as possible and open a new horizons for FSR to be applied in real applications for example in Radar Sensor Network and Microwave Fence .Two sets of field experimentations were carried out, and the target’s signal under the influence of the high clutter was successfully detected using the proposed method. Finally, an algorithm for an automatic detection of the ground target detection in FSR is proposed

Analyzing laser-plasma interferograms with a Continuous Wavelet Transform Ridge Extraction technique: the method

Laser-plasma interferograms are currently analyzed by extracting the phase-shift map with FFT techniques (K.A.Nugent, Applied Optics {\bf 18}, 3101 (1985)). This methodology works well when interferograms are only marginally affected by noise and reduction of fringe visibility, but it can fail in producing accurate phase-shifts maps when dealing with low-quality images. In this paper we will present a novel procedure for the phase-shift map computation which makes an extensive use of the Ridge Extraction in the Continuous Wavelet Transform (CWT) framework. The CWT tool is {\it flexible} because of the wide adaptability of the analyzing basis and it can be very {\it accurate} because of the intrinsic noise reduction in the Ridge Extraction. A comparative analysis of the accuracy performances of the new tool and the FFT-based one shows that the CWT-based tool phase maps are considerably less noisy and it can better resolve local inhomogeneties

TRUFAS, a wavelet based algorithm for the rapid detection of planetary transits

Aims: We describe a fast, robust and automatic detection algorithm, TRUFAS, and apply it to data that are being expected from the CoRoT mission. Methods: The procedure proposed for the detection of planetary transits in light curves works in two steps: 1) a continuous wavelet transformation of the detrended light curve with posterior selection of the optimum scale for transit detection, and 2) a period search in that selected wavelet transformation. The detrending of the light curves are based on Fourier filtering or a discrete wavelet transformation. TRUFAS requires the presence of at least 3 transit events in the data. Results: The proposed algorithm is shown to identify reliably and quickly the transits that had been included in a standard set of 999 light curves that simulate CoRoT data. Variations in the pre-processing of the light curves and in the selection of the scale of the wavelet transform have only little effect on TRUFAS' results. Conclusions: TRUFAS is a robust and quick transit detection algorithm, especially well suited for the analysis of very large volumes of data from space or ground-based experiments, with long enough durations for the target-planets to produce multiple transit events.Comment: 9 pages, 10 figures, accepted by A&