• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.542-545
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• 2005

Spaceborne radar interferometry has been widely used to estimate the topography and deformation of the Earth. It is difficult to obtain coherent interferometric SAR pairs especially over coastal areas mainly because of variation of surface conditions. We carried out the experiment using a cross-interferometric pair with a perpendicular baseline of about 1.4 km, a 30 minutes temporal separation and the height sensitivity of about 6 meters. The temporal decorrelation can be reduced by the cross interferometric technique with a 30 minutes temporal separation. Accurate coregistration was performed through resampling of ENVISAT ASAR data to equivalent pixel spacing to the ERS SAR data, because of the differences of the pulse repetition frequency and range sampling rate between the two sensors. Then we estimated range and azimuth offset to a sub-pixel accuracy using image intensity cross correlation. A larger window chip size than a general case was used because it was difficult to distinguish typical features. As range bin increased, the difference of Doppler centroid also increased. It resulted in lower coherence in far range than in near range. Coherences over wetland in near and far range were about 0.8 and 0.5, respectively. The coherence was improved by applying azimuth and range common band filtering, but coherence gap still existed. ERS-ENVISAT cross-interferogram usually lost information in urban area. However, high coherence over a city in this pair was shown, because of less man-made structures than other major cities. Accuracy of the DEM constructed by the ERS-ENVISAT 30-minute pair in a coastal area is to be evaluated.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.389-398
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• 2019

In this paper, we propose a method to detect small unmanned aerial vehicles(UAVs) flying in a real-world environment. Small UAV signals are frequently obscured by clutter signals because UAVs usually fly at low altitudes over urban or mountainous terrain. Therefore, to obtain a desirable detection performance, clutter signals must be considered in addition to noise, and thus, a performance analysis of each clutter removal technique is required. The proposed detection process uses clutter removal and pulse integration methods to suppress clutter and noise signals, and then detects small UAVs by utilizing a constant false alarm rate detector. After applying three clutter removal techniques, we analyzed the performance of each technique in detecting small UAVs. Based on experimental data acquired in a real-world outdoor environment, we found it was possible to derive a clutter removal method suitable for the detection of small UAVs.