• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.75-86
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• 2010

As the performance of the spaceborne SAR has been dramatically enhanced and demonstrated through advanced missions such as TerraSAR and LRO(Lunar Reconnaissance Orbiter), the need for highly sophisticated and efficient SAR processor is also highlighted. In Korea, the activity of SAR researches has been mainly concerned with SAR image applications and the current SAR raw data studies are mostly limited to stripmap mode cases. The first Korean spaceborne SAR is scheduled to be operational from 2010 and expected to deliver vast amount of SAR raw data acquired from multiple operational scenarios including ScanSAR mode. Hence there will be an increasing demand to implement ground processing systems that enable to analyze the acquired ScanSAR data and generate corresponding images. In this paper, we have developed an efficient ScanSAR processor that can be directly applied to spaceborne ScanSAR mode data. The SPECAN(Spectrum Analysis) algorithm is employed for this purpose and its performance is verified through RADARSAT-1 ScanSAR raw data taken over Korean peninsular. An efficient quick-look processing is carried out to produce a wide-swath SAR image and compared with the conventional RDA processing case.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.91-103
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• 2022

Determination of sound source characteristics such as: sound volume, direction and distance to the source is one of the important techniques for unmanned systems like autonomous vehicles, robot systems and AI speakers. There are multiple methods of determining the direction and distance to the sound source, e.g., using a radar, a rider, an ultrasonic wave and a RF signal with a sound. These methods require the transmission of signals and cannot accurately identify sound sources generated in the obstructed region due to obstacles. In this paper, we have implemented and evaluated a method of detecting and identifying the sound in the audible frequency band by a method of recognizing the volume, direction, and distance to the sound source that is generated in the periphery including the invisible region. A cross-shaped based sound source recognition algorithm, which is mainly used for identifying a sound source, can measure the volume and locate the direction of the sound source, but the method has a problem with "blind spots". In addition, a serious limitation for this type of algorithm is lack of capability to determine the distance to the sound source. In order to overcome the limitations of this existing method, we propose a QRAS-based algorithm that uses rectangular-shaped technology. This method can determine the volume, direction, and distance to the sound source, which is an improvement over the cross-shaped based algorithm. The QRAS-based algorithm for the OSSD uses 6 AITDs derived from four microphones which are deployed in a rectangular-shaped configuration. The QRAS-based algorithm can solve existing problems of the cross-shaped based algorithms like blind spots, and it can determine the distance to the sound source. Experiments have demonstrated that the proposed QRAS-based algorithm for OSSD can reliably determine sound volume along with direction and distance to the sound source, which avoiding blind spots.