• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.2
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• pp.131-138
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• 2016

In this paper, we studied the IRF performances of the chirp signal used in the SAR system. The most important factors that degrade IRF performances are amplitude and phase errors. Each factor can be represented to linear, quadratic, random and ripple terms. That can be extracted by a quadratic polynomial curve fitting of chirp waveform. We analyzed the IRF performances by the error terms and supposed the minimum value of RF non-linearity to meet the specification of the PSLR and ISLR.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.407-411
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• 2002

Ternary sequences are digital codes consisting of discrete values -1, 0, and 1 only. They are advantageous in that the correlation can be carried out using additions only. Also, they feature an ideal circular autocorrelation function, but in channel characterization tasks, the usual requirement is that the linear autocorrelation function be ideal, i.e., a Kronecker delta function. In this article, we consider two approaches to improving their linear autocorrelation or crosscorrelation properties: one is an inverse filtering method with thresholding, and the other is a singular value decomposition (SVD) method. Both methods are simulated under noisy circumstances. The inverse filtering method resulted in an improvement in peak sidelobe level of about 11 dB at an SNR of 30 dB, and the SVD method showed similar performances, albeit more sensitive to noise depending on the singular value selection strategy.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.407-411
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• 2002

Ternary sequences are digital codes consisting of discrete values -1, 0, and 1 only. They are advantageous in that the correlation can be carried out using additions only Also, they feature an ideal circular autocorrelation function, but in channel characterization tasks, the usual requirement is that the linear autocorrelation function be ideal, i.e., a Kronecker delta function. In this article, we consider two approaches to improving their linear autocorrelation or crosscorrelation properties: one is an inverse filtering method with theresholding and the other is a singular value decomposition (SVD) method. Both methods are simulated under noisy circumstances. The inverse filtering method resulted in an improvement in peak sidelobe level of about 1㏈ at an SNR of 30㏈, and the SVD method showed similar performances, albeit more sensitive to noise depending on the singular value selection strategy.