• The Journal of Korean Association of Computer Education
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• v.6 no.3
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• pp.47-56
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• 2003

We describe a new method for removing non-linear phased array antenna aberration called "squint" problem. To develop a compensation scheme. theoretical antenna and artificial neural networks were used. The purpose of using the artificial neural networks is to develop an antenna system model that represents the steering function of an actual array. The artificial neural networks are also used to implement an inverse model which when concatenated with the antenna or antenna model will correct the "squint" problem. Combining the actual steering function and the inverse model contained in the artificial neural network, alters the steering command to the antenna so that the antenna will point to the desired position instead of squinting. The use of an artificial neural network provides a method of producing a non-linear system that can correct antenna performance. This paper demonstrates the feasibility of generating an inverse steering algorithm with artificial neural networks.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.11
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• pp.1141-1144
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• 2011

Range walk has been a major problem in achieving correct Doppler processing. This frequently occurs when range variation is severe just like in a bistatic radar or in high speed target scenario. This paper presents a range walk compensated range-Doppler processing algorithm applicable to the bistatic radar. In order for the compensation, a range-domain interpolation is applied for range compressed signal so that Doppler processing is performed along the evenly time-spaced range bins that contain target returns. Under a bistatic radar scenario, the proposed algorithm including a range domain pulse compression is mathematically described. Finally, the validity of the algorithm is demonstrated by simulation results showing the superiority of a SCDP(Squint Cross-range Doppler Processing) over an uncompensated Doppler processing.