Variable Doppler Starting Point Keystone Transform for Radar Maneuvering Target Detection

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 12; p. 2129
• Main Authors: Jia, Wei, Feng, Yuan, Qiao, Xingshuai, Wang, Tianrun, Shan, Tao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Ambiguity; Boundaries; Compensation; Complexity; Computer applications; Doppler radar; Efficiency; Fourier transforms; Frequency ranges; Interpolation; keystone transform (KT); maneuvering target detection; Maneuvering targets; methodology; Performance degradation; radar; Radar detection; range migration (RM); remote sensing; Signal processing; sinc interpolation; Target detection; variable Doppler starting point keystone transform (VDSPKT); Velocity
• DOI: 10.3390/rs16122129
• ISSN: 2072-4292

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address the aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving computational efficiency. Finally, the simulation and real data experiments demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.