ML iterative superresolution approach for real-beam radar
Yin Zhang, Yongchao Zhang, Yulin Huang, Jianyu Yang, Yuebo Zha, Junjie Wu, Haiguang Yang · 2014
The high azimuth angular resolution problem of real-beam scanning radar is significant to targets detection and location. ML iterative adaptive approach(ML-IAA) has been used in array signal processing to realize high angular resolution. In order to improve the azimuth angular resolution of real-beam scanning radar, we introduce this algorithm to real-beam radar system, called real-beam ML iterative superresolution approach(RML-ISA). This method established the likelihood function by utilizing the statistical property of real beam data. Applying this method to the real-beam radar system only needs few scanning echo to obtain effective results. Simulations illustrate the performance of our algorithm.