Details

Autor(en) / Beteiligte

• Yuebo Zha
• Yulin Huang
• Jianyu Yang
• Junjie Wu
• Yin Zhang
• Haiguang Yang

Titel

An improved Richardson-Lucy algorithm for radar angular super-resolution

Ist Teil von

• 2014 IEEE Radar Conference, 2014, p.0406-0410

Ort / Verlag

IEEE

Erscheinungsjahr

2014

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• The traditional Richardson-Lucy (R-L) algorithm has a strong ability to realize super-resolution. However, it always suffers from noise amplification. In this paper, an improved R-L algorithm is proposed to solve the real-beam scanning radar angular super-resolution problem, which relies on both the traditional R-L deconvolution algorithm and the regularization term. We first describe the angular super-resolution problem as a deconvolution task and formulate our improved R-L decon-volution algorithm from a Bayesian framework. We then solve the angular super-resolution problem in Bayesian framework using the improved R-L algorithm, which lead to the fixed-point iterative method. Experimental results with synthetic data illustrate that the performance of proposed algorithm is better than conventional R-L algorithm.