A Genetic-Algorithm-Based Sparse Planar Array Optimization Method for 77 GHz High-Resolution Imaging

In this paper, a sparse planar array optimization method based on genetic algorithm is proposed for 77 GHz high-resolution imaging. The large scale half-wavelength uniform planar array is divided into multiple symmetric sparse subarrays, and the 2-D sidelobe of the sparse subarray is suppressed by the genetic based optimization approach, which reduces the number of array elements while remains the desired azimuth resolution and elevation resolution. The performance of the proposed method is verified by several simulation experiments. The results show that the optimized arrays can achieve an azimuth resolution of 0.7° and an elevation resolution of 2.3° in a field of view (±45°, ±45°), while the normalized sidelobe magnitudes in these sparse array radiation patterns are below -10 dB, which is suitable for high-resolution, wide field of view, and low-cost 77 GHz radar imaging applications.