Linear Sparse Array Synthesis With Minimum Number of Sensors

The number of sensors employed in an array affects the array performance, computational load, and cost. Consequently, the minimization of the number of sensors is of great importance in practice. However, relatively fewer research works have been reported on the later. In this paper, a novel optimization method is proposed to address this issue. In the proposed method, the improved genetic algorithm that has been presented at a conference recently, is used to optimize the weight coefficients and sensor positions of the array. Sensors that contribute the least to the array performance are then removed systematically until the smallest acceptable number of sensors is obtained. Specifically, this paper reports the study on the relationship between the peak sidelobe level and the sensor weights, and uses the later to select the sensors to be removed. Through this approach, the desired beam pattern can be synthesized using the smallest number of sensors efficiently. Numerical results show that the proposed sensor removal method is able to achieve good sidelobe suppression with a smaller number of sensors compared to other existing algorithms. The computational load required by our proposed approach is about one order less than that required by other existing algorithms too.