Ultra-Quick pulsar positioning and velocimetry method based on GA-optimized quantum CS

The size of the measurement matrix in compressive sensing (CS) is proportional to calculation cost. To reduce the calculation cost, we utilize genetic algorithm (GA) to decrease the size of the measurement mother matrix, and propose an ultra-quick pulsar positioning and velocimetry method based on GA-optimized quantum CS (GQCS). There are two properties of the quantum measurement matrix. One is that the performance of the quantum measurement sub-matrices is different. The other one is that the combination of the quantum measurement sub-matrices affects the performance of the positioning and velocimetry method. Considering the randomness of the quantum measurement matrix, we select the quantum measurement sub-matrices from the quantum measurement mother matrix through GA. The quantum measurement matrix is divided into multiple sub-matrices. One gene in the chromosome determines whether the corresponding sub-matrix is retained or abandoned. The object of the fitness function is the estimated estimation errors of the quantum-based CS pulsar positioning and velocimetry (QCS). Through iteration, GA provides the sub-optimal combination of the quantum measurement sub-matrices, forming a small-sized and high-performance quantum measurement matrix. Simulation results indicate that the GQCS has a lower calculation cost and higher accuracy compared with the QCS.