基于麻雀优化量子压缩感知的脉冲星定位定速方法

Pulsar Positioning and Velocimetry Method Based on Sparrow Search Algorithm-Optimized Quantum CS

针对量子-压缩感知的X射线脉冲星定位定速方法(QuantumbasedCS,QCS)量子测量矩阵尺寸过大,从而导致运行时间长的问题,提出了一种基于麻雀优化压缩感知方法(Sparrow SearchAlgorithmoptimizedQuantumCS,SSAQCS),并将其应用于脉冲星定位定速。利用麻雀优化算法对QCS中构成量子测量母矩阵的各子矩阵进行优化选择。其中,每只麻雀的位置对应一种子矩阵组合,将QCS的定位定速误差联合起来作为麻雀算法的适应度函数值,经过多次迭代得到行数少、性能优的量子测量矩阵。仿真结果表明:与QCS相比,SSA-QCS定位定速精度高、运行时间短,可实现高精度实时的X射线脉冲星定位定速联合估计。

The size of the quantum measurement matrix in the Quantum based CS(QCS)of X-ray pulsar positioning and velocimetry method is large.To reduce calculation time,a fast Quantum-CS method based on the Sparrow Search Algorithm optimization(SSA-QCS)was proposed and applied to the pulsar positioning and velocimetry.The quantum measurement mother matrix in QCS was divided into multiple sub-matrices.The quantum measurement sub-matrices were selected from the quantum measurement mother matrix through SSA.With the location of every sparrow corresponding to the combination of quantum measurement sub-matrices,with the estimation errors of the positioning and velocimetry in QCS as the object of the fitness function,through iterations,the optimal combination of the quantum measurement sub-matrices was obtained,forming a smallsized and high-performance quantum measurement matrix.Simulation results show that the SSA-QCS has a lower calculation cost and higher accuracy compared with the QCS.SSA-QCS can reach high-accuracy and real-time X-ray pulsar positioning and velocimetry.