中文摘要：

为了高效、准确地估计多普勒频率和波达方向（DOA），提出了基于加权子空间拟合（WSF）算法和量子粒子群优化（QPSO）算法的WSF—QPSO联合谱估计方法。首先，利用状态空间模型构造包含多普勒频率和DOA信息的广义可观测矩阵；用WSF算法拟合联合谱函数，将参数估计问题转化为多维非线性函数优化问题。然后，利用QPSO算法优化联合谱函数，得到多普勒频率和DOA的估计值。实验结果表明：在信源参数比较接近的情况下，WSF—QPSO方法在信噪比为0dB时对多普勒频率和DOA估计的均方根误差仅为0．0075rad和0．25°。与其他方法相比，该方法具有估计精度高、控制参数少、鲁棒性好、参数自动配对等特点，在低信噪比和小样本条件下依然能够得到较满意的参数估计结果。

英文摘要：

To estimate Doppler frequency and Direction-of-Arrival （DOA） accurately and efficiently, a joint spectrum estimation method based on Weighted Subspace Fitting （WSF） algorithm and Quan- tum-behaved Particle Swarm Optimization （QPSO） algorithm, namely WSF-QPSO, was presented. First, an extended observability matrix containing the information of Dopplers and DOAs was con- structed by using a state-space model, and the joint spectrum function was fitted by using WSF algo- rithm. Then, the joint parameter estimation was converted to multidimensional nonlinear function op- timization. Finally, the Dopplers and DOAs were estimated by optimizing the joint spectrum function using QPSO algorithm. Experimental results indicate that the Root Medium Square Errors （RMSEs） of Dopplers and DOAs estimated from the WSF-QPSO method are 0. 007 5 rad and 0.25°, respectively when SNR is 0 dB. The proposed method can get high resolution and robust parameter estimation with less control terms, and the parameters are paired automatically. In addition, the WSF-QPSO method can obtain acceptable estimation results even under the condition of low SNR or small snapshot number in comparison with the joint spectrum estimation methods based on subspace decomposition.