Noise variance estimation based on dual-channel phase difference for speech enhancement

In this paper, we propose a method for estimating a signal-to-noise ratio (SNR) in order to improve the performance of a dual-microphone speech enhancement algorithm. The proposed method is able to reliably estimate both a priori and a posteriori SNRs by exploring a direction-of-arrival (DOA)-based local SNR that is defined by using spatial cues obtained from dual-microphone signals. The estimated a priori and a posteriori SNRs are then incorporated into a Wiener filter. Consequently, it is shown from an objective perceptual evaluation of speech quality (PESQ) comparison and a subjective listening test that a speech enhancement algorithm employing the proposed SNR estimate outperforms those using conventional single- or dual-microphone speech enhancement algorithms such as the Wiener filter, beamformer, or phase error-based filter under different noise conditions ranging from 0 to 20 dB.     

平坦响应空域滤波器和在DOA估计中的应用

本文依据半无穷维二次优化技术提出了一个设计平坦响应空域滤波器（ＳＦ）的新方法，基本思想是使用线性泛函不等式约束夺感兴趣的空域范围内保证ＳＦ响应的平坦性，同时使输出噪声功率最小化，由于使用了硬的线性泛函约束，ＳＦ响应总可以控制在充许的误差范围之内，最后研究了基于平坦响应的信号到达方向（ＤＯＡ）估计问题，计算机模拟结果和分析表明本文提出的基于平坦响应ＳＦ的ＤＯＡ估计性能优于其它有关方法的估计性能。     

通道失配对MUSIC空间谱及分辨率的影响

本文分析了高分辨率参数估计算法MUSIC在通道频率响应不一致（通道失配）情况下的统计性能，推导了通道失配对空间谱的影响以及失配方差和分辨能力的关系式．理论表明：通道失配对算法性能有较大的影响，计算机仿真结果证明了理论的有效性．     

有效的自适应波达方向盲估计算法

本文在分析自适应信号盲分离算法渐近稳定性基础上,提出了一种有效的自适应学习算法用于波达方向盲估计。研究了算法的有界性和渐近稳定性。以渐近稳定性为前提,给出了算法中非线性函数的适当选择。为了抑制噪声和估计信源数,在算法中还增加了白化过程。仿真研究表明,算法是有效的而鲁棒的,其能够从有操声的阵元信号中估计波达方向。     

A novel high degree of freedom sparse array with displaced multistage cascade subarrays

The coprime array is a recently developed sparse array that is widely used in direction-of-arrival (DOA) estimation. The degree of freedom (DOF) for existing coprime arrays relies on the virtual array model, which is limited to the array structure. Furthermore, the continuous virtual array aperture is restricted. This paper aims to propose a novel high DOF sparse array with displaced multistage cascade subarrays to further increase the length of the continuous virtual array and improve the DOF. Through cascading and separating the same sparse arrays, the continuous virtual array aperture is improved and the accuracy of the direction-of-arrival estimation is increased. It is proved that the range of the displaced distance for the maximized continuous virtual array can be obtained. The comparison between the array configuration presented in this paper and the existing common arrays are analyzed. The simulation experiments show that, compared with the uniform array, the coprime array, the nested array, and the coprime array with displaced subarrays (CADiS), the sparse array configuration proposed in this paper can significantly increase the aperture of the continuous virtual array, greatly improve the array DOF and the DOA estimation accuracy, and effectively estimate the DOA of multiple sources in underdetermined conditions.     

A sparse direction-of-arrival estimation algorithm for MIMO radar in the presence of gain-phase errors

In this paper, the problem of direction-of-arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar with gain-phase errors is addressed, by using a sparse DOA estimation algorithm with fourth-order cumulants (FOC) based error matrix estimation. Useful cumulants are designed and extracted to estimate the gain and the phase errors in the transmit array and the receive array, thus a reliable error matrix is obtained. Then the proposed algorithm reduces the gain-phase error matrix to a low dimensional one. Finally, with the updated gain-phase error matrix, the FOC-based reweighted sparse representation framework is introduced to achieve accurate DOA estimation. Thanks to the fourth-order cumulants based gain-phase error matrix estimation, and the reweighted sparse representation framework, the proposed algorithm performs well for both white and colored Gaussian noises, and provides higher angular resolution and better angle estimation performance than reduced-dimension MUSIC (RD-MUSIC), adaptive sparse representation (adaptive-SR) and ESPRIT-based algorithms. Simulation results verify the effectiveness and advantages of the proposed method.     

一种改进的波束域ESPRIT算法

高分辨方位估计技术在声纳、雷达、通信、地质探测等众多应用领域中占有十分重要的地位.ESPRIT算法作为一种成熟的高分辨算法有运算量小的优点,但其减小了阵列的有效孔径,在现有的高分辨处理设备中,由于空间的限制,阵列不可能随意增大,因此要最大限度的扩大阵列的有效孔径.通过对均匀线列阵下波束域ESPRIT算法(BmESPRIT)的研究,提出了一种基于ESPRIT的新算法,并给出了转换成波束域处理的构造方法.该算法利用后向平移技术,使两子阵列与原阵列具有相同的有效孔径.有效的利用了阵列资源.仿真结果表明在相同条件下该算法的估计性能明显提高.     

基于FFT的多个空间信号波达方向的估计算法

本文提出了一种与传统算法完全不同的多个空间窄带信号波达方向的估计算法。该算法通过对阵列输出数据的快速傅立叶变换(FFT),建立了FFT频谱与波达方向角的关系;利用这一关系并通过对FFT谱峰的搜索,从而实现了对波达方向的快速估计。计算机模拟实验验证了算法的可行性。     

Direction-of-arrival estimation for coherent GPS signals based on oblique projection

A new direction-of-arrival (DOA) estimation method for coherent global positioning system (GPS) signals is proposed. It is applicable to the GPS propagation environment with rich multipath, strong interferences and noise. According to the properties of oblique projection, interferences and noise are first suppressed and only the coherent GPS signals are retained by projecting the received signal onto the signal subspace. Then, the coherent GPS signals are resolved by the Toeplitz decorrelation scheme. Using the proposed method, interferences and noise can be suppressed simultaneously, and more accurate DOA estimation for coherent GPS signals can be obtained in GPS propagation environment. Simulation results demonstrate the effectiveness of the proposed method.     

基于贪婪算法的高分辨信号源DOA估计

确定辐射源的来波方向(DOA)是阵列信号处理的重要研究内容,已经广泛应用于雷达、声纳和无线通信等领域。本文研究了远场窄带信号源的DOA高分辨估计问题。利用信号来波方向在空域具有稀疏性的特点,建立了远场窄带信号源的稀疏表示模型。根据协方差矩阵的特征值分解和贪婪匹配追踪算法原理提出了一种基于特征值分解的多重正交匹配追踪算法（EIG MOMP）。首先,利用特征值分解对阵列接收数据进行降维处理。这一降维操作使得问题转化为了一个具有多重观测向量（MMV）的欠定方程求解问题。接着利用MOMP算法对降维后的数据进行处理,最终得到信号的DOA估计值。该算法实现了在低信噪比下远场窄带信号源的高分辨DOA估计,并具有较低的运算复杂度。将本文提出的算法与传统的Capon算法、多重信号分类算法（MUSIC）以及正交匹配追踪算法（OMP）进行了对比。结果证明,该算法在低信噪比下能取得较好的DOA估计效果,可以针对任意的相干信号源,并且具有高分辨率的优点。