Direction-of-arrival estimation of quasi-stationary signals using two-level Khatri-Rao subspace and four-level nested array

The Khatri-Rao（KR） subspace method is a high resolution method for direction-of-arrival（DOA） estimation.Combined with 2q level nested array,the KR subspace method can detect O（N2q） sources with N sensors.However,the method cannot be applicable to Gaussian sources when q is equal to or greater than 2 since it needs to use 2q-th order cumulants.In this work,a novel approach is presented to conduct DOA estimation by constructing a fourth order difference co-array.Unlike the existing DOA estimation method based on the KR product and 2q level nested array,the proposed method only uses second order statistics,so it can be employed to Gaussian sources as well as non-Gaussian sources.By exploiting a four-level nested array with N elements,our method can also identify O（N4） sources.In order to estimate the wideband signals,the proposed method is extended to the wideband scenarios.Simulation results demonstrate that,compared to the state of the art KR subspace based methods,the new method achieves higher resolution.     

A new method to estimate DOA in CDMA system in multi-path environment

An algorithm for direction angle of arrival（DOA） estimation and array calibration of signals from multiple mobile users in the CDMA systems and multi-path environment was presented . The main idea is that the algorithm employs code-matched filter and model of the inter-symbol interference and multiple-access interference exactly. The correlation matrices of the received signals before and after code-matched filtering were employed to eliminate the effect of the additive white Gaussian noise, and a new mathematical problem was created, a new maximum likelihood method based on the strong law of large number was derived for DOA estimation and array calibration. Computer simulation results prove that the algorithm is effective.     

DOA estimation of coexisted noncoherent and coherent signals via sparse representation of cleaned array covariance matrix

A new direction finding method is presented to deal with coexisted noncoherent and coherent signals without smoothing operation. First the direction-of-arrival (DOA) estimation task is herein reformulated as a sparse reconstruction problem of the cleaned array covariance matrix, which is processed to eliminate the affection of the noise. Then by using the block of matrices, the information of DOAs which we pursuit are implied in the sparse coefficient matrix. Finally, the sparse reconstruction problem is solved by the improved M-FOCUSS method, which is applied to the situation of block of matrices. This method outperforms its data domain counterpart in terms of noise suppression, and has a better performance in DOA estimation than the customary spatial smoothing technique. Simulation results verify the efficacy of the proposed method.     

2-D spatial-spectrum estimation for wide-band signals without a priori knowledge of the number of sources

In array signal processing,the existing methods for DOA estimation of wide-band signals are only related to the azimuth estimation when the number or sensors is known.It is shown that a circular array possesses several nice properties.2-D spatial-spectrum estimation for wide-band sources is performed by using a circular array of sensors with sample delay lines.Further,the Choi' s approach is extended,which was suggested to estimate DOA of narrow-band signals when the number of sensor was unknown,to wide-band signal processing.The extended approach is shown to be quite adequate for wide-band signal subspace spatial-spectrum estimation (BASS-LE),and a large aperture (r>λvin/2) circular array is found to possess superior resolution capability and robustness.     

Simulation on the Models of Small Scale Passive Acoustic Sensor Arrays Based on Time-Delay Estimation Method

The passive acoustic localization with planar sensor array is introduced. Based on a method to eliminate the influence of effective sound velocity in passive detection, a new five-sensors solid array and its localization model are put forward. The factors that influence the precision of the localization are analyzed. Considering the errors from the factors synchronously, the simulation compares the solid array with the planar array. It can be proved that the five-sensor solid array is better than the four-sensor planar array in the estimation of bearing elements.     

Estimation of Fractional Vegetation Cover Based on Digital Camera Survey Data and a Remote Sensing Model

The objective of this paper is to improve the monitoring speed and precision of fractional vegetation cover （fc）. It mainly focuses on fc estimation when fCmax and fcmin are not approximately equal to 100% and 0%, respectively due to using remote sensing image with medium or low spatial resolution. Meanwhile, we present a new method offc estimation based on a random set offc maximum and minimum values from digital camera （DC） survey data and a dimidiate pixel model. The results show that this is a convenient, efficient and accurate method forfc monitoring, with the maximum error 0.172 and correlation coefficient of 0.974 between DC survey data and the estimated value of the remote sensing model. The remaining DC survey data can be used as verification data for the precision of thefc estimation. In general, the estimation offc based on DC survey data and a remote sensing model is a brand-new development trend and deserves further extensive utilization.     

Building and application of multipath model for meter-wave radar in elevation estimation

A research is done for multipath effects of low-angle tracking in meter-wave radar,and the theory of multi-scattering centers of complex target is discussed as well as the character of reflected echoes.This points out that the distribution and scattering properties of scattering centers are the prime reasons which cause the variation of multipath effects,and all the changes of position,motion and attitude of the target can influence the multipath effects.By building of multipath model for multi-scattering centers for target,the analysis above is verified and a new method of elevation estimation for low-angle target is presented.The new method uses canceling vectors obtained by searching to cancel reflected waves in echoes and reduce the influence of reflected components,which can improve the accuracy of elevation estimation of low-angle target and the performance of low-angle tracking in meter-wave radar.Experimental results verify the availability of the method.     

Multi-Residual Module Stacked Hourglass Networks for Human Pose Estimation

A multi-residual module stacked hourglass network (MRSH) was proposed to improve the accuracy and robustness of human body pose estimation. The network uses multiple hourglass sub-networks and three new residual modules. In the hourglass sub-network, the large receptive field residual module (LRFRM) and the multi-scale residual module (MSRM) are first used to learn the spatial relationship between features and body parts at various scales. Only the improved residual module (IRM) is used when the resolution is minimized. The final network uses four stacked hourglass sub-networks, with intermediate supervision at the end of each hourglass, repeating high-low (from high resolution to low resolution) and low-high (from low resolution to high resolution) learning. The network was tested on the public datasets of Leeds sports poses (LSP) and MPII human pose. The experimental results show that the proposed network has better performance in human pose estimation.     

Pattern synthesis optimization of 3-D ODAR based on improved GA using LSFE method

Pattern synthesis in 3-D opportunistic digital array radar(ODAR) becomes complex when a multitude of antennas are considered to be randomly distributed in a three dimensional space.In order to obtain an optimal pattern,several freedoms must be constrained.A new pattern synthesis approach based on the improved genetic algorithm(GA) using the least square fitness estimation(LSFE) method is proposed.Parameters optimized by this method include antenna locations,stimulus states and phase weights.The new algorithm demonstrates that the fitness variation tendency of GA can be effectively predicted after several "eras" by the LSFE method.It is shown that by comparing the variation of LSFE curve slope,the GA operator can be adaptively modified to avoid premature convergence of the algorithm.The validity of the algorithm is verified using computer implementation.     

Single Radar Measurement Method for Antiaircraft Projectile Miss Distance

To measure miss distance for antiaircraft projectile,a radial velocity identification and positioning method with a single radar is proposed.By analyzing the spatial resolution of multi-frequency ranging radar,the discrimination and testing model of this radar for multi-targets (projectile and target) is established to analyze the systematic error of antiaircraft miss distance.Then through the aerial target flight test and contrast test with optical test equipment,the validity of the measurement method is verified.This new method has the potential to be used in the measurement of antiaircraft projectile miss distance.     

基于二次虚拟扩展的高分辨率波达方向估计方法

为进一步提高天线阵波达方向估计的分辨率,在四阶量多重信号分类方法的基础上,提出一种高分辨率的波达方向估计方法.利用阵列接收数据的四阶矩量进行虚拟阵列扩展,再利用阵列接收数据的共轭进行虚拟阵列扩展,实现二次虚拟扩展;将扩展后的阵列导向矢量和协方差矩阵用于波达方向估计,与原阵列导向矢量和协方差矩阵相比,相当于构造了更多的虚拟阵元,并扩展了阵列的孔径.仿真结果表明:与四阶量多重信号分类等波达方向估计方法相比,所提出的方法在波达方向估计中成功概率更高,均方误差更低,具有更高的分辨率.所提方法通过二次虚拟扩展,构造了更多的虚拟阵元,有效地提高了天线阵波达方向估计的分辨率.     

利用压缩感知的分布式高频地波雷达DOA估计

In order to solve the problems of “large position, difficulty with location chosen, low angle resolution, and poor flexibility”, and to improve the performance of DOA estimation, a new design of the distributed high frequency surface wave radar is proposed, based on the technology of distributed antennas array and the MIMO radar system. With the new design, we propose a new DOA estimation method using compressed sensing, this method reduces the complexity of data processing and solves the angle ambiguity due to the distributed array. Discrete stochastic and semi-QR factorization is also used for improving the performance of the new DOA estimation. Theoretical analysis and simulation results show the validity of the proposed method.     

DOA Estimation Method Using Sparse Repres-entation with Orthogonal Projection

In order to reduce the effect of noises on DOA estimation, this paper proposes a direc-tion-of-arrival (DOA) estimation method using sparse representation with orthogonal projection (OPSR). The OPSR method obtains a new covariance matrix by projecting the covariance matrix of the array data to the signal subspace, leading to the elimination of the noise subspace. After-wards, based on the new covariance matrix after the orthogonal projection, a new sparse representa-tion model is established and employed for DOA estimation. Simulation results demonstrate that compared to other methods, the OPSR method has higher angle resolution and better DOA estima-tion performance in the cases of few snapshots and low SNRs.     

一种特殊阵列实现DOA估计的方法

提出了一种基于特殊阵列形式实现DOA估计的方法，在均匀线性阵列（Uniform Linear Array，ULA）上增加一个阵元，将阵元放在特定的位置，得到一组特殊的阵元组合，打破了均匀线阵中导向矢量的周期性，成功地避免了角度估计模糊.此外，该方法在同等硬件资源的条件下，得到了更多阵元组合，可以获得更高的阵列方位分辨率.提出了一种适用于新阵列结构的矩阵分块空间平滑算法，利用其导向矢量的局部周期性将其数据协方差矩阵分块空间平滑后，重新组合得到修正后的数据协方差矩阵，实现了相干信号源的DOA估计.     

虚拟孔径高分辨测角方法

针对天线阵列中阵元个数有限而导致波达方向DOA估计分辨率较低的问题,该文提出了一种基于宽频跳变信号相位补偿的虚拟孔径重构方法.该方法通过利用宽频跳变信号,将多个频点的信号向量加入补偿相位形成虚拟导向矢量,从而形成远大于物理孔径的虚拟孔径,实现高分辨DOA估计.仿真实验结果表明,该文所提的虚拟阵列重构方法可有效地提高DO...     

矢量传感器阵列高分辨率方位估计技术研究

矢量传感器能同时共点拾取声场中的声压和三路相互正交的振速信息,获得额外的信息量,它的出现引起了国内外的关注.为了改善波束域高分辨率方法的性能,获得矢量传感器阵列对多目标的足够精度的分辨能力,在研究矢量传感器阵列目标估计基础上,首次在矢量传感器阵列处理领域将矢量阵波束形成与波束域目标估计算法结合起来,实现了高精度目标方位估计,与常规阵元域和波束域方法相比,提高了多目标估计性能,降低了输入信噪比门限,增大了阵列输出增益,减小了计算量.仿真验证了该算法的优越性.     

水下多传感器基阵波束域高分辨方位估计方法及实验研究

针对水声设备的实际应用，给出了基于实测基阵阵列流形的波束域高分辨方位估计的MUSIC方法。在预形成多波束时，该方法采用适用于任意阵形的自适应波束优化技术，针对水下实际阵列设计超低旁瓣波束以抑制水面上分布的强干扰。为了减小实际阵列模型和理想阵列模型失配所带来的不利影响，在多波束设计时，利用实测阵列流形代替理想阵列流形进行设计，消声水池实验结果验证了该方法可以有效地降低系统中由于阵列模型失配所造成的波束域MUSIC方位估计方法的性能损失。     

基于矢量阵的自初始化MUSIC方位估计算法

MUSIC空间谱估计突破了常规波束形成中的锐利限,能够对目标进行高精度方位估计.探讨了MUSIC算法在矢量阵上的应用,给出了矢量线阵MUSIC噪声子空间谱估计表达式,利用单个矢量阵元的阵簇估计提供的初始参数,对MUSIC噪声子空间谱进行迭代搜索谱峰实现目标的方位估计,用以提高目标方位估计的精度.对单目标和双目标方位估计进行了仿真研究,在文中的仿真条件下,当满足信噪比大于5dB的条件时,可对目标方位进行较好估计.研究结果表明,通过单个矢量阵元阵簇得出的目标方位估计精度较差,而迭代搜索MUSIC谱峰方法提高了方位估计精度.     

一种新的声矢量阵远程ESPRIT方位估计算法

为解决水下目标远程高分辨定向问题,文章提出了一种新的声矢量阵ESPRIT方位估计算法.与现有的将声矢量传感器(AVS)的振速信息作为独立阵元来处理的声矢量阵方位估计算法不同,新算法完全基于声压与振速联合信息处理,充分利用了声矢量阵(AVSA)中声压与振速的相干性原理,能更好地将ESPRIT算法的高分辨能力与AVSA的抗噪能力有机地结合起来,实现远程、高分辨方位估计.理论分析和基于湖试背景噪声的仿真实验证明了新算法的有效性.     

一种新的声矢量阵最小范数算法

现有的声矢量阵方位估计算法基本都是将声矢量传感器(AVS)的振速信息作为与声压相同的独立阵元信息来处理,没有充分利用AVS中声压和振速的相干性,以及由此带来的抗各向同性噪声能力.基于AVS中声压和振速的相干性原理,提出了一种新的声矢量阵最小范数算法.新算法充分利用了AVS中声压和振速联合信息处理的优势,能更好地将最小范数(MN)算法的高分辨能力与AVS的抗噪能力有机地结合起来,实现远程高分辨DOA估计.理论分析和基于湖试数据的仿真实验证明了所提算法的有效性.