Signal waveform estimation in the presence of uncertainties about the steering vector

We consider the problem of signal waveform estimation using an array of sensors where there exist uncertainties about the steering vector of interest. This problem occurs in many situations, including arrays undergoing deformations, uncalibrated arrays, scattering around the source, etc. In this paper, we assume that some statistical knowledge about the variations of the steering vector is available. Within this framework, two approaches are proposed, depending on whether the signal is assumed to be deterministic or random. In the former case, the maximum likelihood (ML) estimator is derived. It is shown that it amounts to a beamforming-like processing of the observations, and an iterative algorithm is presented to obtain the ML weight vector. For random signals, a Bayesian approach is advocated, and we successively derive an (approximate) minimum mean-square error estimator and maximum a posteriori estimators. Numerical examples are provided to illustrate the performances of the estimators.     

A modified high-order cumulant music algorithm for signal DOA estimation

This paper gives a MUSIC signal DOA estimation algorithm based on the modified high-order cumulant matrix which is constructed by the recieved data and their conjugate rearrangements. When the snapshot number is limited, this algorithm can improve the signal DOA estimation performances obviously, and its computational complexity scarcely increases. Finally, some simulation results to verify the theoretical analyses are presented.     

An improved cyclic beamforming method for signal DOA estimation

Modulated signals used in telecommunication are cyclostationary. This property can be exploited to improve the direction of arrival (DOA) estimation performance. In this work, we propose an improved cyclic beamforming algorithm exploiting cyclostationarity. The proposed method exploits the information of both cyclic correlation matrix and cyclic conjugate correlation matrix with different cyclic frequencies. Compared with the existing methods, the simulation results show that proposed method improves substantially the signal selectivity; also it allows an increase in resolution power.     

自适应频率和二维波达方向角跟踪估计算法

运用特征子空间分析方法的关键问题在于信号或噪声子空间的估计,在实际中有些信号的统计特性通常是随时间变化的,这时需要随时根据新的阵列接收数据对信号或噪声子空间进行更新,以得到参数的实时估计值,在该文中建立了多维信号参量联合估计的3D Unitary ESPRIT算法,然后提出了基于球面平均 ULV分解的子空间跟踪算法,将子空间跟踪算法与多维信号多量联合估计算法相结合,得到多维时变信号参数的跟踪估计算法,仿真计算结果验证了该算法的有效性。     

A novel approach for DOA estimation in unknown correlated noise fields

The key of the subspace-based Direction Of Arrival （DOA） estimation lies in the estimation of signal subspace with high quality. In the case of uncorrelated signals while the signals are temporally correlated, a novel approach for the estimation of DOA in unknown correlated noise fields is proposed in this paper. The approach is based on the biorthogonality between a matrix and its Moore-Penrose pseudo inverse, and made no assumption on the spatial covariance matrix of the noise. The approach exploits the structural information of a set of spatio-temporal correlation matrices, and it can give a robust and precise estimation of signal subspace, so a precise estimation of DOA is obtained. Its performances are confirmed by computer simulation results.     

基于协方差矩阵重构和导向矢量估计的稳健自适应波束形成

实际应用中, 当假定的与真实的期望信号导向矢量之间存在一定误差时, 波束形成器的性能会急剧下降, 特别是当期望信号功率很强的时候.为解决这个问题, 提出了一种新的算法.当信源数小于阵元数时, 干扰加噪声协方差矩阵具有稀疏性.新方法首先利用该特性重构干扰加噪声协方差矩阵并由此得到与干扰导向矢量正交的子空间, 使接收的数据通过该子空间得到只含有期望信号和噪声的混合信号, 然后,对该混合信号基于最大化输出功率原理估计期望信号导向矢量, 最后,把得到的导向矢量和正交子空间来构造阵列加权值.仿真结果表明:该算法分别在假定的期望信号导向矢量存在误差、期望信号很强和低快拍数时仍然具有良好的性能.     

SPATIAL SPECTRUM ESTIMATION OF COHERENT SOURCES IMPINGING ON NONUNIFORM ARRAYS

The problem of bearing estimation of coherent signal impingiog on an array ofarbitrary geometry is studied.Both approaches are developed,the first one synthesizes theobserved array data into the outputs of a linear uniform array and then processes them viaconventional techniques such as spatial smoothing etc.The second approach is based on theinvariant subspace rotation operation which is equivalent to the translational displacement ofthe artay,linearly independent signal vectors are obtained thereby to span perfectly the signalsubspace.As compared with the conventional processing techniques,the approach based oninvariant subspace rotation does not lead to an effective decrease in aperture size and thereforea decrease in resolution capability.The computer simulations are conducted to demonstrate theeffectiveness of the presented approaches.     

基于时空极化导向矢量的阵列信号多维参数联合估计

通过对极化敏感阵列的时域采样构造了具有旋转不变结构的矩阵对,该矩阵对分解的特征值与对应特征向量分别是旋转矩阵和阵列的时空导向矢量,利用该导向矢量和旋转矩阵直接得到信号的频率、二维波达方向和极化的联合估计,该多维参数计算方法具有虚拟阵元和较高的估计精度。简述了多维ESPRIT方法的基本原理,其配对方法采用了多个矩阵束具有相同的特征向量,两种方法均利用了的相同信息-矩阵束的特征值和特征向量,仿真实验验证和比较了两种方法的性能。     

Computationally efficient underwater acoustic 2-D source localization with arbitrarily spaced vector hydrophones at unknown locations using the propagator method

This paper presents a propagator-based algorithm for underwater acoustic 2-D direction-of-arrival (DOA) estimation with arbitrarily spaced vector hydrophones at unknown locations. The proposed algorithm requires only linear operations but no eigen-decomposition or singular value decomposition into the signal and noise subspaces. Comparing with its ESPRIT counterpart (Wong and Zoltowski, IEEE J Oceanic Eng 22:566–575, 1997a), the proposed propagator algorithm has its computational complexity reduced by this ratio: the number of sources to quadruple the number of vector hydrophones. Simulation results show that at high and medium signal-to-noise ratio, the proposed propagator algorithm’s estimation accuracy is similar to its ESPRIT counterpart.

Application of natural computing algorithms to maximum likelihood estimation of direction of arrival

This work presents a study of the performance of populational meta-heuristics belonging to the field of natural computing when applied to the problem of direction of arrival (DOA) estimation, as well as an overview of the literature about the use of such techniques in this problem. These heuristics offer a promising alternative to the conventional approaches in DOA estimation, as they search for the global optima of the maximum likelihood (ML) function in a framework characterized by an elegant balance between global exploration and local improvement, which are interesting features in the context of multimodal optimization, to which the ML-DOA estimation problem belongs. Thus, we shall analyze whether these algorithms are capable of implementing the ML estimator, i.e., finding the global optima of the ML function. In this work, we selected three representative natural computing algorithms to perform DOA estimation: differential evolution, clonal selection algorithm, and the particle swarm. Simulation results involving different scenarios confirm that these methods can reach the performance of the ML estimator, regardless of the number of sources and/or their nature. Moreover, the number of points evaluated by such methods is quite inferior to that associated with a grid search, which gives support to their application.     

SIMFA信号用于机载雷达LPI性能分析

提出一种机载LPI(Low Probability of Intercept)雷达HPRF(High Pulse Repetition Frequency)波形,实现了波形设计产生、接收处理,同时对波形抗干扰性能、目标探测性能和LPI性能进行了分析。首先,从原理上对所设计SIMFA(SIMultaneous Frequency Agile)信号的产生方法进行了数学分析,给出波形产生方法;然后,对SIMFA信号的接收及信号处理方法进行研究,实现了单通道接收机对所设计波形的顺利接收;其次,对于电子对抗常用的两种干扰机--瞄准式窄带干扰机和信道化接收机,给出了SIMFA信号的抗干扰性能;再次,分析了SIMFA信号的目标探测性能,并给出提高目标探测性能的方法;最后,通过截获因子的分析,说明了SIMFA信号LPI性能的有效性。     

Computationally efficient direction-of-arrival estimation of non-circular signal based on subspace rotation technique

In order to solve the problem that the high computational burden of the multiple signal classification algorithm of non-circular signal (NC-MUSIC) in direction-of-arrival (DOA) estimation,a novel computationally efficient DOA estimation algorithm based on subspace rotation technique was proposed.Firstly,the partitioning of noise subspace matrix and the subspace rotation technique (SRT) were used to construct a new reduced-dimension noise subspace.Then,the two-dimensional peak searching was converted to the one-dimensional peak searching on the basis of the separation of variables and the orthogonality between the new reduced-dimension noise subspace and the space spanned by the columns of the extended manifold matrix.The proposed algorithm can enhance the computational efficiency by means of the conversion of the two-dimensional peak searching into the one-dimensional peak searching and the removal of redundant computations.Theoretical analysis and simulation results show that the proposed algorithm can reduce the computational complexity to less than 5% as compared to NC-MUSIC algorithm on the premise of ensuring the accuracy of DOA estimation.Especially,the efficiency advantage of the proposed algorithm is more obvious in scenarios where the large numbers of sensors are required.     

BLIND CHANNEL ESTIMATION IN DELAY DIVERSITY FOR FREQUENCY SELECTIVE CHANNELS1

Delay diversity is an effective transmit diversity technique to combat adverse effects of fading. Thus far, previous work in delay diversity assumed that perfect estimates of current channel fading conditions are available at the receiver and training symbols are required to estimate the channel from the transmitter to the receiver. However, increasing the number of the antennas increases the required training interval and reduces the available time with in whichdata may be transmitted. Learning the channel coefficients becomes increasingly difficult for the frequency selective channels. In this paper, with the subspace method and the delay character of delay diversity, a channel estimation method is proposed, which does not use training symbols. It addresses the transmit diversity for a frequency selective channel from a single carrier perspective in the form of a simple equivalent flat fading model. Monte Carlo simulations give the performance of channel estimation and the performance comparison of our channel-estimation-based detector with decision feedback equalization, which uses the perfect channel information.     

Two-dimensional DOA estimation of coherent signals using acoustic vector sensor array

In this paper, we present two new methods for estimating two-dimensional (2-D) direction-of-arrival (DOA) of narrowband coherent (or highly correlated) signals using an L-shaped array of acoustic vector sensors. We decorrelate the coherency of the signals and reconstruct the signal subspace using cross-correlation matrix, and then the ESPRIT and propagator methods are applied to estimate the azimuth and elevation angles. The ESPRIT technique is based on the shift invariance property of array geometry and the propagator method is based on partitioning of the cross-correlation matrix. The propagator method is computationally efficient and requires only linear operations. Moreover, it does not require any eigendecomposition or singular-value decomposition as for the ESPRIT method. These two techniques are direct methods which do not require any 2-D iterative search for estimating the azimuth and the elevation angles. Simulation results are presented to demonstrate the performance of the proposed methods.     

Power allocation in vector estimation systems with the impact of wireless channel uncertainty

Motivated by the fact that wireless channel uncertainty always exists and influences the distributed estimation system, this paper proposes power allocation schemes for linear minimum mean square error estimation. We consider training‐based vector systems and investigate how the power allocation ratio between training and transmitting is influenced by the system information, assuming that the sum of training and data transmitting power is fixed. We propose to use the average mean square error as the distortion measure so as to fulfill the statistical characteristics of channel estimation. We derive the closed‐form solutions to the optimal power allocation ratio, which is the function of system parameters, such as the vector signal's size, channel covariance, and noise covariance. Monte Carlo simulations are carried out to verify the performance of the proposed methods. Simulation results show that (i) in vector estimation system, the power allocation set to be 0.5 is usually not optimal; (ii) compared with training based, the equal power allocation system, the newly proposed methods could significantly improve the estimation performance. Copyright © 2015 John Wiley & Sons, Ltd.     

结合图像处理与时频分析的直扩/跳频信号参数估计

为实现扩频带宽大于跳频间隔、干扰信号强度较大及存在信道噪声时直扩/跳频(DS/FH)信号参数精确估计,提出了一种结合图像处理算法与时频分析技术的DS/FH信号参数估计方法。该方法首先利用时频分析算法提取DS/FH信号的时频分布图,然后结合干扰分量在时频图中的图像特征,利用图像处理的开运算与闭运算剔除了图像中的孤立干扰点及"空洞"干扰点。特别地,为抑制复杂的毛刺干扰信号提出了一种迭代相关匹配算法。最后通过分析图像像素位置与时频参数的对应关系完成DS/FH信号的参数估计。仿真实验表明,该方法可以有效消除干扰信号的影响,准确快速提取混合扩频信号的时频参数。     

自适应估计信号个数的频谱信号提取算法

无线电监测频谱数据中包含有大量的信号,准确提取这些信号有利于掌握全频段的频谱使用情况。实际信号的频谱由于受噪声干扰,会出现信号频带内个别频点能量值低于检测门限的情况,此时传统的门限检测算法会将该信号错估为多个信号,产生多个虚假的相邻信号间隔,导致频谱信号提取的准确率下降。对此,根据虚假相邻信号间隔特点,提出一种自适应估计信号个数的频谱信号提取算法,该算法可自动、准确估计频谱监测数据中电磁信号的个数,并将对应的信号及频谱信息提取出来。实验结果表明,该方法具有自适应性、强鲁棒性和高准确性,有效提高了频谱信号提取的准确率,为军、民电磁环境的识别与掌握提供基础的电磁信号数据支撑。     

局域波分析在跳频信号参数估计中的应用（李）

【】为了对截获的跳频信号进行有效分析并估计其参数,根据跳频信号频率随时间变化的特点,提出了一种基于局域波分析的跳频参数估计方法。本文利用基于噪声辅助的局域波分解方法先将跳频信号分解成多个单分量信号,再将每个分量求其希尔伯特谱,得出时频图及其边际谱图,进而准确估计出跳频周期和跳频频率等参数。理论分析和仿真结果表明,这种新的时频分析方法能够有效抑制噪声干扰,得到跳频信号的时频图效果很好,是一种有效的跳频信号参数估计方法。     

低信噪比条件下DS信号的检测和参数估计

本文在对目前存在的各种DS信号检测方法进行充分分析、研究的基础上，提出了一种预处理修正周期的DS检测方法。计算机模拟表明，与其他方法相比，该算法具有较好的检测和参数估计性能。     

矢量信号源中IQ调制器的设计与实现

实现了一种应用于矢量信号源中的新型IQ调制器。其中,硬件电路包括FPGA电路,滤波、放大、输出电路以及控制器电路,软件设计主要有基带信号发生器和基于改进型CORDIC算法的IQ调制。该调制器不但可以直接实现MASK,MFSK,MPSK,QPSK,MSK以及QAM等调制,还可以产生正交载波信号。仿真与实际测试结果证明了该设计的正确性和优越性。