基于双L型阵的二维测向算法

提出了一种基于双L型阵的二维测向算法。通过定义新的三维波达方向角,同时利用旋转不变算法获得每一维入射角的独立估计,再由波达方向角之间的约束关系进行配对,提高了测向精度与二维角成功配对概率,改善了双L阵二维测向精度对来波方向敏感的问题。计算机仿真结果表明:所提算法在观测的区域内均具有较高测向精度,特别是在仰角较小时较传统算法其测向精度有较大提高,在低信噪比情况下与传统算法比较提高了二维测向中的角度配对成功概率。     

Noncausal 2-D spectrum estimation for direction finding

A two-dimensional noncausal autoregressive (NCAR) plus additive noise model-based spectrum estimation method is presented for planar array data typical of signals encountered in array processing applications. Since the likelihood function for NCAR plus noise data is nonlinear in the model parameters and is further complicated by the unknown variance of the additive noise, computationally intensive gradient search algorithms are required for computing the estimates. If a doubly periodic lattice is assumed, the complexity of the approximate maximum likelihood (ML) equation is significantly reduced without destroying the theoretical asymptotic properties of the estimates and degrading the observed accuracy of the estimated spectra. Initial conditions for starting the approximate ML computation are suggested. Experimental results that can be used to evaluate the signal-plus-noise approach and compare its performance to those of signal-only methods are presented for Gaussian and simulated planar array data. Statistics of estimated spectrum parameters are given, and estimated spectra for signals with close spatial frequencies are shown. The approximate ML parameter estimate's asymptotic properties, such as consistency and normality, are established, and lower bounds for the estimate's errors are derived, assuming that the data are Gaussian     

ESPRIT-based 2-D direction finding with a sparse uniform array ofelectromagnetic vector sensors

Aperture extension (interferometry baseline extension) is achieved in this novel ESPRIT-based two-dimensional (2-D) arrival angle estimation scheme using a sparse (a.k.a., thin or thinned) uniform rectangular array of electromagnetic vector sensors spaced much farther apart than a half-wavelength. An electromagnetic vector sensor is composed of six spatially co-located, orthogonally oriented, diversely polarized antennas, distinctly measuring all six electromagnetic-field components of an incident multisource wavefield. Each incident source's direction of arrival (DOA) is estimated from the source's electromagnetic-field vector component and serves as a coarse reference to disambiguate the cyclic phase ambiguities in ESPRIT's eigenvalues when the intervector sensor spacing exceeds a half wavelength. Simulations demonstrate the significant performance gain realizable by this method for radar and wireless mobile fading-channel communications     

Cramer-Rao bound analysis for active array with antenna switching

An active radar array with antenna switching provides information on the direction, velocity and range of multiple-targets, in which the receiving antennas are switched to the single receiving channel periodically. The Cramer-Rao bound expressions for estimating the movement parameters using the radar system are derived.     

基于高斯方向图的二维子阵级相干源超分辨测向

二维子阵级超分辨测向在相控阵雷达中具有重要应用.本文研究适用于相干源的子阵级ML估计方法.提出了子阵级ML估计的信号模型.利用子阵相位中心与增益来构造简化的阵列流形,使相控阵的校正成本与代价得到较大的降低.引入加权网络对子阵输出进行后处理,大大提高了阵列处理的灵活性.构造了高斯模式的子阵方向图.与直接简化的阵列流形方法相比,基于高斯方向图的简化阵列流形方法克服了其测向范围无法调整的局限性,且能更好抑制旁瓣源.仿真结果证实了所提出方法的有效性.     

A wide aperture sampling linear array for direction finding

A method of direction finding by sampling and storing the relative phase and amplitude of the signal received at the elements of an array of many wavelengths aperture is described. If the wave interference field set up by sky waves moves slowly relative to the time of acquiring the samples through a single receiving channel, then the interfering modes may be resolved, as in a beamed array, by combining the samples in a computer. The direction of only the dominant mode can be determined when wave interference fading is relatively rapid. By correlating the outputs of the scanning receiver and a reference receiver, the effective bandwidth of the system can be determined in the latter by the desired carrier spectrum rather than by the scanning rate. An analysis is made of the error in a randomly fading field.     

Simple approximations to the Cramer-Rao lower bound on direction ofarrival for closely spaced sources

It is demonstrated that the Cramer-Rao lower bound (CRLB) on the directions of arrival (DOAs) of two closely spaced uncorrelated plane wave sources impinging on a uniform line array can be approximated by simple algebraic formulas which provide insight into the behavior of the bound with respect to signal-to-noise ratios (SNRs), source separation, etc. These formulas are sufficiently accurate for general use with the number of sensors between about 6 and 60, for source separations down to about 0.1 of a classical beamwidth, and for SNRs from -30 to +30 dB     

2-D direction finding of coherent signals VIA Temporo-Spatial processing

In most wireless communication systems, two-dimensional Directions-Of-Arrival (DOA) of multipath signals need to be found for spatial selective transmission. However, it is quite difficult to find their DOAs due to the coherent nature of multipath signals and considerable computations when performing 2-D searches. In this paper, a new algorithm to estimate 2-D DOA of multiple narrow-band signals is proposed. A DOA cyclic matrix is constructed whose eigenvalues and eigenvectors can be simultaneously used to extract 2-D DOA without 2-D searches. By exploiting the temporal property of cyclostationarity, the signal detection capability is significantly improved. Besides, based on the decorrelation model for mobile terminal signals, the algorithm can be effectively extended to the coherent case without spatial smoothing and the loss of array aperture. Simulation results are given to illustrate the performance of the new algorithm.     

Gaussian Cramer-Rao bound for direction estimation of noncircular signals in unknown noise fields

This paper focuses on the stochastic Cramer-Rao bound (CRB) on direction of arrival (DOA) estimation accuracy for noncircular Gaussian sources in the general case of an arbitrary unknown Gaussian noise field parameterized by a vector of unknowns. Explicit closed-form expressions of the stochastic CRB for DOA parameters alone are obtained directly from the Slepian-Bangs formula for general noncircular complex Gaussian distributions. As a special case, the CRB under the nonuniform white noise assumption is derived. Our expressions can be viewed as extensions of the well-known results by Stoica and Nehorai, Ottersten et al., Weiss and Friedlander, Pesavento and Gershman, and Gershman et al. Some properties of these CRBs are proved and finally, these bounds are numerically compared with the conventional CRBs under the circular complex Gaussian distribution for different unknown noise field models.     

基于CNN特征的宽频段智能测向方法

当前电磁环境日益复杂,利用机器学习方法实现快速且精确的宽频段无线电测向逐渐成为研究的热点。使用卷积神经网络基于端到端的方式完成宽频段测向的方法能够在一定程度上解决宽频段相位模糊的问题,但卷积运算后特征维数大大增加,稀疏的特征影响了最后一层全连接前馈神经网络的分类效果。针对这一问题,提出将无线电测向分为特征学习任务和方向预测任务,使用卷积神经网络作为特征提取器,将通过多层卷积运算得到的结果视为二次提取的特征,作为方向预测任务的输入;针对二次提取特征的稀疏性,提出使用主成分分析算法对特征进行降维,并将稀疏性降低后的特征作为后续分类器的输入。此外,针对特征的特点,探索了几种分类模型作为分类器的效果,包括决策树、随机森林、径向基函数神经网络和K-近邻。实验结果表明,使用主成分分析算法对特征进行降维能够提升训练和测试效率;采用K-近邻构成分类器的准确度明显高于原卷积神经网络的准确度;若需要兼顾准确度和测向效率,采用随机森林构成分类器的效果最好。     

基于均匀线阵的宽带信号测向算法

宽带测向问题是目前雷达与电子对抗领域中重要研究课题之一。给出了宽带信号测向中求聚焦矩阵的一大类方法 ,聚焦前后阵列信噪比没有任何损失。仿真实验结果证明了算法的高精度、高分辨率性能     

基于分数阶矩的测向算法研究

在SαS噪声环境中基于阵列输出信号分数阶矩的TLS-ESPRIT测向算法,该算法在保持了原TLS-ESPRIT算法无需谱峰搜索,计算量和存储量小等优点的基础上,对加性SαS噪声有较好的抑制作用,扩展了ESPRIT算法的信号模型和应用环境,计算机仿真验证了该算法的何行性和有效性。     

基于稀疏互质L型阵列的二维测向算法

在阵元数确定的情况下,稀疏阵列能增大阵列孔径,提高测向精度,但也带来测向模糊。针对这个问题,提出了一种稀疏互质L型阵列及其二维测向算法。利用四阶累积量的阵列扩展特性,并结合L型阵列中阵元间距的互质关系,在稀疏阵列结构下实现无模糊二维测向。与二维虚拟旋转不变算法相比,所提算法允许参考阵元间距大于信号半波长,从而提高了测向精度,同时利用逐步解模糊,保证较大的系统容差。计算机仿真结果验证了算法的有效性。     

A compact Cramer-Rao bound expression for parametric estimation ofsuperimposed signals

The problem of parameter estimation of superimposed signals in white Gaussian noise is considered. Closed-form expressions of the Cramer-Rao bound for real or complex signals with vector parameters are derived, extending recent results by P. Stoica and A. Nehorai (1989)     

A modified uniform Cramer-Rao bound for multiple pinhole aperture design

This paper presents a modified Uniform Cramer-Rao bound (UCRB) for studying estimator spatial resolution and variance tradeoffs. We proposed to use a resolution constraint that is imposed on mean gradient vectors of achieved estimators and derived the minimum achievable variance for any estimator satisfies this resolution constraint. This approach partially overcomes the limitations of the former UCRB approach based on a bias-gradient norm constraint. We applied this method in a feasibility study of using multiple pinhole apertures for small animal SPECT imaging applications. The SPECT system studied was based on an existing gamma camera. The achievable spatial resolution and variance tradeoffs for systems with different design parameters, such as number of pinholes and pinhole size, were studied.     

Linearization method for finding Cramer-Rao bounds in signalprocessing

A new approach for finding and interpreting Cramer-Rao bounds in signal processing is presented in this correspondence. Using the linearization method in nonlinear models and the decoupling technique in linear models, the new method simplifies the burdensome derivations in finding Cramer-Rao bounds and offers insight to their interpretations     

Nonmatrix Cramer-Rao bound expressions for high-resolutionfrequency estimators

Analytical expressions are derived for the Cramer-Rao (CR) lower bound on the variance of frequency estimates for the two-signal time-series data models consisting of either one real sinusoid or two complex sinusoids in white Gaussian noise. The expressions give the bound in terms of the signal-to-noise ratio (SNR), the number N of data samples, and a function dependent on the frequency separation and the initial phase difference between the two signal components of each model. The bounds are examined as the phase difference is varied, and the largest and smallest bound expressions and the corresponding critical values of the phase difference are obtained. The exact expressions are analyzed for the case of small frequency separations δω. It is found that the largest bound is proportional to (N·δω)^-4/N³·SNR and that the smallest bound is proportional to (N·δω)^-2 N³·SNR for small δω. Examples indicate that the small δω results closely approximate the exact ones whenever the frequency separation is smaller than the Fourier resolution limit. Based on the asymptotic results, it is found that the threshold SNR at which an unbiased estimator can resolve the two signal frequencies is at least proportional to (N·δω)^-6 /N for the worst phase difference case and to (N·δω)^-4/N for the best phase difference case for small δω. The results are applicable to the general case of sampling where the samples are taken at arbitrary instants     

The Cramer-Rao lower bound for bilinear systems

Estimation of the unknown parameters that characterize a bilinear system is of primary importance in many applications. The Cramer-Rao lower bound (CRLB) provides a lower bound on the covariance matrix of any unbiased estimator of unknown parameters. It is widely applied to investigate the limit of the accuracy with which parameters can be estimated from noisy data. Here it is shown that the CRLB for a data set generated by a bilinear system with additive Gaussian measurement noise can be expressed explicitly in terms of the outputs of its derivative system which is also bilinear. A connection between the nonsingularity of the Fisher information matrix and the local identifiability of the unknown parameters is exploited to derive local identifiability conditions of bilinear systems using the concept of the derivative system. It is shown that for bilinear systems with piecewise constant inputs, the CRLB for uniformly sampled data can be efficiently computed through solving a Lyapunov equation. In addition, a novel method is proposed to derive the asymptotic CRLB when the number of acquired data samples approaches infinity. These theoretical results are illustrated through the simulation of surface plasmon resonance experiments for the determination of the kinetic parameters of protein-protein interactions.     

基于均匀圆阵的二维ESPRIT算法研究

在深入研究均匀圆阵扩展阵列流形的基础上，针对某些均匀圆阵的扩展阵列中含有旋转不变性的子阵列组以及中心对称性质，提出了2种基于均匀圆阵的二维ESPRIT算法，第一种算法减少了一次奇异值分解，第二种算法避免了参数配对。仿真结果验证了算法的正确性和有效性，并且在高斯色噪声条件下，其测向性能均优于MUSIC算法和最大似然算法。     

A NEW ARRAY STRUCTURE FOR ESTIMATING 2-D DIRECTION-OF-ARRIVAL:Y-SHAPED ARRAY

This paper presents a new array structure for estimating two-dimensional (2-D) direction-of-arrivals (DOAs). The structure is called Y-shaped array, which has 10% better accuracy potential than the newly-developed L-shaped array. A great merit is its ability to estimate 2-D DOAs whichever directions the arriving signals come from, compared with L-shaped array whose performance depends on DOAs. Simulation results are given to demonstrate the performance of the new array.