基于线性时频分析的非平稳源空间谱估计

针对存在幅度和相位调制的非平稳信号的定向问题,本文提出了一种改进的线性时频空间谱估计算法。该算法首先在线性时频面上检测信号的时频脊,并沿各时频脊进行采样形成独立的阵列时频快拍,然后对各快拍采用通用算法进行空间谱估计,此时阵列能估计的信号源数已不再受阵元数的限制。计算机仿真实验表明,该算法具有优良的定向精度,且能有效地对多个非平稳信号源进行分离和空间谱估计,其计算量远低于二次时频空间谱估计算法。     

用菲涅尔区板天线实现来波方向的鲁棒性估计

针对蜂窝移动通信局域散射电波传播的空间分布源模型,研究了应用菲涅尔区相位修正平面聚焦结构和馈源阵列的组合形成的一种新型多波束自适应天线．基于广义MUSIC算法及极大似然估计算法．分别实现相干及非相干的空间分布式源信号来波方向及其角度扩展参数的鲁棒性估计问题．数值仿真计算结果表明．在这一无线传播环境中,基于菲涅尔区相位修正平面聚焦结构在减小相关参数估计的RMS误差等方面,其性能优于传统的均匀直线阵列模型．因此基于该新型自适应天线．可以实现来波方向的鲁棒性估计。     

多基站非圆信号直接定位：降维PM与泰勒补偿

针对现有多阵列非圆(NC)信号直接定位方法(DPD)谱峰搜索计算复杂度高,对基站的位置比较敏感,没有考虑信号在空间中传播时的损耗差异,导致估计性能不稳定的问题,提出一种联合降维传播算子与泰勒补偿(JRT-PM)的非圆信号直接定位算法。首先根据非圆信号的椭圆协方差信息扩展阵列孔径,通过降维方法消除非圆相位搜索维度进行粗估计降低计算复杂度,然后联合所有基站的信息进行泰勒补偿提升算法估计性能。仿真实验表明,相比于传统到达角K均值聚类(AOA-clustering)两步定位算法、最小均方无畸变响应(MVDR)直接定位算法、子空间数据融合(SDF)直接定位算法,所提算法在提升定位精确度的同时可以估计更多目标;与非圆传播算子(NC-PM)直接定位相比,所提算法在保证估计性能的同时显著降低了计算复杂度。     

多星测控中波达方向估计传播因子虚拟ESPRIT算法

针对多星测控中涉及的阵列天线信号波达方向估计问题,给出了一种新的基于传播因子的二维波达方向估计(2D-DOA)算法--传播因子虚拟ESPRIT算法(PMV-ESPRIT).传统的虚拟ESPRIT算法需要对互相关矩阵进行特征值分解(EVD)或对数据接收矩阵进行奇异值分解(SVD),因此其运算量较大.而新算法基于传播因子方...     

An adaptive algorithm for separating and tracking multipledirectional sources in linear arrays

An algorithm for spatially filtering out, enhancing, and tracking individual directional sources in an adaptive array is proposed and investigated. In this algorithm, the sources are separated by using an adaptive beamformer whose outputs are processed by using the LMS algorithm to track distinct sources individually. From the LMS weights used, the source locations can be estimated. Whenever significant changes in these are detected, the beamformer is updated so that its outputs will be due to different sources in the steady state. With this algorithm, the problems of look-direction errors in look-direction constrained arrays and of large signal power in power inversion arrays are eliminated, and the enhancement of multiple moving sources becomes a natural process. Furthermore, because the sources are individually tracked and the beamformer is only updated occasionally, the algorithm possesses fast tracking behavior, and its implementation complexity is comparable to that of beamformer-based adaptive arrays using the LMS algorithm     

Broadband source tracking using adaptive directional filter withmaximally flat responses at null directions

A new algorithm for the tracking of broadband sources in linear arrays employing tapped delay line processing is presented and investigated. Using this technique, each broadband source is individually tracked in a time-multiplexed manner by steering a broadband null formed from the use of a tapped delay line filter whose weights are constrained to have a maximally flat frequency response at the nulling direction. To ensure that the other nulls are not changed while a particular null is being adjusted, a beamformer is employed to preprocess the element signals before adaptive processing. Since the new algorithm updates only one tapped delay line filter at any instant, the overall implementation complexity of the proposed algorithm is comparable with using the LMS algorithm directly on a broadband array. However, unlike the latter algorithm, the convergence behavior of the new algorithm is significantly faster and is almost independent of the external noise environment     

A fast adaptive array algorithm for tracking nonstationaryenvironments

This correspondence presents a new adaptive algorithm that estimates both the optimal weight and its rate of change to achieve better tracking in nonstationary environments. Although the algorithm has been derived using a two-element array, it can be used directly in the multielement source separation array proposed in [1]     

Source separation in post-nonlinear mixtures

We address the problem of separation of mutually independent sources in nonlinear mixtures. First, we propose theoretical results and prove that in the general case, it is not possible to separate the sources without nonlinear distortion. Therefore, we focus our work on specific nonlinear mixtures known as post-nonlinear mixtures. These mixtures constituted by a linear instantaneous mixture (linear memoryless channel) followed by an unknown and invertible memoryless nonlinear distortion, are realistic models in many situations and emphasize interesting properties i.e., in such nonlinear mixtures, sources can be estimated with the same indeterminacies as in instantaneous linear mixtures. The separation structure of nonlinear mixtures is a two-stage system, namely, a nonlinear stage followed by a linear stage, the parameters of which are updated to minimize an output independence criterion expressed as a mutual information criterion. The minimization of this criterion requires knowledge or estimation of source densities or of their log-derivatives. A first algorithm based on a Gram-Charlier expansion of densities is proposed. Unfortunately, it fails for hard nonlinear mixtures. A second algorithm based on an adaptive estimation of the log-derivative of densities leads to very good performance, even with hard nonlinearities. Experiments are proposed to illustrate these results     

Blind multiuser detection: a subspace approach

A new multiuser detection scheme based on signal subspace estimation is proposed. It is shown that under this scheme, both the decorrelating detector and the linear minimum-mean-square-error (MMSE) detector can be obtained blindly, i.e., they can be estimated from the received signal with the prior knowledge of only the signature waveform and timing of the user of interest. The consistency and asymptotic variance of the estimates of the two linear detectors are examined. A blind adaptive implementation based on a signal subspace tracking algorithm is also developed. It is seen that compared with the previous minimum-output-energy blind adaptive multiuser detector, the proposed subspace-based blind adaptive detector offers lower computational complexity, better performance, and robustness against signature waveform mismatch. Two extensions are made within the framework of signal subspace estimation. First, a blind adaptive method is developed for estimating the effective user signature waveform in the multipath channel. Secondly, a multiuser detection scheme using spatial diversity in the form of an antenna array is considered. A blind adaptive technique for estimating the array response for diversity combining is proposed. It is seen that under the proposed subspace approach, blind adaptive channel estimation and blind adaptive array response estimation can be integrated with blind adaptive multiuser detection, with little attendant increase in complexity     

An algorithm for multisource beamforming and multitarget tracking

A new algorithm for simultaneous robust multisource beamforming and adaptive multitarget tracking is proposed. Self-robustness to locations errors or variations is introduced by a source-subspace-based tracking procedure of steering vectors in the array manifold. This LMS-type procedure is generalized from a former work we developed in the single source case. Two beamforming structures are actually proposed. The first is adaptive and optimal for uncorrelated sources and correlated noise. The second is conventional and optimal for correlated sources and uncorrelated white noise. The proposed algorithm and MUSIC show an identical asymptotic variance in localization for immobile sources, whereas for the mobile case, the proposed algorithm is highly advantageous. Then, it is shown that the additional use of some kinematic parameters (i.e., speed, acceleration, etc.) inferred from the reconstructed trajectories improves the tracking performance and overcomes some of the problems of crossing targets. The efficiency of multitarget tracking and the robustness of multisource beamforming are proved and then confirmed by simulation. The number of sources can be initialized and tracked by a marginal proposed procedure. The beamforming performance is shown to be optimal as the single source case. Finally, the algorithm has a very low order of arithmetic complexity     

Adaptive Beamforming and Recursive DOA Estimation Using Frequency-Invariant Uniform Concentric Spherical Arrays

This paper proposes recursive adaptive beamforming and broadband 2-D direction-of-arrival (DOA) estimation algorithms for uniform concentric spherical arrays (UCSAs) having nearly frequency-invariant (FI) characteristics. The basic principle of the FI-UCSA is to transform the received signals to the phase mode and remove the frequency dependency of individual phase modes through a digital beamforming network. Hence, the far-field pattern of the array is determined by a set of weights. Thanks to the FI characteristic, traditional narrowband adaptive beamforming algorithms such as minimum variance beamforming and the generalized sidelobe canceller method can be applied to the FI-UCSA. Simulation results show that the proposed adaptive FI-UCSA beamformer achieves a lower steady-state error and converges faster than the conventional tapped-delay line approach while requiring fewer adaptive coefficients. A new broadband 2-D DOA estimation algorithm using ESPRIT techniques for FI-UCSA is proposed to recursively estimate the DOAs of the moving targets. Simulation results show that the proposed DOA estimation algorithm achieves a satisfactory performance for slowly varying sources at low arithmetic complexity.      

时空欠采样线性调频信号参数及其二维到达角联合估计

针对宽频段(2-18GHz)内非平稳来波信号的参数估计和测向问题,提出一种时空欠采样线性调频信号参数与二维到达角联合估计方法。该方法首先用时域解线调方法估计调频斜率,然后在分数阶傅里叶变换(FRFT)域进行滤波,实现信号提取。利用参考阵元及其延时通道进行无模糊初始频率估计,通过构建FRFT波束空间阵列模型实现无模糊测向。数值仿真表明,该方法能够实现宽频段内多个线性调频信号的参数和二维到达角精确估计,在低信噪比下仍有较好的估计性能。     

基于相位估计和线性拟合的运动补偿方法

结合基于无人水下航行器(UUV)小型拖线阵的应用,针对常规扩展拖曳阵测量方法(ETAM)在阵列存在运动误差时算法性能有所下降,提出一种运动误差估计与相位校正法.在阵形保持直线阵的前提下,采用最小二乘算法对相邻两次测量中位置重叠阵元相位相关因子进行线性拟合,根据拟合出的关系式对阵列输出数据进行相位补偿,从而有效克服线列阵的偏航带来的相位误差.在阵列偏航3°和5°时,仿真结果表明新算法的方位分辨性能有所提高.     

脉冲噪声下基于相关熵的相干分布源DOA估计新方法

针对复杂电磁环境下被动无线监测定位问题,该文提出广义相关熵的概念,推导了广义相关熵的性质,用以抑制阵列输出信号中的脉冲噪声。为了实现脉冲噪声环境下相干分布源中心DOA和扩散角的联合估计,提出基于广义相关熵的DOA估计新方法,并证明了该方法的有界性。为进一步提升算法的鲁棒性,推导了一种仅依赖阵列输出信号的自适应核函数。仿真结果表明,该算法能够实现脉冲噪声环境下相干分布源参数的联合估计,相比已有算法,具有更高的估计精度和鲁棒性。     

矢量传感器阵列MIMO雷达高精度二维DOA与极化联合估计

该文采用矢量传感器配置下的十字型阵列MIMO雷达系统,提出一种新的2维高精度DOA与极化参数联合估计算法。首先根据MIMO雷达虚拟阵列导向矢量的特点,通过降维矩阵的设计及回波数据的降维变换,将高维回波数据转换至低维信号空间;然后基于传播算子获得对应信号子空间的估计,利用收、发阵列阵元间长基线对应的旋转不变性和极化矢量中电场矢量和磁场矢量的叉积进行2维高精度DOA估计和解模糊处理,同时利用与阵列结构无关的极化域旋转不变性进行极化辅角和极化相位差的联合估计。该矢量传感器MIMO雷达阵列可同时获取MIMO雷达的波形分集和矢量传感器的极化分集,无需额外增加阵元和硬件开销,能够有效扩展阵列孔径,提高参数估计性能;同时通过降维变换及传播算子,在获取信噪比增益的同时,能够实现2维高精度DOA和2维极化矢量的联合估计及参数的自动配对,有效降低数据处理维数和参数估计的运算复杂度;最后,仿真结果验证了理论分析的正确性和算法的有效性。     

基于噪声子空间解析形式的快速DOA估计算法

该文针对特殊的信号环境各辐射源信号均值相等且不为零,利用均匀线阵导向矢量的Vandermonde结构,推导出了噪声子空间的解析形式,并以此为基础提出了利用均匀线阵和稀疏平面阵的1维和2维DOA估计快速算法。该算法不需要计算接收数据的协方差矩阵,也不需要任何矩阵分解,因此计算量远小于传统的超分辨DOA估计,而且无论信号之间是否具有相干性,该方法有相同的估计性能。仿真实验表明,在噪声均值为零且快拍数足够的条件下,该方法的估计性能整体上与Root-MUSIC算法相当,而在信噪比较低时性能优于后者。     

基于重加权?1范数惩罚的远近场混合源定位算法

现有信源定位方法大多假定信源是远场源或近场源,而实际定位系统中往往存在远场源和近场源共存的情况。为实现远、近场源分离及高精度信源定位,本文在稀疏信号重构理论框架下提出了一种新的远近场混合源定位算法。该算法利用阵列协方差矩阵反对角线元素和重加权l1范数惩罚获得所有信源的到达角（Direction Of Arrival, DOA）估计。在DOA估计的基础上,根据远场与近场源距离参数位于不同区间的特点利用一维搜索实现远、近场源分离以及近场源距离参数的估计。从理论角度分析了重加权l1范数惩罚算法的重构性能。本文所提算法不仅同时适用于高斯和非高斯信号,而且无需多维搜索和参数配对,也无需信源数的先验信息,同时还可以获得较好的定位精度。计算机仿真结果验证了所提算法的有效性。     

A Physically Constrained Maximum-Likelihood Method for Snapshot-Deficient Adaptive Array Processing

This paper presents a physically constrained maximum-likelihood (PCML) method for spatial covariance matrix and power spectral density estimation as a reduced-rank adaptive array processing algorithm. The physical constraints of propagating energy imposed by the wave equation and the statistical nature of the snapshots are exploited to estimate the ldquotruerdquo maximum-likelihood covariance matrix that is full rank and physically realizable. The resultant matrix may then be used in adaptive processing for interference cancellation and improved power estimation in nonstationary environments where the amount of available data is limited. Minimum variance distortionless response (MVDR) power estimates are computed for a given environment at different levels of snapshot support using the PCML method and several other reduced-rank techniques. The MVDR power estimates from the PCML method are shown to have less bias and lower standard deviation at a given level of snapshot support than any of the other reduced-rank methods used. Furthermore, the estimated power spectral density from the PCML method is shown to offer better low-level source detection than the MVDR power estimates.     

相干信源波达方向估计的广义最大似然算法

论文基于广义导向矢量和广义阵列流形矩阵,建立了多相干源(组)情况下的阵列数据模型,然后提出了波达方向估计的广义最大似然算法。对于广义最大似然算法,入射信源可以是多相干源(组),阵列的几何结构也没有任何约束,而且它分辨的信源数还可以大于阵元数。随后,论文将广义最大似然算法与常规最大似然算法进行了理论比较,并给出了广义最大似然竹法方位估计一致性的证明和方位估计方差的计算公式。理论分析表明,在空间只存在非相干信源时,广义最大似然算法与常规的最大似然算法是等价的,而在空间存在多相干源(组)时,它的性能较常规最大似然算法有较大的改进,方位估计的方差更小。最后论文利用遗传算法实现了广义最大似然算法,并通过MonteCarlo仿真实验证明了广义最大似然算法的有效性。     

Adaptive traffic admission for integrated services in CDMAwireless-access networks

Code-division multiple-access (CDMA) is a serious candidate for personal communication systems at 1.9 GHz in North America. We consider the issue of bandwidth management in a CDMA integrated wireless-access network with heterogeneous services. A framework for adaptive connection admission in the up-link direction is proposed. It is based on estimation of the interference at the base station receivers. The estimation algorithm employs a linear Kalman filter which is driven by a measurement of the interference and by predicted traffic parameters of the admitted connections. We derived several generic variants of the control architecture for the up-link direction to assess the main characteristics of the framework and to determine the trade-offs between complexity and performance. They vary from a fixed strategy with fixed power control to an adaptive strategy with full information about network state and adaptive power control. A numerical study of the proposed framework shows that the estimated value of the average interference adapts well to the real value under nonstationary and nonuniform environment. This feature results in high network utilization for arbitrary traffic conditions