盲源分离及盲信号提取的研究进展

盲源分离和盲信号提取是从观测信号中恢复源信号的有效方法,目前已成为信号处理领域的研究热点。介绍了盲信号处理的数学模型,给出盲源分离与盲信号提取的基本思想,阐述了各方法的原理、特点及性能,并对它们之间的区别与联系进行分析,最后指出盲源分离及盲信号提取的发展方向。     

Cumulant-based blind optimum beamforming

Sensor response, location uncertainty, and use of sample statistics can severely degrade the performance of optimum beamformers. We propose blind estimation of the source steering vector in the presence of multiple, directional, correlated or coherent Gaussian interferers via higher order statistics. In this way, we employ the statistical characteristics of the desired signal to make the necessary discrimination, without any a-priori knowledge of array manifold and direction-of-arrival (DOA) information about the desired signal. We then improve our method to utilize the data in a more efficient manner. In any application, only sample statistics are available, so we propose a robust beamforming approach that employs the steering vector estimate obtained by cumulant-based signal processing. We further propose a method that employs both covariance and cumulant information to combat finite sample effects. We analyze the effects of multipath propagation on the reception of the desired signal. We show that even in the presence of coherence, cumulant-based beamformer still behaves as the optimum beamformer that maximizes the signal-to-interference-plus-noise ratio (SINR). Finally, we propose an adaptive version of our algorithm simulations demonstrate the excellent performance of our approach in a wide variety of situations     

Performance evaluation of digital beamforming strategies for satellite communications

Smart antennas are becoming one of the promising technologies to meet the rapidly increasing demands for more capacity of satellite communication systems. A main component in a smart antenna system is beamforming. Because of the limitations of analog beamforming, digital beamforming will be employed in future satellite communication systems. We evaluate the performance of various digital beamforming strategies proposed in the literature for satellite communications: 1) single fixed beam/single user, 2) single fixed beam/multiple users, 3) single adaptive beam/single user, and 4) single Chebyshev dynamic beam/multiple users. Multiple criteria including coverage, system capacity, signal-to-interference-plus-noise ratio (SINR), and computation complexity are used to evaluate these satellite communication beamforming strategies. In particular, a Ka-band satellite communication system is used to address the various issues of these beamforming strategies. For the adaptive beamforming approach, subarray structure is used to obtain the weights of a large 2D antenna array, and a globally convergent recurrent neural network (RNN) is proposed to realize the adaptive beamforming algorithm in parallel. The new subarray-based neural beamforming algorithm can reduce the computation complexity greatly, and is more effective than the conventional least mean square (LMS) beamforming approach. It is shown that the single adaptive beam/single user approach has the highest system capacity.     

APSO算法在语音信号盲源分离中的研究

将自适应粒子群优化（Adaptive Praticle Swarm Optimization）算法运用到语音信号的盲源分离中,以峰度为目标函数,通过自适应调整惯性因子,克服了收敛速度和分离效果之间的矛盾,最终实现盲源分离。该算法避免了传统的优化算法自然梯度法稳定性差、易于陷入局部最优的不足。通过比较仿真结果和性能指标可以看出,APSO算法提高了收敛速度,改善了分离性能,表明了该算法在实现语音信号盲源分离中性能的优越性。     

基于软件无线电的机会信号实时盲分离系统

在复杂的电磁环境中,往往存在多种频率交叠的机会信号,为利用机会信号进行定位增加了难度。针对这个问题,提出了一种基于软件无线电的机会信号实时盲分离系统。系统的硬件部分是在FPGA中实现等变自适应盲分离算法的,实现了AD采集后的复数域混合信号的分离。分离后的信号通过USB3.0数据线上传到上位机中进行存储、识别和实时显示。算法的FPGA实现采用流水线架构和浮点运算设计来提高信号的实时处理能力和数据计算精度。实测结果表明,该系统能够实时分离多路复数域混合机会信号。     

一种卫星导航接收机固定多波束抗干扰方法

自适应波束形成抗干扰性能受先验信息、通道幅相误差等因素影响较大,在工程应用中实现复杂,鲁棒性较差。针对这一问题,提出了一种卫星导航接收机固定多波束抗干扰方法,该方法将信号空间分为多个子空间,通过最优分配策略选取多个子空间分别实现固定波束指向,并相应地在每个波束后配置独立的卫星捕获跟踪通道组,然后依据最高信噪比准则在所有的子空间中优选卫星进行定位解算。该方法无需先验信息辅助,在抑制干扰信号的同时对卫星信号形成接收增益,在存在工程误差的实际条件下可达到与典型自适应波束形成算法相当的抗干扰性能,且具有鲁棒性强、更易工程实现等优点。最后,通过计算机仿真验证了该方法的有效性。     

高级波束成形在旋转声源定位中的应用

为了使高级波束成形中的函数波束成形、压缩感知波束成形、正交波束成形在旋转声源定位中获得广泛应用,对3种高级波束成形在旋转声源定位中的应用进行了研究,从声源空间分辨率,动态范围以及声源功率积分上与传统旋转波束成形和DAMAS(deconvolution approach for the mapping of acoustic sources)反卷积作对比。仿真与实验结果表明:3种高级波束成形均可以应用到旋转声源定位中,并且均能显著提升旋转声源空间分辨率和动态范围以及拥有较高的计算效率。函数波束成形在低频段的空间分辨率低于DAMAS并且容易产生较高的声源功率积分误差。压缩感知波束成形整体性能与DAMAS相近,并且在低频段的空间分辨率比DAMAS有优势。正交波束成形在低频段容易产生声源定位位置误差,抗干扰能力较弱,并且声源功率积分整体低于DAMAS。      

小波域的自适应波束形成算法

在分析传统自适应波束形成的基础上,首次提出了一种小波域的自适应波束形成算法。与通常的自适应波束形成算法相比,该算法利用小波变换对小波空间进行了分解,信号经小波变换自相关性会下降,收敛速度提高,同时在此分解过程中,根据信号与白噪声在不同尺度上的小波变换模极大值表现完全不同的特性进行信号的消噪。理论分析和仿真结果表明了该算法收敛速度较快,且计算量增加较少,易于实时实现,而且具有较好性能。同时仿真实验表明该算法收敛速度与小波基和尺度的选择有关,尺度越大收敛速度越快;对于同一小波基系列,小波基的正则性越好收敛速度越快。     

An Algebraic Approach to Superresolution Array Processing

In a recent paper we made an algebraic characterization of the problem of resolving closely spaced plane waves incident on a linear array. The characterization encompassing several superresolution processing methods and encompassed the Wiener, maximum likelihood, and Pisarenko methods as well as suggesting new procedures. In this paper we amplify the algebraic approach and extend the results to consider correlated noise. An adaptive algorithm is given for a particularly effective processing method.     

一种改进的声反卷积相关声源定位方法

基于传声器阵列的声源定位算法的性能优劣决定了气动噪声试验检测中气动噪声源定位成像的准确性。为了提高相关声源定位方法的稳健性和准确性，提出一种改进的声反卷积相关声源定位方法——D-MACS（Developed-Mapping of Acoustic Correlated Sources）方法。该方法的特点是：在传统MACS方法的基础上，将声反卷积模型进行改进，使模型体现出声场空间扫描点声压互谱矩阵与互谱波束形成输出矩阵之间的数学关系，其最终获得的声源定位结果，能够提高MACS方法声源定位辨识的稳健性和准确性。通过仿真信号研究和声学风洞环境中的声源定位试验研究，比较了DAS（Delay And Sum beamformming）、DAMAS-C（Deconvolution Approach for the Mapping of Acoustic Correlated Sources）、MACS和D-MACS这4种方法的声源定位性能，验证了D-MACS方法在声源定位性能上的优势。     

Adaptive event-triggered distributed optimal guidance design via adaptive dynamic programming

In this paper, the multi-missile cooperative guidance system is formulated as a general nonlinear multi-agent system. To save the limited communication resources, an adaptive eventtriggered optimal guidance law is proposed by designing a synchronization-error-driven triggering condition, which brings together the consensus control with Adaptive Dynamic Programming(ADP) technique. Then, the developed event-triggered distributed control law can be employed by finding an approximate solution of eve...     

ARMA Time Series Modeling: an Effective Method

The ability to generate rational models of time series plays an important role in such applications as adaptive filtering, spectral estimation, digital control, array processing, and forecasting. A method for effecting an autoregressive moving average (ARMA) model estimate is presented which possesses a number of admirable properties: 1) it has an elegant algebraic structure, 2) its modeling performance in spectral estimation applications has been empirically found to typically exceed that of such contemporary techniques as the periodogram, the Burg method, and the Box-Jenkins method on a variety of problems, 3) it is implementable by computationally efficient algorithms, and 4) it is based on pseudomaximum likelihood concepts. Taken in combination, these properties mark this method as being an effective tool in challenging applications requiring high modeling performance in a real time setting.     

A flexible processor for a digital adaptive array radar

A digital beamforming processor for an adaptive array radar is described. The functionality and the architecture of the processor are strongly driven by a goal of achieving adaptive null depths in the 60-dB to 70-dB range, which necessitates substantial preprocessing of each channel. In particular, conversion to baseband quadrature channels is accomplished digitally using a single A/D converter per channel, and FIR (finite impulse response) equalizing filters are employed in each channel to match channel transfer functions. The processor is highly modular, and this not only distributes the total processing load, but also the I/O (input/output) bandwidth requirement. This is accomplished by distributing the adaptive beamforming algorithm systolically across a linear array of processing nodes. The processor is expandable to a different number of channels and sufficiently flexible to be applied to other problems of an array signal processing nature. Experimental data presented demonstrate that the processor is capable of supporting channel-to-channel cancellation of interfering signals to the level of -65 dB     

WiMAX dynamnic beamforming antenna

This presents our design and development of an 8-channel adaptive beaniforming antenna for new developments in radio technology: the emergence of chip-based components implementing the recently completed IEEE 802.16d-2004 (WiMAX) radio standard [1-4]. WiMAX offers the potential of long-range (up to 50 miles) and high bandwidth (up to 50 Mb~s) radio links. Although the new WiMAX standard offers the potential for using smart, adaptive antennas, this functionality has not been implemented. Our design will serve as a common platform for testing adaptive array algorithms including direction of arrival (DOA) estimation, beamforming, and adaptive tracking, as well as complete wireless communication with a WiMAX Radio. Heavy emphasis will be placed on ease of implementation in a multi-channel / multi-user environment. In the future, we hope to expand this interface to support dynamic radio channel selection via direct interaction with radio hardware or implementation of a cognitive radio system.     

转子振动故障信号的盲分离

介绍了盲源信号分离基本原理。基于最小化互信息原理,建立了用独立分量分析方法估计分离矩阵的加速梯度法,并给出了实现步骤。利用所建立的方法进行振动信号盲源分离的数值仿真,分离后的信号波形与源信号一致。对具有故障的实际转子进行多传感器信号采集并进行盲分离,不同故障的特征被分离开来。分析实例体现了所建立的方法在盲源信号分离中的有效性和对转子故障分离的实用性。      

利用压缩网格方法提高旋转声源定位计算效率

利用两种压缩网格方法:基于传统波束形成的CG2压缩网格方法和基于小样本数据的CG4压缩网格方法,对经典时域旋转声源波束形成(ROSI)算法进行优化加速。实验结果表明:两种压缩网格方法均不影响ROSI算法旋转声源定位效果,基于传统波束压缩形成的CG2网格方法能够提高ROSI算法的旋转声源定位计算效率1～2倍,基于小样本数据的CG4压缩网格方法能够提高ROSI算法的旋转声源定位计算效率13～18倍。除此之外,基于小样本数据的CG4压缩网格方法在相控麦克风阵列平面与旋转声源平面垂直条件下,仍能准确进行旋转声源定位。      

基于四阶累积量和小波降噪的干扰抑制算法

针对盲信号分离算法在低信噪比下分离效果较差的情况,提出了一种基于四阶累积量和小波降噪的含噪盲分离算法,并将该算法用于卫星通信干扰抑制。首先,利用基于四阶累积量的盲分离算法进行信扰盲分离;然后对分离出的已被噪声污染的通信信号进行小波降噪处理。最后,对提出的算法在单音干扰下进行了性能仿真。仿真结果表明,该算法不仅使抗噪性能提高了3dB左右,而且在低信噪比下对强单音干扰具有较好的抑制效果。     

Exact least squares adaptive beamforming using an orthogonalizationnetwork

The pros and cons of various classical and state-of-the-art methods in adaptive array processing are discussed, and the relevant concepts and historical developments are pointed out. A set of easy-to-understand equations for facilitating derivation of any least-squares-based algorithm is derived. Using this set of equations and incorporating all of the useful properties associated with various techniques, an efficient solution to the real-time adaptive beamforming problem is developed     

Adaptive digital beamforming for angle estimation in jamming

A radar digital beamforming (DBF) architecture and processing algorithm is described for nulling the signal from a mainlobe electronic jammer and multiple sidelobe electronic jammers while maintaining monopulse angle estimation accuracy on the target. The architecture consists of a sidelobe jamming (SLJ) cancelling adaptive array (AA) followed by a mainlobe jamming (MLJ) canceller. A mainlobe maintenance (MLM) technique or constrained adaptation during the sidelobe cancellation process is imposed so that the results of the SLJ cancellation process do not distort the subsequent mainlobe cancellation process. The SLJ signals and the MLJ signals are thus cancelled sequentially in separate processes. This technique was developed for improving radar processing in determining the angular location of a target, and specifically for improving the monopulse technique by maintaining the accuracy of the target echo monopulse ratio in the presence of electronic jamming by adaptive suppression of the jamming signals before forming the monopulse sum and difference beams     

Discrete-time observability and estimability analysis for bearings-only target motion analysis

Observability in the context of bearings-only tracking (BOT) is still the subject of important literature. Different from previous approaches, where continuous-time analysis was considered, our approach relies on discrete-time analysis. It is then shown that this allows us to use directly and efficiently the simple formalisms of linear algebra. Using the direct approach, observability analysis is essentially reduced to basic considerations about subspace dimensions. Even if this approach is conceptually quite direct, it becomes more and more complex as the source-encounter scenario complexity increases. For complex scenarios, the dual approach may present some advantages essentially due to the direct use of multilinear algebra. New results about BOT observability for maneuvering sources are thus obtained. Observability analysis is then extended to unknown instants of source velocity changes. Even if observability analysis provides thorough insights about the algebraic structure of the BOT problem, the optimization of the observer maneuvers is essentially a control problem. Basic algebraic considerations prove that a relevant cost functional for this control problem is the determinant of the Fisher information matrix (FIM). So, a large part of this work is devoted to the analysis of this cost functional. Using multilinear algebra, general approximations of this functional are given. In order to involve only directly estimable parameters, the source bearing-rates are examined. Using these approximations, a general framework for optimizing the observer trajectory is derived which allow us to approximate the optimal sequence of controls. It is worth stressing that our approach does not require the knowledge of the source trajectory parameters and is still valid for a maneuvering source.