基于二阶锥规划的空域维纳滤波

提出了基于二阶锥规划的空域维纳滤波。基于噪声与阵列流型不相关的特性,将阵列的方向向量作为期望通过二阶锥规划方法对阵列快拍进行维纳滤波。利用维纳滤波抑制噪声的能力,提高阵列的信号检测和方位估计性能。通过仿真研究,验证了单目标时,其低信噪比下的信号检测性能优于Bartlett波束形成,其方位估计的信噪比门限要低于Bartlett波束形成和MUSIC算法,因而是一种优良的波束形成器。该波束形成器用于多波束测深仪和侧扫声纳等海洋测绘设备的阵列,可以有效的提高设备的作用距离和测量精度。     

基于二阶锥规划的空域维纳滤波

提出了基于二阶锥规划的空域维纳滤波。基于噪声与阵列流型不相关的特性,将阵列的方向向量作为期望通过二阶锥规划方法对阵列快拍进行维纳滤波。利用维纳滤波抑制噪声的能力,提高阵列的信号检测和方位估计性能。通过仿真研究,验证了单目标时,其低信噪比下的信号检测性能优于Bartlett波束形成,其方位估计的信噪比门限要低于Bartlett波束形成和MUSIC算法,因而是一种优良的波束形成器。该波束形成器用于多波束测深仪和侧扫声纳等海洋测绘设备的阵列,可以有效的提高设备的作用距离和测量精度。     

基于高阶累积量的自适应波束形成

常规波束形成器仅限于利用信号的二阶统计特性。作者采用高阶累积量估计期望信号的导向矢量 (Steering Vector) ,实现了一种基于高阶累积量的自适应波束形成器 (HCAB)。该波束形成器利用信号的更高阶统计特性 ,减少了对阵列流型的依赖 ,具有较好的容差性 ,能自动跟踪信号。数值模拟实验表明该波束形成器工作良好。     

强干扰背景下水中微弱信号的垂直阵波束形成研究

针对垂直阵在强干扰背景下采用常规波束形成方法难以估计目标俯仰角的问题,研究了超波束与逆波束形成等方法在垂直阵中的应用。讨论了超波束与逆波束形成原理,仿真分析了其波束图,表明分辨率比常规波束形成有明显提高。实测数据分析表明：超波束方法可以分辨目标与干扰的俯仰角轨迹,对角加载逆波束 MVDR 方法可以获得目标的俯仰角轨迹。研究结果解决了强干扰背景下微弱目标信号俯仰角难以估计的问题,为微弱目标定位提供了新的途径。     

圆阵低频超增益波束形成用于水下目标探测研究

针对小尺度基阵难以取得低频空间增益的问题,研究了圆阵超增益波束形成技术检测低频目标的方法。利用小尺度基阵低频噪声的空间相关性,求出基阵的相关系数矩阵,该矩阵对常规波束形成的权系数进行加权,产生超增益波束形成器的最优加权向量。计算机仿真与实测数据处表明：超增益波束形成技术能够在较低的工作频率上形成波束,获得更好的探测定位性能,有良好的应用前景。     

基于高阶累积量的简正波声场匹配场定位效果分析

由于传统情况,匹配场处理器主要针对接收信号为高斯信号,而海洋环境中存在大量的有色噪声,因此设计基于高阶累积量的匹配场处理器具有一定价值。本文介绍了一种在海洋声层析环境下用高阶累积量来降低匹配场处理中高斯有色噪声的算法,对比研究了基于高阶累积量的简正波声场匹配场处理器、传统线性匹配场处理器、自适应匹配场处理器及二阶MUSIC算法匹配场处理器在相同声场环境条件下的匹配场定位效果。研究结果表明,在不考虑参数失配与计算量前提下,接收信号经过基于高阶累积量的简正波声场匹配场处理器后再进行匹配场定位,能有效降低旁瓣,且使用高阶累积量可显著消除海洋传播环境中高斯有色噪声和高斯白噪声对信号的干扰,其匹配效果优于另外三种匹配场处理器。     

浅水多波束测深仪高分辨率波束形成方法研究

常规波束形成技术以其稳健性好、计算量低等特点得到广泛应用,但其空间分辨率受阵元个数限制,不能突破瑞利限。因此,常规波束形成技术应用于高精度浅水多波束测深仪时,存在空间分辨率不足和旁瓣干扰等问题。对比研究了最小方差无失真(MVDR)及多重信号分类(MUSIC)波束形成在浅水多波束测深仪中的应用,给出了浅水多波束测深仪的常规波束形成、MVDR和MUSIC处理方法,并结合能量法和相位法两种底检测测深算法,处理了iBeam8120浅水多波束测深仪外场数据,验证了本文方法的性能。     

一种基于稳健自适应波束形成的声隔离技术

介绍了一种基于稳健自适应波束形成的声隔离技术.通过分析声诱饵工作时干扰与信号的主要入射方位,建立了边发边收声诱饵模型.针对STMV波束形成器在基阵模型失配情况下容易出现信号自消的现象,研究了一种解决波束形成稳健性的新方法,即Robust Capon Beamforming(RCB)算法,实现有效的干扰抑制和信号提取.仿...     

基于虚拟仪器的水下目标二维角估计

利用LabVIEW软件,通过8通道数据采集卡和均匀圆阵对水下目标的噪声进行采集和处理。结合一维直线阵波束形成理论,实现了对水下目标二维方向角估计实验研究和算法验证。实验证明利用虚拟仪器方便地实现了对水声信号的采集、处理,以及在方位估计时,为传感器布阵和算法的确定提供参考。     

深弹自导过程的波束相关处理仿真

深弹精确自导受测向测距精度影响,为探究自导全过程算法,采用波束域相关处理方法,开展深弹自导过程中自导算法的测向测距性能研究.首先,对方法的可行性进行分析,确定噪声背景下有效混响抑制方法.进而,假设深弹运动态势和弹目的初始位置,根据几何态势推算出运动过程中的弹目位置和目标俯仰角,并加入随机误差模拟弹体旋转.最后,仿真计算...     

ROC evaluation of adaptive beamforming in a simulated shallow waterenvironment

An optimal evaluation of adaptive beamforming techniques in a reverberation-limited shallow water environment is presented. A comprehensive simulation, using the sonar simulation toolset (SST) software in conjunction with the generic sonar model (GSRT) software, is used to create realistic beam data complete with target, noise, and reverberation. Adaptive beamforming techniques from the recursive least squares (RLS) family are applied to enhance detection performance via interference rejection. Two techniques are considered: linearly constrained beamforming using the minimum variance distortionless response (MVOR) beamformer and constrained adaptive noise cancelling (ANC) using the joint process least squares lattice (JPLSL) algorithm. Target detection trials, summarized in the form of receiver operator characteristics (ROC), are used to evaluate performance of the two adaptive beamformers. Results demonstrate mixed performance in reverberation-limited shallow water environments     

Adaptive port-starboard beamforming of triplet sonar arrays

For a low-frequency active sonar (LFAS) with a triplet receiver array, it is not clear in advance which signal processing techniques optimize its performance. Here, several advanced beamformers are analyzed theoretically, and the results are compared to experimental data obtained in sea trials. Triplet arrays are single line arrays with three hydrophones on a circular section of the array. The triplet structure provides the ability to solve the notorious port-starboard (PS) ambiguity problem of ordinary single-array receivers. More importantly, the PS rejection can be so strong that it allows to unmask targets in the presence of strong coastal reverberation or traffic noise. The theoretical and experimental performance of triplet array beamformers is determined in terms of two performance indicators: array gain and PS rejection. Results are obtained under several typical acoustic environments: sea noise, flow noise, coastal reverberation, and mixtures of these. A new algorithm for (beam space) adaptive triplet beamforming is implemented and tuned. Its results are compared to those of other triplet beamforming techniques (optimum and cardioid beamforming). These beamformers optimize for only one performance indicator, whereas in theory, the adaptive beamformer gives the best overall performance (in any given environment). The different beamformers are applied to data obtained with an LFAS at sea. Analysis shows that adaptive triplet beamforming outperforms conventional beamforming algorithms. Adaptive triplet beamforming provides strong PS rejection, allowing the unmasking of targets in the presence of strong directional reverberation (e.g., from a coast) and at the same time provides positive array gain in most environments.     

Towed array shape estimation using frequency-wavenumber data

Towed array beamforming algorithms require accurate array shape information in order to perform properly. Very often, these algorithms assume the array is linear. Unfortunately, the mechanical forces on the array due to ship motion and sea dynamics can change the shape of the array, which degrades the performance of the beamforming algorithm. A data-driven approach to estimating the relative shape of a nominally linear array is presented. The algorithm is robust in that it optimally combines information contained in a wide band of frequencies and source bearings. At the heart of the algorithm is a maximum-likelihood (ML) estimation scheme. The Cramer-Rao lower bound is derived and compared to the performance of the ML estimator. The utility of the algorithm is verified using both simulated and actual towed array data experiments     

A Study of Spatial Processing Gain in Underwater Acoustic Communications

Spatial processing, including beamforming and diversity combining, is widely used in communications to mitigate intersymbol interference (ISI) and signal fading caused by multipath propagation. Beamforming suppresses ISI (and noise) by eliminating multipath (and noise) arrivals outside the signal beam. Beamforming requires the signals to be highly coherent between the receivers. Diversity combining combats ISI as well as signal fading by taking advantage of the independent information in the signal. Classical (spatial) diversity requires that signals are independently fading, hence are (spatially) uncorrelated with each other. In the real world, the received signals are neither totally coherent nor totally uncorrelated. The available diversity is complex and not well understood. In this paper, we study the spatial processing gain (SPG) as a function of the number of receivers used, receiver separation, and array aperture based on experimental data, using beamforming and multichannel combining algorithms. We find that the output symbol signal-to-noise ratio (SNR) for a multichannel equalizer is predominantly determined by the array aperture divided by the signal coherence length, with a negligible dependence on the number of receivers used. For a given number of receivers, an optimal output symbol SNR (OSNR) is achieved by spacing the receivers equal to or greater than the signal coherence length. We model the SPG in decibels as the sum of the noise suppression gain (NSG, equivalent to signal-to-noise enhancement) and the ISI suppression gain (ISG, equivalent to signal-to-ISI enhancement) both expressed in decibels; the latter exploits the spatial diversity and forms the basis for the diversity gain. Data are interpreted using the modeled result as a guide. We discuss a beam-domain processor for sonar arrays, which yields an improved performance at low-input SNR compared to the element-domain processor because of the SNR enhancement from beamforming many sensors.     

Improved time-delay estimates of underwater acoustic signals usingbeamforming and prefiltering techniques

Passive sonar systems that localize broadband sources of acoustic energy estimate the difference in arrival times (or time delays) of an acoustic wavefront at spatially separated hydrophones, The output amplitudes from a given pair of hydrophones are cross-correlated, and an estimate of the time delay is given by the time lag that maximizes the cross correlation function. Often the time-delay estimates are corrupted by the presence of noise. By replacing each of the omnidirectional hydrophones with an array of hydrophones, and then cross-correlating the beamformed outputs of the arrays, the author shows that the effect of noise on the time-delay estimation process is reduced greatly. Both conventional and adaptive beamforming methods are implemented in the frequency domain and the advantages of array beamforming (prior to cross-correlation) are highlighted using both simulated and real noise-field data. Further improvement in the performance of the broadband cross-correlation processor occurs when various prefiltering algorithms are invoked     

Adaptive beamforming of a towed array during a turn

During maneuvering, towed array beamforming degrades if a straight array is assumed. This is especially true for high-resolution adaptive beamforming. It is experimentally demonstrated that adaptive beamforming is feasible on a turning array, provided that array shape is estimated. The array shape can be inferred solely from the coordinates of the tow vessel's Global Positioning System (GPS) without any instrumentation in the array. Based on estimated array shape from the GPS, both the conventional beamformer and the white noise constrained (WNC) adaptive beamformer are shown to track the source well during a turn. When calculating the weight vector in the WNC approach, a matrix inversion of the cross-spectral density matrix is involved. This matrix inversion can be stabilized by averaging the cross-spectral density matrix over neighboring frequencies. The proposed algorithms have been tested on real data with the tow-vessel making 45/spl deg/ turns with a 500-m curvature radius. While turning, the improvement in performance over the assumption of a straight array geometry was up to 5 dB for the conventional beamformer and considerably larger for the WNC adaptive beamformer.     

The TAP III beamforming system

The Three-Array Processor (TAP III) beamforming system incorporating both wide-band time-domain beamforming and narrow-band frequency-domain beamforming is described. This paper briefly develops the beamforming theory and shows how the fast Fourier transform (FFT) is applied to accomplish frequency-domain beamforming. The frequency-domain beamformer operates in the frequency domain to form beams and power spectrum data over narrow frequency bands of interest. A real-time digital filtering technique is used to extract the narrow bands of interest from the broad-band input signal. The frequency-domain beamformer accomplishes real-time digital filtering and beamforming by using a high-speed array processor to do the complex calculations and data handling required by the algorithm. The time-domain beamformer operates in parallel with the frequency-domain beamformer to form up to 16 broad-band beams in the time domain. A programmable all-pass digital filter is used to create the fine time delays required by the time-domain beamformer.     

Robust adaptive unscented Kalman filter for bearings-only tracking in three dimensional case

This paper proposes an improved version of Unscented Kalman Filter (UKF), namely Robust Adaptive UKF (RAUKF), with a special focus on Bearings-Only Target Tracking for three-dimensional case (3DBOT). The automatic tuning of the noise covariance matrices and the robust estimation of the target states form a critical point for the performance of the Kalman-type filtering algorithms, especially in the variable environmental conditions exposed in underwater. The key idea of the proposed filter is to combine robust aspects of UKF and adaption of the process and measurement noise covariance matrices with low computational complexity. The main contribution of this paper is to adjust these matrices by means of the steepest descent algorithm, and the H_∞ technique is embedded to achieve superior performance in terms of accuracy and robustness against initial conditions and model uncertainties. Different experiments are performed to evaluate the performance of the proposed algorithm in the 3DBOT problem with a single moving observer. Simulations demonstrate that the proposed filter produce more accurate results with satisfactory computational burden in comparison with other methods.