Phase-shift-based time-delay estimators for proximity acousticsensors

Time-delay estimation is important in a wide range of applications in oceanic engineering. In this paper, we present a novel time-delay estimation algorithm based on maximum likelihood theory for the case that the measurements are corrupted by colored or nonuniform zero-mean Gaussian noise. It turns out that the likelihood function associated with the problem is highly oscillatory, and we propose a computationally efficient technique to maximize this function. Our algorithm first obtains an initial estimate based on a smooth approximate cost function, and then refines this estimate based on the true cost function. Simulation results show that our estimator outperforms a traditional phaseshift based estimator, and that the estimation error approaches the Cramer-Rao bound when the SNR increases without bound     

Effects of sensor placement on acoustic vector-sensor arrayperformance

We consider the role played by the sensor locations in the optimal performance of an array of acoustic vector sensors, First we derive an expression for the Cramer-Rao bound on the azimuth and elevation of a single far-field source for an arbitrary acoustic vector-sensor array in a homogeneous wholespace and show that it has a block diagonal structure, i.e., the source location parameters are uncoupled from the signal and noise strength parameters. We then derive a set of necessary and sufficient geometrical constraints for the two direction parameters, azimuth and elevation, to be uncoupled from each other. Ensuring that these parameters are uncoupled minimizes the bound and means they are the natural or “canonical” location parameters for the model. We argue that it provides a compelling array design criterion. We also consider a bound on the mean-square angular error and its asymptotic normalization, which are useful measures in three-dimensional bearing estimation problems. We derive an expression for this bound and discuss it in terms of the sensors' locations. We then show that our previously derived geometrical conditions are also sufficient to ensure that this bound is independent of azimuth. Finally, we extend those conditions to obtain a set of geometrical constraints that ensure the optimal performance is isotropic     

Experimental measurement of the array bearing error caused bybathymetric refraction

Experimental measurements have been carried out to identify the effects of a realistic bottom bathymetry on detecting a source with a horizontal line array. The measurements have been conducted over a 1:10000 scale model of the Santa Lucia Escarpment. The experiments measured the bearing error obtained in locating a CW source with a horizontal line array using plane-wave beamforming. The error in the detected bearing is caused by the bending of sound rays in the horizontal plane, commonly referred to as horizontal or bathymetric refraction. The results of the experiment demonstrate large bearing errors which vary rapidly and do not increase monotonically with range. The rapid variation of the bearing errors was unexpected and was a result which has not been previously identified. The magnitude of the errors was also a function of the across slope look direction and frequency, with bearing errors as large as 26° for low frequencies. At higher frequencies, the bearing error is reduced. An analytical solution for the acoustic field over a shear supporting sloping bottom has been used to approximate the results of the experiment. The simulation confirmed the trends in the experimental results by showing bearing errors of the same order of magnitude with the same dependence on across slope direction and frequency. Most importantly, the theoretical results also showed a rapidly varying bearing error as a function of across slope range     

On the ability to estimate narrow-band signal parameters usingtowed arrays

Using the Cramer-Rao lower bound (CRLB) as an indicator of potential performance, the limits on the estimation and resolution capabilities of a towed line array of uniformly spaced hydrophones to provide frequency and bearing information about narrowband signals are examined. It is assumed that a monochromatic plane wave arrives at the array for each source. Several versions of the bounds are computed using different assumptions about which parameters have known values and about the way in which the samples are taken in space and in time. It is shown that the CRLB values for different situations can be compared to provide information about the effective use of a moving aperture for estimation of the parameters of narrowband signals arriving at the array. It is also shown that adding at least one hydrophone occupying a fixed position in space can improve the bearing estimates of a towed array by supplying additional frequency information if both the bearings and frequencies of the sources are unknown     

HF radar ocean current algorithm based on MUSIC and the validation experiments

Wuhan University's ocean state measuring and analyzing radar (OSMAR2000), working at around 7.5 MHz in the low region of the HF band with a 120-m-long linear receiving antenna array, can measure ocean surface current at ranges of up to 200 km. An ocean surface current algorithm based on direction finding (DF) using the multiple signal classification (MUSIC) method is developed for the OSMAR2000 radar. This paper describes the OSMAR2000 ocean surface current algorithm based on MUSIC and the validation experiments in the East China Sea. The results of the ocean surface current measurements demonstrate that the OSMAR2000 ocean surface current algorithm based on MUSIC is feasible for the long range of ocean surface current mapping with a sufficient bearing resolution.     

导航雷达近海回波图像丢帧内插技术研究

研究了在连续的X波段导航雷达图像序列中丢失一帧图像状态下的一种快速修复算法.该算法主要采用基于运动估计与运动补偿的视频序列修复技术进行插帧修复.具体步骤是根据雷达图像中像素点的运动特点,采用改进的三步搜索算法并结合双向运动估计进行搜索,然后对矢量场进行后续处理,最后内插出重建帧.为了提高图像修复的精度,还引入了亚像素运...     

Four-element CODAR beam forming

An HF radar called the Coastal Ocean Dynamics Applications Radar (CODAR) is presently being used in several forms to measure ocean surface parameters. The original version was developed by the National Oceanic and Atmospheric Administration (NOAA) and utilizes a four-element receive array. The array consists of four equally spaced elements arranged on a circle with a radius of 0.2151 wavelengths (at 25.4 MHz). It was designed to measure ocean currents using a direction-finding technique based on an extension to a simple two-element interferometer. The problem of determining the bearing of a radiating source can be readily shown to be equivalent to that incurred in spectral estimation. In an attempt to improve upon the processing of existing data, modern nonlinear spectral estimation techniques are applied in a beam-forming bearing estimation procedure and compared against several direction-finding algorithms. Enhancement of bearing estimators via analysis of the eigenstructure of a spatial correlation matrix is included. Antenna response patterns are calculated and used to investigate properties of direction-finding algorithms. Simulated data are used for a comparison of direction finding and beam forming. The asymmetrical bias of each method is investigated to determine its effect on the error in estimating the angle of arrival of a radar target.     

FFT-based joint bearing estimation

The least-squares estimate for the joint estimation of M directions-of-arrival in an array-processing scenario is rederived in a way that makes explicit use of the discrete Fourier transform. It is shown that the M-dimensional search algorithm can be made orders of magnitude faster than the traditional search algorithm. This new approach is compared via simulation with conventional narrowband beamforming.     

The maximum likelihood estimator for acoustic synthetic apertureprocessing

The maximum likelihood estimator of source amplitude, bearing, and frequency for a moving towed line array of equispaced elements is discussed. A two-dimensional search in equivalent phase and frequency variables is found to yield the best estimates of the unknown parameters. Application to a physical experiment and comparison with the Cramer-Rao bound verify the unity of the approach     

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     

面向应用的成像光谱数据分类技术

提出了面向应用的成像光谱数据分类技术,旨在从根本上、全方位地削弱各种不利因素对分类精度的影响。该项技术主要包括利用IEM算法获取更为精确的类别分布信息,采用禁忌搜索算法进行原始特征空间的降维,运用基于混合规则的组合分类器来判断待识样本的类别标签。实验表明,按照该方法进行成像光谱数据的分类处理,可以得到很高精度的分类结果。     

Matched field inversion for geoacoustic model parameters usingadaptive simulated annealing

A method is described for the estimation of geoacoustic model parameters by the inversion of acoustic field data using a nonlinear optimization procedure based on simulated annealing. The cost function used by the algorithm is the Bartlett matched-field processor (MFP), which related the measured acoustic field with replica fields calculated by the SAFARI fast field program. Model parameters are perturbed randomly, and the algorithm searches the multidimensional parameter space of geoacoustic models to determine the parameter set that optimizes the output of the MFP. Convergence is driven by adaptively guiding the search to regions of the parameter space associated with above-average values of the MFP. The performance of the algorithm is demonstrated for a vertical line array in a shallow water enviornment where the bottom consists of homogeneous elastic solid layers. Simulated data are used to determine the limits on estimation performance due to error in experimental geometry and to noise contamination. The results indicate that reasonable estimates are obtained for moderate conditions of noise and uncertainty in experimental geometry     

基于A星算法的无人机路径规划优化模型研究

在分析传统无人机路径规划方法应用局限的基础上,针对传统A星算法启发函数单一、拐点冗余,以及效率不够高等系列问题,以虚拟城市地理环境为研究背景,通过提出面向起讫节点方位角的A星算法加速优化、启发式搜索中动态加权重构评价函数的A星算法加速优化和顾及Floyd算法插点策略的A星算法路径优化等层次递进的算法优化策略,解决了对A星算法搜索方向的量化约束、搜索权重数值的优化重构及冗余拐点的插点删除等技术问题,实现了改进后的A星算法运行效率的显著提高及最短路径判定准确性的明显提升。     

基于径向基神经网络的水下自主航行器寻源算法研究

针对水下航行器在二维信号场中的场源搜索问题,提出了一种基于径向基函数神经网络（radial basis function neural network,简称RBFNN）的在线自主寻源算法。在神经网络中引入全局正则化参数以保证泛化性和稳定性,通过最小化广义交叉验证误差（generalized cross-validation,简称GCV）进行正则化参数的迭代优化,并利用增量式奇异值分解（incremental SVD）对迭代过程进行加速,此外通过基于样本新颖性的资源分配网络算法（resource-allocating network,简称RAN）进行径向基函数的分配,在此基础上使用动量梯度算法进行航行器运动方向的规划。最后,以热泉区硫化氢浓度分布场中的搜索作业为背景,使用该算法与其他研究中的算法进行单峰值信号场的场源搜索模拟计算对比,结果显示该算法对于信号场梯度的估计更加准确,且搜索过程的路径更短。此外在多峰值信号场的寻源模拟中该算法能够以较高的成功率通过局部最大值区域。证明该算法具有良好的拟合、预测性能以及稳定性,并且能在一定程度上避免陷入局部最优解。     

A CPSO-SVM Model for Ultimate Bearing Capacity Determination

In this study, the CPSO-SVM models, which combine chaotic system, particle swarm optimization (PSO) and support vector machine (SVM), are presented and applied to predict the ultimate bearing capacity of shallow foundations. Chaotic mapping enjoys certainty, ergodicity and the stochastic property. Chaotic PSO (CPSO) increases the convergence rate of PSO and precision of the results through introducing chaos mapping into the particle swarm optimization algorithm. Since the selection of parameters for SVM is crucial to its performance of prediction, the CPSO is adopted to search for the optimal parameters. The proposed methods are used to predict the ultimate bearing capacity of shallow foundations based on data of load tests. Results indicate that the proposed methods can appropriately describe the relationship between ultimate bearing capacity and its affective factors, and make good predictions.     

基于滚动圆模型的线要素综合方法

针对当前滚动圆模型大多采用双向缓冲区模型进行构建,其在缓冲区构建过程中涉及了大量缓冲区叠置计算处理,算法较为复杂的不足,提出一种基于滚动圆模型的线要素综合方法。从滚动圆的定义出发,分析了利用滚动圆模型综合曲线时遇到的几种基本情形,计算并推导了相应的计算公式;最后通过实验验证了本文方法的有效性。     

Passive Doppler-bearing tracking using a pseudo-linear estimator

In this paper an explicit pseudo-linear estimator for Doppler-bearing tracking is proposed. It overcomes the problems with the bias of earlier pseudo-linear estimators and with the nonlinear frequency measurement equation by using another representation than the Cartesian one and by using the logarithm of the frequency, respectively. It is fast, inherently stable, and easy to implement. The Doppler shift is, for a nonmoving own-ship, determined by the target velocities, while the bearing rate is determined by the same velocities divided by the range. The special representation in this paper uses this difference in behavior to give fast and bias free estimation of the range. Instead of iterating a weighted feast squares problem using bearing and frequency measurements simultaneously, the true bearings and the course are estimated in a bearings only step followed by a frequency only step, which estimates range and frequency. The range estimate then gives the speed estimate. Modifications for scenarios with multiple emitted frequency lines and/or for frequency lines that disappear during parts of a scenario are shown     

Tracking a moving vessel from bearing measurements

This paper presents a method for tracking a distant moving target using only bearing measurements obtained from a tracking platform. The method is an improvement of the Hinich-Bloom passive tracking approach presented in [1]. The target is assumed to be moving at constant speed on a fixed heading, whereas the platform maneuvers during the measurement period. The direction cosines of the bearings are computed with respect to a rotation of the coordinate system that places 0° at the mean estimated target bearing. This is done to minimize the approximation bias due to the linearization of sine bearing as a function of inverse range and time. The coordinate system is rotated back to estimate the target coordinates. When the noise is Gaussian, the estimates of target range and heading are approximately maximum likelihood when the target's relative range is slowly varying during the observation period. In this case the mean square errors of the target parameter estimates are the smallest achievable within the order of the approximation.     

带乘性噪声的一类非线性系统的滤波算法

针对带乘性噪声的一类非线性系统,给出了1种带单重渐消因子的强跟踪状态滤波算法。该算法将非线性系统线性化后,采用了线性最小方差估计方法来进行状态估计,通过运用正交原理和引入渐消因子,使得滤波效果具有强跟踪的优良性能。该算法扩展了卡尔曼滤波在带乘性噪声非线性系统状态估计中的应用范围。仿真结果表明了该算法的有效性。     

A closed-form solution to bearings-only target motion analysis

Bearings-only target motion analysis is a nonlinear state estimation problem in which the noise corrupted angle of arrival measurements of an emitted signal are used to obtain estimates of the source's range, bearing, course, and speed. The estimation process is complicated by unusual observability properties that render the quality of the estimate highly dependent on both the measurement noise levels and the source-observer geometry. Solutions that use recursive Kalman filtering approach or batch-style algorithms have been reported. The nonlinear batch style estimators for this process require iterative solution methods and under certain scenarios can be sensitive to initial conditions. Pseudolinear solutions that alleviate some of the difficulties with the iterative batch algorithms have been proposed. Although early versions of the pseudolinear filter suffered from biased estimates, subsequent improvements appear to have reduced the bias problem. This paper discusses a new pseudolinear solution based on the observable parameters from individual data segments defined by periods of constant observer velocity (termed “legs”). This solution is a true closed-form solution to the bearings-only target motion analysis problem. Although theoretically interesting, the technique does suffer under conditions of poor observability. A practical pseudolinear estimate, that does not suffer from the same observability problems, is developed and related to the first solution. Algorithm performance results, obtained from computer simulation, are presented. For the scenarios examined, the technique provides good state estimates under conditions of high observability. As observability conditions deteriorate, the solution does develop biases. However, it may still be useful for initializing an iterative nonlinear batch-style estimation algorithm