Sonar tracking of multiple targets using joint probabilistic data association

The problem of associating data with targets in a cluttered multi-target environment is discussed and applied to passive sonar tracking. The probabilistic data association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, a new theoretical result is presented: the joint probabilistic data association (JPDA) algorithm, in which joint posterior association probabilities are computed for multiple targets (or multiple discrete interfering sources) in Poisson clutter. The algorithm is applied to a passive sonar tracking problem with multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, two or more acoustic states, and realistic (i.e., low) probabilities of detection at each sample time. A simulation result is presented for two heavily interfering targets illustrating the dramatic tracking improvements obtained by estimating the targets' states using joint association probabilities.     

地波雷达目标检测跟踪联合处理粒子滤波方法

针对固定粒子数PF-TBD算法计算量大、复杂环境下地波雷达海上船只目标检测与跟踪性能不佳的问题,本文将粒子滤波方法应用于地波雷达船只目标检测与跟踪中,提出了基于自适应粒子滤波的地波雷达目标检测与跟踪联合处理方法。该方法结合地波雷达回波谱中目标展宽特性,充分利用了地波雷达回波谱中面目标的粒子权重信息来设置粒子自适应采样策略,提高了目标检测和跟踪联合处理的效果。通过地波雷达实测数据的目标跟踪结果及与同步AIS信息的比对分析,结果表明:提出的检测跟踪联合处理方法在对低信噪比、快速机动等复杂环境下的多目标跟踪时,可提高目标整体跟踪性能。     

Joint direction-of-arrival and array-shape tracking for multiplemoving targets

The problem of tracking the directions-of-arrival (DOAs) of multiple moving sonar targets with an array of passive sensors is complicated by sensor movement. An algorithm for the joint tracking of source DOAs and sensor positions is presented to address this problem. Initial maximum-likelihood estimates of source DOAs and sensor positions are refined by Kalman filtering. Spatio-temporally correlated array movement is considered. Source angle dynamics are used to achieve correct data association. The new technique is capable of performing well for the difficult cases of sources that cross in angle as well as for fully coherent sources. Computer simulations show that the approach is robust in the presence of array motion modeling uncertainty and effectively reduces dependence on expensive and possibly unreliable hardware     

基于水声环境空间中多模态深度融合模型的目标识别方法研究

随着对水下目标特性研究的深入和声学探测技术的发展,基于单模态的阵列式信息融合或基于空间信息的分布式信息融合的水下目标识别方法研究已有一定成果,但针对复杂海况导致单一物理场或单一融合层次的系统识别性能提高有限等方面影响的水下目标识别方法研究还有所不足,因此,开展基于多模态深度融合模型的水下目标识别方法研究可利用模态互补,共享信息而提升识别率。文中在国内外研究基础上,深入研究了基于到达时差法和多模态方法组合的检测方法,初步形成了基于水声环境空间中多模态深度融合模型的识别框架,开展了海洋中典型自然与人为事件的信号分析与特征提取,并在此基础上,设计新型基于海底基站的被动识别系统。该系统同步记录和由位置等组成的时间序列标记声、磁和压数据,可实现高精度、高分辨率的识别。本研究可满足未来海洋观测对高性能水下目标探测、定位和跟踪系统的迫切需要,为海洋安全监管、海洋突发事件应急响应等领域提供新的技术手段和科学参考。     

Robust regression-based EKF for tracking underwater targets

In underwater target tracking applications, measurement uncertainty and inaccuracies are usually modeled as additive Gaussian noise. The Gaussian model of noise may not be appropriate in many practical systems. The non-Gaussian noise and the model non-linearity arising in a tracking system will seriously affect the tracking performance. This paper discusses one way to create a robust version of the extended Kalman filter for enhanced underwater target tracking. State estimation in the filter is done through the robust regression approach and Welsch's proposal is used in the regression process. Monte Carlo simulation results with heavy-tailed contaminated observation noise demonstrate the robustness of the proposed estimation procedure     

Target parameter estimation using measurements acquired with a small number of sensors

A performance prediction procedure is developed and applied to the evaluation of a passive tracking technique intended primarily for the localization of targets in the near field or vicinity of the sensors. The analysis is sufficiently general to be applied to underwater and air acoustics, passive radar, and electromagnetic direction finding systems. Since near field applications are of primary concern, localization parameter identifiability with a single pair of omni-directional sensors is established with the aid of the Fisher Information Matrix (FIM). The Fisher Information Matrix is also used to determine upper bounds on localization performance, and the corresponding uncertainty ellipses associated with target position are evaluated for various tracking scenarios and types of measurements. Emphasis is placed on the use of measurements such as time difference of arrival and frequency difference of arrival obtained with two sensors, and frequency estimates obtained with a single sensor. It is shown that under certain conditions the time difference of arrival measurements yield full localization information, even though the conditioning can be marginal. Additional measurements, such as frequency, are shown to improve localization performance significantly. Bearing measurements obtained with a closely spaced cluster of a few sensors are also considered.     

A Simplified Subspace Fitting Method for Estimating Shape of a Towed Array

Although the existing array shape estimation methods using reference sources demonstrate good performance, most of the methods require at least two reference sources 2RS in known locations and the reference sources must be in far field. This communication proposes a simplified subspace fitting method that uses only a single reference source 1RS. In the proposed method, the horizontal and vertical positions of each sensor are modeled by applying fixed intersensor spacing and sensor angles, so that the unknown parameters of the cost function are reduced. Furthermore, the Cramer–Rao lower bound (CRLB) is investigated for the proposed method. The results of the numerical experiments indicate that the proposed method is practical for estimating the shape of a towed array.      

基于AIS信息校准的双频地波雷达的船只融合跟踪

高频地波雷达（HFSWR）和自动船只确认系统（AIS）是船只跟踪的重要传感器。高频地波雷达可以用来跟踪探测区域的所有船只,而AIS只能用来确认合作船只的信息。由于海杂波的干扰,使用单频率地波雷达的船只跟踪会淹没在布拉格峰值的盲区里,改变探测频率是克服这一缺点的有效手段。在这种背景下,我们提出一种基于AIS校准的双频雷达融合探测算法。因为不同频率的地波雷达测量与AIS的测量值存在系统误差,所以AIS信息可以用来估计和校准地波雷达的每个频率的系统误差。首先,将合作目标的点迹测量与地波雷达的点迹测量通过JVC分配算法进行点迹关联。从合作船只的点迹关联结果中,双频雷达的系统误差可以估计和校准。其次,基于校准的双频雷达数据,使用融合JPDA-UKF算法进行船只跟踪。通过真实探测的数据的实验结果显示所提算法可以实时跟踪船只,相比单频率跟踪可以进一步提高跟踪能力和跟踪精度。     

A neural approach to the assignment algorithm for multiple-targettracking

A neural network is presented for performing data association for multiple-target tracking on an optimal assignment basis, i.e., the sum of likelihood functions of measurement-to-track file associations is optimized. The likelihoods are shown to be derivable from a Kalman filter, which updates and maintains the track files from the measurements assigned by the neural network. Not only are measurements assigned to track files on an optimal basis, but undetected targets and unassigned measurements are identified also. A multiple-target tracking system utilizing the neural network, in conjunction with Kalman filtering, can also automatically delete and initiate track files. The solution to the data association problem, and therefore the design of the neural network, is based on the minimization of a properly defined energy function. Computer simulations indicate the ability of the neural network to converge quickly to the optimal hypothesis under various conditions, provided that the ambiguity in the scenario is not extreme. The computational complexity involved is moderate     

On the determination of directional wave spectra for practical applications

The performance of various directional instruments for practical oceanographic and coastal engineering applications is examined. The emphasis is put on the application of conventional current meters equipped with high resolution pressure sensors and three element arrays. Two simulation techniques have been used to produce input data with known frequency spectrum and known directional spreading. The directional spreading is determined by the maximum likelihood method and the resulting spreading is compared with the input spreading. The performance of a conventional current meter equipped with a high resolution pressure sensor depends on the width of the directional spreading of surface waves and on the frequencies under consideration. Even for very narrow directional spreading, the current meter response is acceptable for practical applications and for shallow water deployment. In general, the current meter directional response does not depend on the direction of the incident waves. The spatial array of three wave staffs deployed in shallow water shows a similar performance to that of the current meters when the dimension of the spatial array is of the order of 1 m. This performance also does not depend on the direction of the incident waves.     

高频地波雷达海上目标航迹跟踪新思路

高频地波雷达是对海上运动目标进行监视监测的一种重要手段,为了提高地波雷达对海上特定目标独立跟踪探测时的性能,本文对高频地波雷达海上目标跟踪技术的研究现状进行了综述,分析总结了目前航迹跟踪方法存在的主要问题。结合海上目标跟踪的实际应用需求,借助目前流行的深度学习方法充分挖掘其他同步探测手段获取的目标信息,提出了基于知识辅助的特定目标跟踪方法,改善后续地波雷达对特定目标独立跟踪时的航迹质量,初步的航迹跟踪结果验证了提出方法的有效性。提出的地波雷达特定目标跟踪方法对目标跟踪方法的理论研究及地波雷达目标跟踪系统的业务化应用均具有重要意义及参考价值。     

Nonlinear adaptive control of an underwater towed vehicle

This paper addresses the problem of simultaneous depth tracking and attitude control of an underwater towed vehicle. The system proposed uses a two-stage towing arrangement that includes a long primary cable, a gravitic depressor, and a secondary cable. The towfish motion induced by wave driven disturbances in both the vertical and horizontal planes is described using an empirical model of the depressor motion and a spring-damper model of the secondary cable. A nonlinear, Lyapunov-based, adaptive output feedback control law is designed and shown to regulate pitch, yaw, and depth tracking errors to zero. The controller is designed to operate in the presence of plant parameter uncertainty. When subjected to bounded external disturbances, the tracking errors converge to a neighbourhood of the origin that can be made arbitrarily small. In the implementation proposed, a nonlinear observer is used to estimate the linear velocities used by the controller thus dispensing with the need for costly sensor suites. The results obtained with computer simulations show that the controlled system exhibits good performance about different operating conditions when subjected to sea-wave driven disturbances and in the presence of sensor noise. The system holds promise for application in oceanographic missions that require depth tracking or bottom-following combined with precise vehicle attitude control.     

Object detection and tracking method of AUV based on acoustic vision

This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow (underwater acoustic images) is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the Gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.     

Underwater Navigation Based on Real-Time Simultaneous Sound Speed Profile Correction

Precise sound speed profile (SSP) information is critical when using sonar for underwater terrain navigation. Nevertheless, acquiring SSP information in real-time is difficult, especially in underwater navigation environment. To account for this problem, this paper presents an underwater navigation method that applies real-time SSP correction to the terrain measurements. The method uses a probe to measure the surface sound speed simultaneously. Meanwhile, redundancy of topographic measurement data is exploited to derive the equivalent SSP information. The acquired equivalent SSP is updated continuously with particle filter algorithm. The terrain measurements can be corrected in real-time with the equivalent SSP to improve the performance of underwater terrain navigation. By removing the impact of inaccurate SSP from the terrain measurements, the proposed method can achieve precise and robust underwater navigation result without using an underway-profiling instrument. Simulated results confirm the good performance of the proposed method.     

Acoustic model-based matched filter processing for fadingtime-dispersive ocean channels: theory and experiment

It is shown that the performance of a conventional matched filter can be improved if the reference (replica) channel compensates for the distortion by the ocean medium. A model-based matched filter is generated by correlating the received signal with a reference channel that consists of the transmitted signal convolved with the impulse response of the medium. The channel impulse responses are predicted with a broadband propagation model using in situ sound speed measured data and archival bottom loss data. The relative performance of conventional and model-based matched filter processing is compared for large time-bandwidth-product linear-frequency-modulated signals propagating in a dispersive waveguide. From ducted propagation measurements conducted in an area west of Sardinia, the model-based matched filter localizes the depths of both the source and receiving array and the range between them. The peak signal-to-noise ratio for the model-based matched filter is always larger than that of the conventional filter     

基于方向信息约束的遥感平台强跟踪定位算法

为了提高机载遥感平台在应对气流抖动和外界强干扰时的定位性能,在加速度自适应截断正态分布的基础上,通过Lagrange乘子法将速度方向信息约束到强跟踪滤波中,得到了基于方向信息约束的强跟踪定位算法。仿真表明:该算法通过在线控制使输出残差强制近似正交,充分利用了方向约束信息。与一般的强跟踪算法相比较,在提高定位精度的同时可及时响应空中机动,较好地解决了机载遥感平台在强机动干扰条件下的跟踪定位问题。     

Curved shape reconstruction using multiple hypothesis tracking

Panoramic sweeps produced by a scanning range sensor often defy interpretation using conventional line-of-sight models, particularly when the environment contains curved, specularly reflective surfaces. Combining multiple scans from different vantage points provides geometric constraints necessary to solve this problem, but not without introducing new difficulties. Existing multiple scan implementations, for the most part, ignore the data correspondence issue. The multiple hypothesis tracking (MHT) algorithm explicitly deals with data correspondence. Given canonical observations extracted from raw scans, the MHT applies multiple behavior models to explain their evolution from one scan to the next. This technique identifies different topological features in the world to which it assigns the corresponding measurements. We apply the algorithm to real sonar scans generated specifically for this investigation. The experiments consist of interrogating a variety of two-dimensional prismatic objects, standing on end in a 1.2-m-deep freshwater tank, from multiple vantage points using a 1.25 MHz profiling sonar system. The results reflect the validity of the algorithm under the initial assumptions and its gradual performance degradation when these assumptions fail to characterize the environment adequately. We close with recommendations that detail extending the approach to handle more natural underwater settings     

Simulation of an Inertial Acoustic Navigation System With Range Aiding for an Autonomous Underwater Vehicle

This paper presents an integrated navigation system for underwater vehicles to improve the performance of a conventional inertial acoustic navigation system by introducing range measurement. The integrated navigation system is based on a strapdown inertial navigation system (SDINS) accompanying range sensor, Doppler velocity log (DVL), magnetic compass, and depth sensor. Two measurement models of the range sensor are derived and augmented to the inertial acoustic navigation system, respectively. A multirate extended Kalman filter (EKF) is adopted to propagate the error covariance with the inertial sensors, where the filter updates the measurement errors and the error covariance and corrects the system states when the external measurements are available. This paper demonstrates the improvement on the robustness and convergence of the integrated navigation system with range aiding (RA). This paper used experimental data obtained from a rotating arm test with a fish model to simulate the navigational performance. Strong points of the navigation system are the elimination of initial position errors and the robustness on the dropout of acoustic signals. The convergence speed and conditions of the initial error removal are examined with Monte Carlo simulation. In addition, numerical simulations are conducted with the six-degrees-of-freedom (6-DOF) equations of motion of an autonomous underwater vehicle (AUV) in a boustrophedon survey mode to illustrate the effectiveness of the integrated navigation system.     

Particle-Velocity-Field Difference Smoothing for Coherent Source Localization in Spatially Nonuniform Noise

This communication considers the problem of estimating 2-D directions of arrival (DOAs) of multiple coherent signals under spatially nonuniform noise (spatially inhomogeneous temporary white noise) using an array of vector hydrophones. A novel preprocessing method called particle-velocity-field difference smoothing (PVFDS) is proposed. The key idea underlying the PVFDS is to remove the spatially nonuniform noise by using the matrix difference of pairs of particle-velocity data correlation matrices, and to decorrelate the coherent signals by summing these difference correlation matrices. Unlike most of other existing preprocessing techniques, such as spatial smoothing and forward–backward averaging, the PVFDS processing does not decrease the array aperture. For arbitrary array geometries, the PVFDS can resolve up to four coherent signals, and for centro–symmetric arrays, forward–backward averaging can double this number to eight. Monte Carlo simulations illustrate that the PVFDS-based eigenstructure algorithms can offer better performance than the particle-velocity-field smoothing (PVFS)-based counterparts.      

基于CT非线性模型的水下目标跟踪算法比较

针对水下目标跟踪非线性跟踪精度问题,假设目标机动模型为恒转速运动模型,贝叶斯框架下,因扩展卡尔曼滤波跟踪方法进行模型在估计点的泰勒展开,忽略一阶以上高阶项,存在模型误差,比较了扩展卡尔曼滤波、无迹卡尔曼滤波、容积卡尔曼滤波在高斯噪声干扰下滤波误差均方根,以及3种方法运行时间。仿真证明,非线性系统下状态维度为5,容积卡尔曼滤波跟踪的精度高于无迹卡尔曼滤波,无迹卡尔曼滤波高于扩展卡尔曼滤波。该研究为海上目标非线性测量系统提供仿真实例,为进一步滤波算法改进提供基础。