改进的形态学运算在声纳图像生成中的应用

声纳技术应用日益广泛,已成为海洋测量的重要工具,而去除噪声处理是对声纳图像进行正确判读的前提,本文采用了改进的形态学算法减少声纳图像中噪声的影响,对图像进行预处理,采用Matlab图形图像处理工具对处理的算法进行仿真的方式,从而使处理效果达到最佳。     

多普勒声纳系统原理及应用

多普勒声纳已成功应用于多种水下载体的导航。首先分析了水下导航的特点,推导了四波束配置多普勒声纳速度矢量的最小二乘估计,给出了误差速度的计算方法和物理意义,最后介绍了多普勒声纳在水下导航中的应用。     

基于模糊神经网络的移动机器人沿墙导航控制

移动机器人沿墙导航控制包含了追踪和避障两种情况,是移动机器人研究中的常见问题。它是指机器人在一定方向上沿墙运动,或者更一般意义上的沿着物体轮廓运动,并与墙保持一定距离。移动机器人利用声纳采集机器人与墙体的距离和角度信息,通过模糊神经网络将输入数据进行融合,从而判断移动机器人的位姿信息,输出左右轮速度控制其动作。实验证明此方法可以有效地保证移动机器人在安全距离内沿墙体运动。对比采用模糊神经网络前后的实验,采用后的移动机器人沿墙导航控制轨迹优于采用前,均方误差大大减小。     

水下无人装备声呐发射机自动测试系统设计

针对某型水下无人装备声呐发射机的工作原理和现有声呐发射机检测方式落后,检测能力不足等问题,设计了基于国产PXI总线仪器平台的水下无人装备声呐发射机自动测试系统;首先分析了发射机的工作原理和检测需求,进行声呐发射机自动测试系统的总体设计;其次介绍自动测试系统的软硬件平台实现方法和关键技术,包括国产PXI总线仪器选型、接口单元设计、测试平台软件架构设计和实现以及数据处理算法设计和实现等技术;最后对声呐发射机自动测试系统的试验结果进行分析;应用结果表明：该系统能实现某型水下无人装备声呐发射机主要参数的测量,相较传统测试方法,自动测试系统能够节约大量的测试时间与人力,实现测试工作效果的最大化提高,并且还能防止人为因素对测试结果造成的不良影响。     

基于DDS技术和FPGA的声纳发射机模拟器设计

设计一个声纳发射机的模拟器,其工作原理基于DDS技术和FPGA。该模拟器能够输出四路脉冲宽度、重复周期、起始相位、起始频率、终止频率、相对时延均可控制的线性调频正弦脉冲信号。     

Countering loss of vigilance in sonar watchstanding using signal injection and performance feedback

Sonar operators are confronted with a watchstanding task that demands high levels of vigilance for the appearance of weak or transitory signals. Maintaining vigilance is difficult because of very low target signal rates and an open loop system with (usually) no performance feedback. Four experiments were conducted to see whether operator vigilance, as reflected by target detection latency, could be enhanced through signal injection and performance feedback. In each of these experiments, target detection performance was markedly enhanced. The effects were operationally and statistically significant and generally increased with time on watch. The beneficial effects were shown not to be simply a function of increased signal rate due to signal injection. Analysis using a signal detection theory model showed that the target reporting threshold dropped under the experimental treatment and detection efficiency increased. In addition, it was shown that the subjects spent significantly more time observing the search display with signal injection and feedback. On the negative side, there was a modest increase in false alarms which was judged to be tolerable in view of the marked reduction in target detection times. Most false alarms were quickly recognized and reported as such. These beneficial effects were confirmed in a fifth experiment using trained sonar operators as subjects and prototype displays of an advanced sonar system.     

Comparative analysis of different approaches to target differentiation and localization with sonar

This study compares the performances of different methods for the differentiation and localization of commonly encountered features in indoor environments. Differentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target tracking. Different representations of amplitude and time-of-flight measurement patterns experimentally acquired from a real sonar system are processed. The approaches compared in this study include the target differentiation algorithm, Dempster-Shafer evidential reasoning, different kinds of voting schemes, statistical pattern recognition techniques (k-nearest neighbor classifier, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering algorithm), and artificial neural networks. The neural networks are trained with different input signal representations obtained using pre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen's self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications.     

掩埋小目标声探测技术研究

探测和识别沉底、掩埋水雷等小目标在军事上显得愈来愈迫切。在介绍掩埋小目标探测声纳现状的基础上,剖析了掩埋小目标声探测的技术难点,总结了小目标声探测技术的发展趋势,主要包括探测频率向低频发展,重点发展合成孔径探测技术,积极探索时反探测和MIMO探测方法,小目标识别技术向联合利用图像特征和散射特征的方向发展等,对沉底/掩埋小目标声探测技术研究及其声纳设计有借鉴指导意义。     

A sonar approach to obstacle detection for a vision-based autonomous wheelchair

An advanced prototype Computer Controlled Power Wheelchair Navigation System or CCPWNS has been developed to provide autonomy for highly disabled users, whose mix of disabilities makes it difficult or impossible to control their own power chairs in their homes. The working paradigm is “teach and repeat” a mode of control for typical industrial holonomic robots. Ultrasound sensors, which during subsequent autonomous tracking will be used to detect obstacles, also are active during teaching. Based upon post-processed data collected during this teaching event, elaborate trajectories–which may involve multiple direction changes, pivoting and so on, depending upon the requirements of the typically restricted spaces within which the chair must operate–will later be called upon by the disabled rider. An off-line postprocessor assigns an ultrasound profile to the sequence of poses of any taught trajectory. Use of this profile during tracking obviates most of the inherent problems of using ultrasound to avoid obstacles while retaining the ability to near solid objects, such as when passing through a narrow doorway, where required by the environment and trajectory objectives. The work in this article describes a procedure to obtain consistent maps of sonar boundaries during the teaching process, and a preliminary approach to use this information during the tracking phase. The approach is illustrated by results obtained by using the CCPWNS prototype.     

Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation

This study investigates the processing of sonar signals with ensemble neural networks for robust recognition of simple objects such as plane, corner and trapezium surface. The ensemble neural networks can differentiate the target objects with high accuracy. The simplified fuzzy ARTMAP (SFAM) and probabilistic ensemble simplified fuzzy ARTMAP (PESFAM) are compared in terms of classification accuracy. The PESFAM implements an accurate and effective probabilistic plurality voting method to combine outputs from multiple SFAM classifiers. Five benchmark data sets have been used to evaluate the applicability of the proposed ensemble SFAM network. The PESFAM achieves good accuracy based on the twofold cross-validation results. In addition, the effectiveness of the proposed ensemble SFAM is delineated in sonar target differentiation. The experiments demonstrate the potential of PESFAM classifiers in offering an optimal solution to the data-ordering problem of SFAM implementation and also as an intelligent classification tool in mobile robot application.