【作者】 杨志国；

【导师】 徐文；

【作者基本信息】 浙江大学 ， 通信与信息系统， 2013， 博士

【摘要】 水下声源定位与跟踪问题一直以来都受到广泛的关注。传统定位方法研究主要集中于低辐射噪声目标的定位与跟踪,对于这一类目标进行定位与跟踪通常需要大孔径的接收阵与复杂的处理系统。近年来随着自主水下航行器广泛应用于各类水下作业中,对于这一类具有合作声源的运动平台的定位问题逐渐受到关注。现有方法主要是从无线电技术中发展而来,对于水声信道的考虑较少。在深海环境中,水声信号传播模型近似为自由场,这些技术可以很好地实现声源定位。当声源位于浅海环境时,自由场的假设不再成立,环境对于信号传播的作用就变得非常重要,直接影响到现有方法的适用性。水声信号在浅海中传播时会受到水面与水底的反射,形成多径现象。由于水面以及水体的起伏,不仅会导致接收信号时延扩展,同时也会引起多普勒扩展,对于运动声源这种扩展现象更加严重。因此对于声信号而言,浅海环境本质上是一个时延与多普勒双扩展的信道。本论文研究合作声源定位与跟踪的方法,试图在浅海环境中利用小孔径阵实现具有合作声源的运动平台的定位与跟踪。合作声源定位问题既不同于主动声纳目标定位,又区别于被动声纳目标定位,是被动声纳接收声源主动发射的信号。声源发射信号的形式与参数已知,可以作为先验信息。通常自主水下航行器在水下作业时深度变化不大或者定深航行,因此对其定位主要是水平位置的定位,进而可以将三维空间定位问题简化为二维平面定位。声源运动轨迹通常不会是突变的,声源当前时刻的状态往往只与前面若干时刻的状态有关,具有马尔可夫性,具备部分的先验知识的前提下可以通过一定的运动方程描述。声源跟踪问题可以看成声源状态变化这一随机过程的估计,在估计过程中可以通过测量获得一定的新信息。整个问题可以纳入到状态-空间模型下,声源跟踪问题转化为利用一个可观测的输入与可观测的输出寻求声源状态这一不可观测量的最小均方误差估计器。在实际过程中可观测输入项往往无法获得,可以采用观测误差项取代。论文中采用五元均匀线阵作为观测平台,可观测输出项通过五元均匀线阵获得,由两项组成：当前测量时刻的声源方位角及前后两个测量时刻距离变化量。声源方位角可以通过波束形成估计获得,距离变化量与发射、接收脉冲信号时间间隔差有直接关系。当发射脉冲信号时间间隔已知时,估计出接收信号的脉冲时间间隔就可以计算得到距离变化量。给定状态-空间模型以后利用序贯贝叶斯滤波方法实现声源跟踪。浅海环境中信号传播受到波导效应影响,采用传统的互相关等方法直接测量接收脉冲信号时间间隔会带来较大的误差,引入时间反转技术可以改善脉冲信号时间间隔估计精度。时间反转技术是基于声信号在时不变环境中传播的互易性提出的信号处理方法,当环境为时不变或者变化缓慢时采用时反技术可以有效压缩由多径传播引起的信号时延扩展。论文利用被动时反技术恢复接收信号波形,用于估计接收脉冲信号时间间隔,验证了被动时反处理对时延估计精度的改善。但是当环境起伏较快时被动时反技术不再适合,需要发展新的方法。此外,论文研究对象为合作声源,因此通过设计合适的声源发射信号可以进一步改善跟踪算法的适用性。浅海时变信道中,发射信号经过多径传播到达接收阵,声源与接收阵之间的相对运动以及界面和水体的运动导致信号每条传播路径的时延与多普勒扩展均不同,时延-多普勒二维模糊度函数能量集中于几个区域内,表现出多峰现象。由声源与接收阵相对运动引起的时延-多普勒扩展具有一定的稳定和慢变性,由界面以及水体运动引起的时延-多普勒扩展具有一定的随机性。论文借鉴蝙蝠、海豚等使用声纳作为主要探测和识别工具的动物的声学发射特性,通过分析不同信号的时延-多普勒分辨力,选择了在时延维和多普勒维均有较好分辨力的发射信号,进而将接收信号的时延-多普勒模糊度函数作为测量值,对时延-多普勒进行跟踪,从中提取直达波或最先到达信号的时延-多普勒信息。将这一信息作为声源跟踪的状态-空间模型的可观测输出值,在浅海时变信道中实现了对声源的有效跟踪。对于特定的声源运动轨迹,当接收阵保持静止时采用以上方法仍然会出现声源的不可观察现象,需要接收阵运动增加声源的可观察性。论文中在均方误差准则下对接收阵运动轨迹进行了路径搜索,使得在声源以特定轨迹运动时接收阵仍然可以保持对声源的可观察性。论文通过仿真验证了波导环境中合作声源定位与跟踪算法的性能。在时不变波导环境中,验证了时反与跟踪算法结合的跟踪效果。在时变起伏波导环境中验证了时延-多普勒跟踪与跟踪算法相结合的声源跟踪算法性能。对于特殊运动轨迹的声源而言,在最小均方误差准则下仿真生成不同的接收阵运动轨迹验证了接收阵运动对于声源可观察性的改善。论文的研究过程中设计并实现了一套合作声源定位系统,包括模拟声源与接收阵系统。通过水池测试以及海上试验的实际使用,该系统正常稳定工作并采集了有效的实验数据。海试数据处理结果验证了论文中提出的波导环境中时反与跟踪相结合算法的有效性及性能。更多还原

【Abstract】 Passive underwater acoustic source localization and tracking has been of great inter-est for both research community and various application users. Traditional focus is on the detection of target with low-level noise-like self-radiation. The large-aperture receiving ar-ray and complex processing systems are used in these applications. Lately, the localization problem of those moving platforms with a cooperative acoustic source is more widely dis-cussed, as different types of autonomous underwater vehicles become popular in a variety of applications. Most of the localization methods currently used is evolved from the radio technology. Those methods rarely consider the impact introduced by the underwater acous-tic channel. They could work well in deep sea, but the performance will degrade in shallow water because of the waveguide effect. In shallow water environments, fluctuations along the boundaries and in the media will cause severe multi-path time-delay spreading and Doppler spreading, which can be more serious for a moving platform. The shallow water environment is a doubly-spread and rapidly time-varying channel for sound propagation. This thesis is aimed to track the moving platform carrying with a cooperative acoustic source via a small receiving array in shallow water.When tracking a cooperative target, the array received signals from source are d-ifferent from both the active localization and passive localization. The receiver knows the source signals’ parameters for cooperative source tracking. The localization is sim-plified from three-dimension to two-dimension as the Autonomous Underwater Vehicle often move at a constant depth. With the knowledge that the source state always changes continuously as a wide-sense Markov process, we can model the source movement in a kinematic equation. Then the tracking process is the same as random process estimation with some measurements. For the moment, it will suffice to generalize the tracking prob-lem to the state-space model, Given the tracking problem described by the state-space model, we wish to operate on the observable inputs and observable outputs to find the min- imum mean-square-error estimators of the source state which is not directly observable. However, instead of the unavailable inputs, we drive the model with a measurement error. The measurement vector consists of source bearing and range difference, both of which are estimated via a five-element uniform linear array developed in this thesis. The source bearing angle is estimated via beamforming and the range difference is calculated with the time difference of transmitting-receiving interval of signals when the transmitting interval is given. Sequential Bayesian filter is used to realize the tracking when system is described by the state-space model.Because of the waveguide effect for signal propagation in the shallow water environ-ment, the estimation error will be large if traditional estimation methods such as cross-correlation are used to estimate the time interval of receiving pulse signals. The estima-tion accuracy could be improved after time-reversal processing for receiving signal. Time-reversal processing is a signal processing method developed based on the reciprocity of sound propagation in a time-invariant environment. When the environment is stationary or changing slowly, time-delay spreading caused by multi-path propagation can be com-pressed with time-reversal processing. Passive time-reversal is adopted to recover the re-ceiving signal used to estimate time interval of the receiving pulse signals. The performance of the processing is verified. However, in the fluctuating shallow water environment, the performance of the time-reversal method will be limited. For cooperative source tracking problem we focus on in the thesis, the waveform optimization can be adopted to improve the performance of the tracking method.The signals of different paths are encountered with different time-doppler spreading when they propagate in fluctuating time-varying shallow water. The time-doppler ambi-guity function of the receiving signal have an extention around several regions. The time-doppler spread introduced by relative movement between source and receiver is stable or changing slowly compared with that introduced by surface waves and/or internal turbu-lence. Based on the knowledge of acoustic transmitting character of echolocating mammal-s such as bats and dolphins, we adopt the signal with good resolution both in time-delay and doppler scale. Time-doppler spread of the direct arrival signal or first arrival signal is tracked after the computation of time-doppler spread function. Then it is used to calcu-late the measurement vector of the state-space model for tracking the moving source in fluctuating time-varying shallow water.When moving in some particular trajectory, the source can not be tracked by a station-ary observer. The observer need to move with some strategy to keep the observability of the source. The optimal observer moving path is searched in the mean-square-error sense. The source moving in a particular trajectory is observable after the observer’s movement.Localization and tracking of a cooperative moving source in waveguide is verified via simulations in this thesis. The performance of combination of time reversal processing and the tracking method is verified in a time-invariant waveguide. The performance of combination of delay-doppler filtering and the tracking method is verified in a time variant waveguide. For source moving in a particular trajectory, the trajectory of the receiving array is searched based on mean square error in order to improve the observability of the moving source. During the thesis research, a prototype localization system has been designed and built, including a simulated source and a passive sonar system. Through a tank test and sea experiment, the system has shown working reliably while acquiring a mount of valuable real data. The processing results of the experiment data has verified the performance of a combination of time reversal processing and tracking proposed in the thesis.更多还原