多波束测深系统高分辨力底检测：算法研究与系统实现
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摘要
海底地形的测量是海洋环境调查、资源开发及航行保障等工作的基础,主要任务是测量海底深度,并进一步绘制海底地形图。多波束测深系统利用广角度定向发射和多通道接收技术,形成条带状的高密度水深数据,是一项高技术海底地形测量手段。近五十年来,多波束测深技术不断完善,并持续朝着宽覆盖、高分辨以及高精度的方向发展。
多波束测深系统作为一种水声探测设备,其性能最终取决于测量过程背后的物理机制。系统接收的海底后向散射回波信号在空间和时间上都是连续分布的,相对于传统声纳研究的点目标而言,回波信号严重展宽,底部对应的回波信号特征不明显,信号的分辨和特征参数的估计都更为复杂,对底部回波信号检测／估计性能的分析更具困难。
多波束测深中的底检测处理属于含有未知参量的检测问题,应用时回波参量估计与水底检测是联合进行的,一方面估计中需用到检验为真的信号样本,另一方面检测判决需要有效参量估计的支撑。底检测处理中的目标为水底,面临的问题主要包括到达角度/到达时间估计、后向散射信号检测和条带测量模型变化检测。
论文以提高边缘波束测量精度和水底测深数据可靠性为目的,以国家863计划课题中的多波束浅海地形测量系统为研究平台,通过估计-检测-模型联动的方式,围绕着影响测线的三个主要因素(测点位置、测点深度以及测线模型),对多波束测深中的关键技术——波束形成处理、底检测处理和测深数据的模型检测展开研究工作。
首先针对多波束测深系统的应用环境,构建了回波信号的传播模型。对于浅海环境,信号在近距离情况时以球面波的形式传播,此时按照平面波模型处理,将会导致测点位置出现偏差。论文发展了近场聚焦处理与远场常规处理相结合的波束形成方法,以提高波束定向的精确度以及测点的定位精度；针对近场聚焦算法计算量大,很难实时处理的问题,提出了近似的聚焦波束形成算法,并仿真研究了近似聚焦的波束模式图与理想情况的差别,验证了近似算法的有效性。
测点的深度由回波信号的到达时间和到达角度两个待估参量决定,其中回波到达角度通常根据预成的波束角度获得,对于边缘的倾斜波束,脚印明显展宽,导致测深精度严重下降。针对边缘波束脚印内地形起伏导致回波信号表现出的点状特征,论文采用高分辨力方位估计算法分辨波束宽度内的多向回波信号,获得亚波束宽度的分辨性能。具体提出了波束域多指向的基于旋转不变技术的信号参数估计方法(Estimation of Signal Parameters via Rotational Invariance Techniques,简写为ESPRIT),降低了此类算法对信噪比以及样本点数的要求,且计算复杂度降低。试验数据处理结果表明,与传统的加权平均时间(Weighted Mean Time,简写为WMT)或者方位偏离指示(Bearing Deviation Indicator,简写为BDI)底检测算法相比,波束域多指向ESPRIT算法能够提供更好的细节分辨效果。
另一方面,系统覆盖范围/分辨力要求、安装空间限制等因素使得多波束系统多采用特殊形状的接收阵列,导致基于均匀线阵假设的高分辨力方位估计算法无法直接应用,同时回波信号的短时平稳性使得算法所需的协方差矩阵的估计较为困难,增加了高分辨力方位估计算法的应用复杂度。论文通过将虚拟阵列变换、多子阵空间前后向平滑方法与波束域多指向ESPRIT算法相结合,得到适用于任意形状平面阵的高分辨力底检测算法,计算机仿真以及试验数据分析结果均验证了算法的有效性。
回波信号的到达时间和到达角度可转化为水底的测点数据。由于各种干扰的影响,测点数据中存在着异常值,同时测点分布的不均匀性也不利于获得连续的水底测线。论文提出利用最小二乘支持向量机对条带的测点数据进行建模,根据模型对测深数据进行筛选,剔除异常值,进一步提高模型的拟合精度。试验数据分析表明,对水底地形建模既可以填补测量间隙,又能够实现多种算法测量结果的融合处理,提高测点数据的可靠性。
论文在理论研究的基础上完成了WMT和BDI两种底检测算法的信号处理机的实现。通过多次水池测试、湖上以及海上试验的实际使用,验证了信号处理机及其算法实现的有效性和可靠性。
Information on bottom topography is the foundation of numerous human ocean activities including marine environmental investigation, resource exploitation and navigation security. The goal of a bathymetry measurement system is to measure the water depth and further map the seabed topography. As such a high-tech instrument, multi-beam echo sounders (MBES) can generate high-density strip depth measurements by exploiting wide-swath directional transmitting and multi-channel receiving. During the past 50 years, the MBES technology has been improved significantly; it continues to improve toward even wider coverage, higher resolution and better precision.
As an underwater acoustic system, performance of the MBES is ultimately determined by the physical mechanism of the measurement process. The backscattering signal of the seabed is continuously distributed in both space and time. Compared to the case of point objects studied in conventional sonar applications, detection and estimation of signal parameters in MBES are more complex, and the associated performance analysis is even more challenging.
The bottom detection process in MBES is a hypothesis testing problem with unknown parameters, i.e., signal detection is jointly done with parameter estimation. On one hand, data samples, for which the hypothesis testing is true, are needed in parameter estimation; on the other hand, detection is supported by effective estimation results. Here, the target is the seabed, and the problem facing includes target direction-of-arrival (DOA) and time-of-arrival (TOA) estimation, backscattering signal detection, and model-based change detection.
This thesis is to improve the depth accuracy of the outer beams and the reliability of the measurement results, using the shallow-water MBES system funded by the national 863 program as the research platform. Through an interactive linkage among detection, estimation and model, considering main factors impacting the measurement lines (the position and depth of measuring points, and the seabed model), the thesis research focuses on three key aspects of MBES signal processing:beamforming. bottom detection, and model-based bottom change detection.
Firstly, a seabed backscattering signal model is developed. For a shallow-water environment, the signal in the near distance propagates in the form of a spherical wave; in this case, use of the plane wave model will result in some deviation of the measured position. As such, a combination of the near-field focused beamforming and plane wave beamforming is presented to make the beams pointed more precisely and improve the localization accuracy. Considering the large amount of calculation required for focused beamforming, the thesis proposes an approximate algorithm. The difference between the approximate and the ideal beam patterns is discussed; and simulations validate the effectiveness of the approximate algorithm.
The depth of measuring point is determined by DOA and TOA estimation. The estimation of DOA can be obtained from the beam angles; however, broadening of the footprints of the outer inclined beams reduces the resolution significantly and affects the accuracy of depth estimation. In fact, the topographic relief within the footprints of the inclined beams makes the echo present point-like features. Hence the high resolution algorithms can be used to resolve simultaneously echo signals and obtain a sub-beamwidth resolution. In this thesis, estimation of signal parameters via rotational invariance techniques (ESPRIT) with multiple-angle subarray beamforming is developed to reduce the requirement for the signal-to-noise ratio (SNR), the number of snapshots, and computational efforts. Experimental data processing results show that, compared to the traditional bottom detection algorithms such as weighted mean time (WMT) and bearing deviation indicator (BDI), ESPRIT with multiple-angle subarray beamforming can provide better fine-structure resolution.
On the other hand, because of the system coverage/resolution requirement and the space constraint of installation, an MBES usually employs a receiving array of special shape, which leads to that the high resolution algorithms based on a uniform linear array (ULA) can not be applied directly. Meanwhile the short-term stationary of the echo signals makes it difficult to estimate the covariance matrix required, further increasing the complexity in applying a high-resolution algorithm. In this thesis, a high resolution bottom detection algorithm is developed, which can be used on a planar array of any shape, by combining the ESPRIT with multiple-angle subarray beamforming with virtual array transformation and multiple subarray forward-backward spatial smoothing. Computer simulations and experimental data processing results verify the effectiveness of the developed algorithm.
The DOA and TOA estimation can be converted to the horizontal distance and depth of a measuring point. Due to the impact of operational errors and external interferences, there are outliers in the results of bottom detection, while the uneven distribution of measuring points is not conducive to obtaining a continuous bottom profile. In the thesis, the LS-SVM (least squares support vector machine) approach is studied to model the bottom profile. Based on this model, the outliers are removed to further improve the fitting precision. Experimental data processing results show that the model can not only fill the measurement gap, but also achieve the fusion of the results with a variety of algorithms, and improve the reliability of the results.
During the thesis research, two kinds of bottom detection algorithms—WMT and BDI have been designed on a digital signal processor for the MBES. Numerous tank tests, lake and sea trials have demonstrated that the signal processor and its algorithms can work effectively and reliably.
多波束测深系统作为一种水声探测设备,其性能最终取决于测量过程背后的物理机制。系统接收的海底后向散射回波信号在空间和时间上都是连续分布的,相对于传统声纳研究的点目标而言,回波信号严重展宽,底部对应的回波信号特征不明显,信号的分辨和特征参数的估计都更为复杂,对底部回波信号检测／估计性能的分析更具困难。
多波束测深中的底检测处理属于含有未知参量的检测问题,应用时回波参量估计与水底检测是联合进行的,一方面估计中需用到检验为真的信号样本,另一方面检测判决需要有效参量估计的支撑。底检测处理中的目标为水底,面临的问题主要包括到达角度/到达时间估计、后向散射信号检测和条带测量模型变化检测。
论文以提高边缘波束测量精度和水底测深数据可靠性为目的,以国家863计划课题中的多波束浅海地形测量系统为研究平台,通过估计-检测-模型联动的方式,围绕着影响测线的三个主要因素(测点位置、测点深度以及测线模型),对多波束测深中的关键技术——波束形成处理、底检测处理和测深数据的模型检测展开研究工作。
首先针对多波束测深系统的应用环境,构建了回波信号的传播模型。对于浅海环境,信号在近距离情况时以球面波的形式传播,此时按照平面波模型处理,将会导致测点位置出现偏差。论文发展了近场聚焦处理与远场常规处理相结合的波束形成方法,以提高波束定向的精确度以及测点的定位精度；针对近场聚焦算法计算量大,很难实时处理的问题,提出了近似的聚焦波束形成算法,并仿真研究了近似聚焦的波束模式图与理想情况的差别,验证了近似算法的有效性。
测点的深度由回波信号的到达时间和到达角度两个待估参量决定,其中回波到达角度通常根据预成的波束角度获得,对于边缘的倾斜波束,脚印明显展宽,导致测深精度严重下降。针对边缘波束脚印内地形起伏导致回波信号表现出的点状特征,论文采用高分辨力方位估计算法分辨波束宽度内的多向回波信号,获得亚波束宽度的分辨性能。具体提出了波束域多指向的基于旋转不变技术的信号参数估计方法(Estimation of Signal Parameters via Rotational Invariance Techniques,简写为ESPRIT),降低了此类算法对信噪比以及样本点数的要求,且计算复杂度降低。试验数据处理结果表明,与传统的加权平均时间(Weighted Mean Time,简写为WMT)或者方位偏离指示(Bearing Deviation Indicator,简写为BDI)底检测算法相比,波束域多指向ESPRIT算法能够提供更好的细节分辨效果。
另一方面,系统覆盖范围/分辨力要求、安装空间限制等因素使得多波束系统多采用特殊形状的接收阵列,导致基于均匀线阵假设的高分辨力方位估计算法无法直接应用,同时回波信号的短时平稳性使得算法所需的协方差矩阵的估计较为困难,增加了高分辨力方位估计算法的应用复杂度。论文通过将虚拟阵列变换、多子阵空间前后向平滑方法与波束域多指向ESPRIT算法相结合,得到适用于任意形状平面阵的高分辨力底检测算法,计算机仿真以及试验数据分析结果均验证了算法的有效性。
回波信号的到达时间和到达角度可转化为水底的测点数据。由于各种干扰的影响,测点数据中存在着异常值,同时测点分布的不均匀性也不利于获得连续的水底测线。论文提出利用最小二乘支持向量机对条带的测点数据进行建模,根据模型对测深数据进行筛选,剔除异常值,进一步提高模型的拟合精度。试验数据分析表明,对水底地形建模既可以填补测量间隙,又能够实现多种算法测量结果的融合处理,提高测点数据的可靠性。
论文在理论研究的基础上完成了WMT和BDI两种底检测算法的信号处理机的实现。通过多次水池测试、湖上以及海上试验的实际使用,验证了信号处理机及其算法实现的有效性和可靠性。
Information on bottom topography is the foundation of numerous human ocean activities including marine environmental investigation, resource exploitation and navigation security. The goal of a bathymetry measurement system is to measure the water depth and further map the seabed topography. As such a high-tech instrument, multi-beam echo sounders (MBES) can generate high-density strip depth measurements by exploiting wide-swath directional transmitting and multi-channel receiving. During the past 50 years, the MBES technology has been improved significantly; it continues to improve toward even wider coverage, higher resolution and better precision.
As an underwater acoustic system, performance of the MBES is ultimately determined by the physical mechanism of the measurement process. The backscattering signal of the seabed is continuously distributed in both space and time. Compared to the case of point objects studied in conventional sonar applications, detection and estimation of signal parameters in MBES are more complex, and the associated performance analysis is even more challenging.
The bottom detection process in MBES is a hypothesis testing problem with unknown parameters, i.e., signal detection is jointly done with parameter estimation. On one hand, data samples, for which the hypothesis testing is true, are needed in parameter estimation; on the other hand, detection is supported by effective estimation results. Here, the target is the seabed, and the problem facing includes target direction-of-arrival (DOA) and time-of-arrival (TOA) estimation, backscattering signal detection, and model-based change detection.
This thesis is to improve the depth accuracy of the outer beams and the reliability of the measurement results, using the shallow-water MBES system funded by the national 863 program as the research platform. Through an interactive linkage among detection, estimation and model, considering main factors impacting the measurement lines (the position and depth of measuring points, and the seabed model), the thesis research focuses on three key aspects of MBES signal processing:beamforming. bottom detection, and model-based bottom change detection.
Firstly, a seabed backscattering signal model is developed. For a shallow-water environment, the signal in the near distance propagates in the form of a spherical wave; in this case, use of the plane wave model will result in some deviation of the measured position. As such, a combination of the near-field focused beamforming and plane wave beamforming is presented to make the beams pointed more precisely and improve the localization accuracy. Considering the large amount of calculation required for focused beamforming, the thesis proposes an approximate algorithm. The difference between the approximate and the ideal beam patterns is discussed; and simulations validate the effectiveness of the approximate algorithm.
The depth of measuring point is determined by DOA and TOA estimation. The estimation of DOA can be obtained from the beam angles; however, broadening of the footprints of the outer inclined beams reduces the resolution significantly and affects the accuracy of depth estimation. In fact, the topographic relief within the footprints of the inclined beams makes the echo present point-like features. Hence the high resolution algorithms can be used to resolve simultaneously echo signals and obtain a sub-beamwidth resolution. In this thesis, estimation of signal parameters via rotational invariance techniques (ESPRIT) with multiple-angle subarray beamforming is developed to reduce the requirement for the signal-to-noise ratio (SNR), the number of snapshots, and computational efforts. Experimental data processing results show that, compared to the traditional bottom detection algorithms such as weighted mean time (WMT) and bearing deviation indicator (BDI), ESPRIT with multiple-angle subarray beamforming can provide better fine-structure resolution.
On the other hand, because of the system coverage/resolution requirement and the space constraint of installation, an MBES usually employs a receiving array of special shape, which leads to that the high resolution algorithms based on a uniform linear array (ULA) can not be applied directly. Meanwhile the short-term stationary of the echo signals makes it difficult to estimate the covariance matrix required, further increasing the complexity in applying a high-resolution algorithm. In this thesis, a high resolution bottom detection algorithm is developed, which can be used on a planar array of any shape, by combining the ESPRIT with multiple-angle subarray beamforming with virtual array transformation and multiple subarray forward-backward spatial smoothing. Computer simulations and experimental data processing results verify the effectiveness of the developed algorithm.
The DOA and TOA estimation can be converted to the horizontal distance and depth of a measuring point. Due to the impact of operational errors and external interferences, there are outliers in the results of bottom detection, while the uneven distribution of measuring points is not conducive to obtaining a continuous bottom profile. In the thesis, the LS-SVM (least squares support vector machine) approach is studied to model the bottom profile. Based on this model, the outliers are removed to further improve the fitting precision. Experimental data processing results show that the model can not only fill the measurement gap, but also achieve the fusion of the results with a variety of algorithms, and improve the reliability of the results.
During the thesis research, two kinds of bottom detection algorithms—WMT and BDI have been designed on a digital signal processor for the MBES. Numerous tank tests, lake and sea trials have demonstrated that the signal processor and its algorithms can work effectively and reliably.
引文
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