Side-scan sonar(SSS)is essential for acquiring high-resolution seafloor images over large areas,facilitat-ing the identification of subsea objects.However,military security restrictions and the scarcity of subsea targ...Side-scan sonar(SSS)is essential for acquiring high-resolution seafloor images over large areas,facilitat-ing the identification of subsea objects.However,military security restrictions and the scarcity of subsea targets limit the availability of SSS data,posing challenges for Automatic Target Recognition(ATR)research.This paper presents an approach that uses Cycle-Consistent Generative Adversarial Networks(CycleGAN)to augment SSS images of key subsea objects,such as shipwrecks,aircraft,and drowning victims.The process begins by constructing 3D models to generate rendered images with realistic shadows frommultiple angles.To enhance image quality,a shadowextractor and shadow region loss function are introduced to ensure consistent shadowrepresentation.Additionally,amulti-resolution learning structure enables effective training,even with limited data availability.The experimental results show that the generated data improved object detection accuracy when they were used for training and demonstrated the ability to generate clear shadow and background regions with stability.展开更多
Underwater shipwreck identification technology, as a crucial technique in the field of marine surveying, plays a significant role in areas such as the search and rescue of maritime disaster shipwrecks. When facing the...Underwater shipwreck identification technology, as a crucial technique in the field of marine surveying, plays a significant role in areas such as the search and rescue of maritime disaster shipwrecks. When facing the task of object detection in shipwreck side-scan sonar images, due to the complex seabed environment, it is difficult to extract object features, often leading to missed detections of shipwreck images and slow detection speed. To address these issues, this paper proposes an object detection algorithm, CSC-YOLO (Context Guided Block, Shared Conv_Group Normalization Detection, Cross Stage Partial with 2 Partial Convolution-You Only Look Once), based on YOLOv8n for shipwreck side-scan sonar images. Firstly, to tackle the problem of small samples in shipwreck side-scan sonar images, a new dataset was constructed through offline data augmentation to expand data and intuitively enhance sample diversity, with the Mosaic algorithm integrated to strengthen the network’s generalization to the dataset. Subsequently, the Context Guided Block (CGB) module was introduced into the backbone network model to enhance the network’s ability to learn and express image features. Additionally, by employing Group Normalization (GN) techniques and shared convolution operations, we constructed the Shared Conv_GN Detection (SCGD) head, which improves the localization and classification performance of the detection head while significantly reducing the number of parameters and computational load. Finally, the Partial Convolution (PConv) was introduced and the Cross Stage Partial with 2 PConv (C2PC) module was constructed to help the network maintain effective extraction of spatial features while reducing computational complexity. The improved CSC-YOLO model, compared with the YOLOv8n model on the validation set, mean Average Precision (mAP) increases by 3.1%, Recall (R) increases by 6.4%, and the F1-measure (F1) increases by 4.7%. Furthermore, in the improved algorithm, the number of parameters decreases by 20%, the computational complexity decreases by 23.2%, and Frames Per Second (FPS) increases by 17.6%. In addition, compared with the advanced popular model, the superiority of the proposed model is proved. The subsequent experiments on real side-scan sonar images of shipwrecks fully demonstrate that the CSC-YOLO algorithm meets the requirements for actual side-scan sonar detection of underwater shipwrecks.展开更多
Underwater target detection in forward-looking sonar(FLS)images is a challenging but promising endeavor.The existing neural-based methods yield notable progress but there remains room for improvement due to overlookin...Underwater target detection in forward-looking sonar(FLS)images is a challenging but promising endeavor.The existing neural-based methods yield notable progress but there remains room for improvement due to overlooking the unique characteristics of underwater environments.Considering the problems of low imaging resolution,complex background environment,and large changes in target imaging of underwater sonar images,this paper specifically designs a sonar images target detection Network based on Progressive sensitivity capture,named ProNet.It progressively captures the sensitive regions in the current image where potential effective targets may exist.Guided by this basic idea,the primary technical innovation of this paper is the introduction of a foundational module structure for constructing a sonar target detection backbone network.This structure employs a multi-subspace mixed convolution module that initially maps sonar images into different subspaces and extracts local contextual features using varying convolutional receptive fields within these heterogeneous subspaces.Subsequently,a Scale-aware aggregation module effectively aggregates the heterogeneous features extracted from different subspaces.Finally,the multi-scale attention structure further enhances the relational perception of the aggregated features.We evaluated ProNet on three FLS datasets of varying scenes,and experimental results indicate that ProNet outperforms the current state-of-the-art sonar image and general target detectors.展开更多
圆周合成孔径声呐通过对成像场景作360°圆周运动获得目标全方位观测信息,以实现水下目标三维高精度成像,其成像效果受无人搭载平台的圆周运动误差影响较大.针对这一问题,根据圆周路径推导设计了基于向心加速度的圆周运动非线性制...圆周合成孔径声呐通过对成像场景作360°圆周运动获得目标全方位观测信息,以实现水下目标三维高精度成像,其成像效果受无人搭载平台的圆周运动误差影响较大.针对这一问题,根据圆周路径推导设计了基于向心加速度的圆周运动非线性制导算法(centripetal acceleration based nonlinear guidance for circular,CANGC),对圆周轨迹贴合度高,具有较高的控制精度.此外,还设计了基于模型预测控制算法的圆周运动控制律,具有较快的控制响应和较强的自适应能力.将两种算法较好地融合,可实现精准圆周轨迹跟踪.其中制导律的控制输出为偏航角速度,因此控制过程中不依赖于无人船磁力计测得的偏航角数据,可以在具有较强磁场影响的条件下使用.通过仿真实验验证了算法的优越性,本文所设计的算法跟踪精度比文献中的算法的跟踪精度高80.1%.湖上实验进一步验证该算法对于圆周运动有较高的控制精度.研究成果为圆周合成孔径声呐成像无人船平台研究提供了算法基础.展开更多
Phase errors in synthetic aperture sonar (SAS) imaging must be reduced to less than one eighth of a wavelength so as to avoid image destruction. Most of the phase errors occur as a result of platform motion errors, fo...Phase errors in synthetic aperture sonar (SAS) imaging must be reduced to less than one eighth of a wavelength so as to avoid image destruction. Most of the phase errors occur as a result of platform motion errors, for example, sway yaw and surge that are the most important error sources. The phase error of a wide band synthetic aperture sonar is modeled and solutions to sway yaw and surge motion estimation based on the raw sonar echo data with a Displaced Phase Center Antenna (DPCA) method are proposed and their implementations are detailed in this paper. It is shown that the sway estimates can be obtained from the correlation lag and phase difference between the returns at coincident phase centers. An estimate of yaw is also possible if such a technique is applied to more than one overlapping phase center positions. Surge estimates can be obtained by identifying pairs of phase centers with a maximum correlation coefficient. The method works only if the platform velocity is low enough such that a number of phase centers from adjacent pings overlap.展开更多
A new monostatic array system taking advantage of diverse waveforms to improve the performance of underwater tar- get localization is proposed. Unlike the coherent signals between different elements in common active a...A new monostatic array system taking advantage of diverse waveforms to improve the performance of underwater tar- get localization is proposed. Unlike the coherent signals between different elements in common active array, the transmitted signals from different elements here are spatially orthogonal waveforms which allow for array processing in the transit mode and result in an extension of array aperture. The mathematical derivation of Capon estimator for this sonar system is described in detail. And the performance of this orthogonal-waveform based sonar is an- alyzed and compared with that of its phased-array counterpart by water tank experiments. Experimental results show that this sonar system could achieve 12 dB-15 dB additional array gain over its phased-array counterpart, which means a doubling of maximum detection range. Moreover, the angular resolution is significantly improved at lower SNR.展开更多
The performance of a sonar system is closely related to the marine environment and the target characteristics. When dealing with the echoes of a traditional active sonar system, the sonar designers often do not take i...The performance of a sonar system is closely related to the marine environment and the target characteristics. When dealing with the echoes of a traditional active sonar system, the sonar designers often do not take into account the influence of the environmental information and prior knowledge perceived by sonar receivers, making it difficult to obtain desired processing results. Based on the basic principle and key technology of sonar, this paper proposed a cognition-based intelligent sonar system in theory--cognitive sonar. Cognitive sonar is capable of jointly optimizing the transmission waveform and receiver according to the changes of environment so that its detection and identification performance can be significantly improved.展开更多
For increasing the cross-track resolution, the multiple input multiple output （MIMO） technique is introduced into the swath bathymetry system and a new swath bathymetry approach using MIMO sonar is proposed. The MIM...For increasing the cross-track resolution, the multiple input multiple output （MIMO） technique is introduced into the swath bathymetry system and a new swath bathymetry approach using MIMO sonar is proposed. The MIMO sonar is composed of two parallel transmitting uniform linear arrays （ULAs） and a receiving ULA which is perpendicular to the former. The spacing between the two transmitting ULAs is equal to the product of the receiving sensor number and the receiving inter-sensor spacing. Furthermore, two narrowband linear frequency modulation （LFM） pulses, sharing the same frequency band but with opposite modulation slopes, are used as transmitting waveforms of the two transmitting ULAs. With such an array layout and transmitting signals, the MIMO sonar can sound a swath with the cross-track resolution doubling that of the traditional multibeam sonar using a Mills cross array. Numerical examples are provided to verify the effectiveness of the proposed approach.展开更多
The maximum likelihood （ML） estimator demonstrates remarkable performance in direction of arrival （DOA） estimation for the multiple input multiple output （MIMO） sonar. However, this advantage comes with prohibit...The maximum likelihood （ML） estimator demonstrates remarkable performance in direction of arrival （DOA） estimation for the multiple input multiple output （MIMO） sonar. However, this advantage comes with prohibitive computational complexity. In order to solve this problem, an ant colony optimization （ACO） is incorporated into the MIMO ML DOA estimator. Based on the ACO, a novel MIMO ML DOA estimator named the MIMO ACO ML （ML DOA estimator based on ACO for MIMO sonar） with even lower computational complexity is proposed. By extending the pheromone remaining process to the pheromone Gaussian kernel probability distribution function in the continuous space, the pro- posed algorithm achieves the global optimum value of the MIMO ML DOA estimator. Simulations and experimental results show that the computational cost of MIMO ACO ML is only 1/6 of the MIMO ML algorithm, while maintaining similar performance with the MIMO ML method.展开更多
Underwater terrain-aided navigation is used to complement the traditional inertial navigation employed by autonomous underwater vehicles during lengthy missions. It can provide fixed estimations by matching real-time ...Underwater terrain-aided navigation is used to complement the traditional inertial navigation employed by autonomous underwater vehicles during lengthy missions. It can provide fixed estimations by matching real-time depth data with a digital terrain map, This study presents the concept of using image processing techniques in the underwater terrain matching process. A traditional gray-scale histogram of an image is enriched by incorporation with spatial information in pixels. Edge comer pixels are then defined and used to construct an edge comer histogram, which employs as a template to scan the digital terrain map and estimate the fixes of the vehicle by searching the correlation peak. Simulations are performed to investigate the robustness of the proposed method, particularly in relation to its sensitivity to background noise, the scale of real-time images, and the travel direction of the vehicle. At an image resolution of 1 m2/pixel, the accuracy of localization is more than 10 meters.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00334159)the Korea Institute of Ocean Science and Technology(KIOST)project entitled“Development of Maritime Domain Awareness Technology for Sea Power Enhancement”(PEA0332).
文摘Side-scan sonar(SSS)is essential for acquiring high-resolution seafloor images over large areas,facilitat-ing the identification of subsea objects.However,military security restrictions and the scarcity of subsea targets limit the availability of SSS data,posing challenges for Automatic Target Recognition(ATR)research.This paper presents an approach that uses Cycle-Consistent Generative Adversarial Networks(CycleGAN)to augment SSS images of key subsea objects,such as shipwrecks,aircraft,and drowning victims.The process begins by constructing 3D models to generate rendered images with realistic shadows frommultiple angles.To enhance image quality,a shadowextractor and shadow region loss function are introduced to ensure consistent shadowrepresentation.Additionally,amulti-resolution learning structure enables effective training,even with limited data availability.The experimental results show that the generated data improved object detection accuracy when they were used for training and demonstrated the ability to generate clear shadow and background regions with stability.
基金supported in part by the Hainan Provincial Natural Science Foundation(Grant No.420CXTD439)Sanya Science and Technology Special Fund(Grant No.2022KJCX83)+1 种基金Institute and Local Cooperation Foundation of Sanya in China(Grant No.2019YD08)National Natural Science Foundation of China(Grant No.61661038).
文摘Underwater shipwreck identification technology, as a crucial technique in the field of marine surveying, plays a significant role in areas such as the search and rescue of maritime disaster shipwrecks. When facing the task of object detection in shipwreck side-scan sonar images, due to the complex seabed environment, it is difficult to extract object features, often leading to missed detections of shipwreck images and slow detection speed. To address these issues, this paper proposes an object detection algorithm, CSC-YOLO (Context Guided Block, Shared Conv_Group Normalization Detection, Cross Stage Partial with 2 Partial Convolution-You Only Look Once), based on YOLOv8n for shipwreck side-scan sonar images. Firstly, to tackle the problem of small samples in shipwreck side-scan sonar images, a new dataset was constructed through offline data augmentation to expand data and intuitively enhance sample diversity, with the Mosaic algorithm integrated to strengthen the network’s generalization to the dataset. Subsequently, the Context Guided Block (CGB) module was introduced into the backbone network model to enhance the network’s ability to learn and express image features. Additionally, by employing Group Normalization (GN) techniques and shared convolution operations, we constructed the Shared Conv_GN Detection (SCGD) head, which improves the localization and classification performance of the detection head while significantly reducing the number of parameters and computational load. Finally, the Partial Convolution (PConv) was introduced and the Cross Stage Partial with 2 PConv (C2PC) module was constructed to help the network maintain effective extraction of spatial features while reducing computational complexity. The improved CSC-YOLO model, compared with the YOLOv8n model on the validation set, mean Average Precision (mAP) increases by 3.1%, Recall (R) increases by 6.4%, and the F1-measure (F1) increases by 4.7%. Furthermore, in the improved algorithm, the number of parameters decreases by 20%, the computational complexity decreases by 23.2%, and Frames Per Second (FPS) increases by 17.6%. In addition, compared with the advanced popular model, the superiority of the proposed model is proved. The subsequent experiments on real side-scan sonar images of shipwrecks fully demonstrate that the CSC-YOLO algorithm meets the requirements for actual side-scan sonar detection of underwater shipwrecks.
基金supported in part by Youth Innovation Promotion Association,Chinese Academy of Sciences under Grant 2022022in part by South China Sea Nova project of Hainan Province under Grant NHXXRCXM202340in part by the Scientific Research Foundation Project of Hainan Acoustics Laboratory under grant ZKNZ2024001.
文摘Underwater target detection in forward-looking sonar(FLS)images is a challenging but promising endeavor.The existing neural-based methods yield notable progress but there remains room for improvement due to overlooking the unique characteristics of underwater environments.Considering the problems of low imaging resolution,complex background environment,and large changes in target imaging of underwater sonar images,this paper specifically designs a sonar images target detection Network based on Progressive sensitivity capture,named ProNet.It progressively captures the sensitive regions in the current image where potential effective targets may exist.Guided by this basic idea,the primary technical innovation of this paper is the introduction of a foundational module structure for constructing a sonar target detection backbone network.This structure employs a multi-subspace mixed convolution module that initially maps sonar images into different subspaces and extracts local contextual features using varying convolutional receptive fields within these heterogeneous subspaces.Subsequently,a Scale-aware aggregation module effectively aggregates the heterogeneous features extracted from different subspaces.Finally,the multi-scale attention structure further enhances the relational perception of the aggregated features.We evaluated ProNet on three FLS datasets of varying scenes,and experimental results indicate that ProNet outperforms the current state-of-the-art sonar image and general target detectors.
文摘圆周合成孔径声呐通过对成像场景作360°圆周运动获得目标全方位观测信息,以实现水下目标三维高精度成像,其成像效果受无人搭载平台的圆周运动误差影响较大.针对这一问题,根据圆周路径推导设计了基于向心加速度的圆周运动非线性制导算法(centripetal acceleration based nonlinear guidance for circular,CANGC),对圆周轨迹贴合度高,具有较高的控制精度.此外,还设计了基于模型预测控制算法的圆周运动控制律,具有较快的控制响应和较强的自适应能力.将两种算法较好地融合,可实现精准圆周轨迹跟踪.其中制导律的控制输出为偏航角速度,因此控制过程中不依赖于无人船磁力计测得的偏航角数据,可以在具有较强磁场影响的条件下使用.通过仿真实验验证了算法的优越性,本文所设计的算法跟踪精度比文献中的算法的跟踪精度高80.1%.湖上实验进一步验证该算法对于圆周运动有较高的控制精度.研究成果为圆周合成孔径声呐成像无人船平台研究提供了算法基础.
文摘Phase errors in synthetic aperture sonar (SAS) imaging must be reduced to less than one eighth of a wavelength so as to avoid image destruction. Most of the phase errors occur as a result of platform motion errors, for example, sway yaw and surge that are the most important error sources. The phase error of a wide band synthetic aperture sonar is modeled and solutions to sway yaw and surge motion estimation based on the raw sonar echo data with a Displaced Phase Center Antenna (DPCA) method are proposed and their implementations are detailed in this paper. It is shown that the sway estimates can be obtained from the correlation lag and phase difference between the returns at coincident phase centers. An estimate of yaw is also possible if such a technique is applied to more than one overlapping phase center positions. Surge estimates can be obtained by identifying pairs of phase centers with a maximum correlation coefficient. The method works only if the platform velocity is low enough such that a number of phase centers from adjacent pings overlap.
基金supported by the National Natural Science Foundation of China(60572098)
文摘A new monostatic array system taking advantage of diverse waveforms to improve the performance of underwater tar- get localization is proposed. Unlike the coherent signals between different elements in common active array, the transmitted signals from different elements here are spatially orthogonal waveforms which allow for array processing in the transit mode and result in an extension of array aperture. The mathematical derivation of Capon estimator for this sonar system is described in detail. And the performance of this orthogonal-waveform based sonar is an- alyzed and compared with that of its phased-array counterpart by water tank experiments. Experimental results show that this sonar system could achieve 12 dB-15 dB additional array gain over its phased-array counterpart, which means a doubling of maximum detection range. Moreover, the angular resolution is significantly improved at lower SNR.
基金Supported by Research Foundation of Shaanxi Province Returned Overseas Students No.SLZ2008006
文摘The performance of a sonar system is closely related to the marine environment and the target characteristics. When dealing with the echoes of a traditional active sonar system, the sonar designers often do not take into account the influence of the environmental information and prior knowledge perceived by sonar receivers, making it difficult to obtain desired processing results. Based on the basic principle and key technology of sonar, this paper proposed a cognition-based intelligent sonar system in theory--cognitive sonar. Cognitive sonar is capable of jointly optimizing the transmission waveform and receiver according to the changes of environment so that its detection and identification performance can be significantly improved.
基金supported by the National Natural Science Foundation of China(11104222)the Doctorate Foundation of Northwestern Polytechnical University(CX201101)
文摘For increasing the cross-track resolution, the multiple input multiple output （MIMO） technique is introduced into the swath bathymetry system and a new swath bathymetry approach using MIMO sonar is proposed. The MIMO sonar is composed of two parallel transmitting uniform linear arrays （ULAs） and a receiving ULA which is perpendicular to the former. The spacing between the two transmitting ULAs is equal to the product of the receiving sensor number and the receiving inter-sensor spacing. Furthermore, two narrowband linear frequency modulation （LFM） pulses, sharing the same frequency band but with opposite modulation slopes, are used as transmitting waveforms of the two transmitting ULAs. With such an array layout and transmitting signals, the MIMO sonar can sound a swath with the cross-track resolution doubling that of the traditional multibeam sonar using a Mills cross array. Numerical examples are provided to verify the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China (60972152)the National Laboratory Foundation of China (9140C2304080607)+1 种基金the Aviation Science Fund (2009ZC53031)the Doctoral Foundation of Northwestern Polytechnical University (CX201002)
文摘The maximum likelihood （ML） estimator demonstrates remarkable performance in direction of arrival （DOA） estimation for the multiple input multiple output （MIMO） sonar. However, this advantage comes with prohibitive computational complexity. In order to solve this problem, an ant colony optimization （ACO） is incorporated into the MIMO ML DOA estimator. Based on the ACO, a novel MIMO ML DOA estimator named the MIMO ACO ML （ML DOA estimator based on ACO for MIMO sonar） with even lower computational complexity is proposed. By extending the pheromone remaining process to the pheromone Gaussian kernel probability distribution function in the continuous space, the pro- posed algorithm achieves the global optimum value of the MIMO ML DOA estimator. Simulations and experimental results show that the computational cost of MIMO ACO ML is only 1/6 of the MIMO ML algorithm, while maintaining similar performance with the MIMO ML method.
基金Supported by the National Natural Nature Science Foundation of China （Grant No. 41376102）, Fundamental Research Funds for the Central Universities （Gant No. HEUCF150514） and Chinese Scholarship Council （Grant No. 201406680029）.
文摘Underwater terrain-aided navigation is used to complement the traditional inertial navigation employed by autonomous underwater vehicles during lengthy missions. It can provide fixed estimations by matching real-time depth data with a digital terrain map, This study presents the concept of using image processing techniques in the underwater terrain matching process. A traditional gray-scale histogram of an image is enriched by incorporation with spatial information in pixels. Edge comer pixels are then defined and used to construct an edge comer histogram, which employs as a template to scan the digital terrain map and estimate the fixes of the vehicle by searching the correlation peak. Simulations are performed to investigate the robustness of the proposed method, particularly in relation to its sensitivity to background noise, the scale of real-time images, and the travel direction of the vehicle. At an image resolution of 1 m2/pixel, the accuracy of localization is more than 10 meters.
