The task of food image recognition,a nuanced subset of fine-grained image recognition,grapples with substantial intra-class variation and minimal inter-class differences.These challenges are compounded by the irregula...The task of food image recognition,a nuanced subset of fine-grained image recognition,grapples with substantial intra-class variation and minimal inter-class differences.These challenges are compounded by the irregular and multi-scale nature of food images.Addressing these complexities,our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion,grounded in the ConvNeXt architecture.Our model employs hybrid attention(HA)mechanisms to pinpoint critical discriminative regions within images,substantially mitigating the influence of background noise.Furthermore,it introduces a multi-stage local fusion(MSLF)module,fostering long-distance dependencies between feature maps at varying stages.This approach facilitates the assimilation of complementary features across scales,significantly bolstering the model’s capacity for feature extraction.Furthermore,we constructed a dataset named Roushi60,which consists of 60 different categories of common meat dishes.Empirical evaluation of the ETH Food-101,ChineseFoodNet,and Roushi60 datasets reveals that our model achieves recognition accuracies of 91.12%,82.86%,and 92.50%,respectively.These figures not only mark an improvement of 1.04%,3.42%,and 1.36%over the foundational ConvNeXt network but also surpass the performance of most contemporary food image recognition methods.Such advancements underscore the efficacy of our proposed model in navigating the intricate landscape of food image recognition,setting a new benchmark for the field.展开更多
To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level ...To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.展开更多
The localization of the blanket jamming is studied and a new method of solving the localization ambiguity is proposed. Radars only can acquire angle information without range information when encountering the blanket ...The localization of the blanket jamming is studied and a new method of solving the localization ambiguity is proposed. Radars only can acquire angle information without range information when encountering the blanket jamming. Netted radars could get position information of the blanket jamming by make use of radars' relative position and the angle information, when there is one blanket jamming. In the presence of error, the localization method and the accuracy analysis of one blanket jamming are given. However, if there are more than one blanket jamming, and the two blanket jamming and two radars are coplanar, the localization of jamming could be error due to localization ambiguity. To solve this confusion, the Kalman filter model is established for all intersections, and through the initiation and association algorithm of multi-target, the false intersection can be eliminated. Simulations show that the presented method is valid.展开更多
For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sens...For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion. The pedestrian’s localization in indoor environment is described as dynamic system state estimation problem. The algorithm combines the smart mobile terminal with indoor localization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization information have been used to improve the filtering precision and the stability. Moreover, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic changes of the indoor environment. Experimental results show that the algorithm based on multi-sensor improves the localization accuracy and robustness compared with the location algorithm based on Wi-Fi.展开更多
s-Lap is a new gene sequence from pig retinal pigment epithelial(RPE) cells, which was found and cloned in the early period of apoptosis of RPE cells damaged with visible light. We cloned the coding area sequence of t...s-Lap is a new gene sequence from pig retinal pigment epithelial(RPE) cells, which was found and cloned in the early period of apoptosis of RPE cells damaged with visible light. We cloned the coding area sequence of the novel gene of s-Lap and constructed its recombinant eukaryotic plasmid pcDNA3.1-GFP/s-lap with the recombinant DNA technique. The expression and localization of s-lap/GFP fusion protein in CHO and B_~16 cell lines were studied with the instantaneously transfected pcDNA3.1-GFP/s-lap recombinant plasmid. ~s-Lap/GFP fusion protein can be expressed in CHO and B_~16 cells with a high rate expression in the nuclei.展开更多
A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a ...A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.展开更多
This paper presents a robust face recognition algorithm by using transform domain-based multiple feature fusion and lin- ear regression. Transform domain-based feature fusion can provide comprehensive face information...This paper presents a robust face recognition algorithm by using transform domain-based multiple feature fusion and lin- ear regression. Transform domain-based feature fusion can provide comprehensive face information for recognition, and decrease the effect of variations in illumination and pose. The holistic feature and local feature are extracted by discrete cosine transform and Gabor wavelet transform, respectively. Then the extracted holistic features and the local features are fused by weighted sum. The fused feature values are finally sent to linear regression classifier for recognition. The algorithm is evaluated on AR, ORL and Yale B face databases. Experiment results show that our proposed algo- rithm could be more robust than those single feature-based algo- rithms under pose and expression variations.展开更多
The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains thr...The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.展开更多
Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. In order to retain useful information and g...Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. In order to retain useful information and get more reliable results, a novel medical image fusion algorithm based on pulse coupled neural networks (PCNN) and multi-feature fuzzy clustering is proposed, which makes use of the multi-feature of image and combines the advantages of the local entropy and variance of local entropy based PCNN. The results of experiments indicate that the proposed image fusion method can better preserve the image details and robustness and significantly improve the image visual effect than the other fusion methods with less information distortion.展开更多
With the total RNA of Dunaliella salina as a template,the cD NA sequence of D. salina small GTP-binding protein gene was amplified by RT-PCR technique,and cloned onto pM Dl8-T simple vector,the recon was subjected to ...With the total RNA of Dunaliella salina as a template,the cD NA sequence of D. salina small GTP-binding protein gene was amplified by RT-PCR technique,and cloned onto pM Dl8-T simple vector,the recon was subjected to PCR detection and restriction endonuclease analysis,and the total sequence of DNA was determined. The results showed that the cloned fragment was 612 bp,and shared 100% homology with reported D. salina DsRab gene（ GenB ank： JN989548）. The target gene fragment was inserted downstream of pM DCG 35 S promoter,constructing subcellular localization recombinant vector pM DCG-DsRab. The successfully constructed subcellular localization recombinant vector pM DCG-DsRab was transformed into Agrobacterium tumefaciens LBA4404,and positive single clones were screened and used for transinfection of onion epidemical cells by Agrobacterium-mediated method,and the instant expression of DsRab was observed under fluorescence microscope. The results showed that the fusion protein GFP-DsRab was successfully expressed in onion epidemical cells,and mainly distributed on cytomembrane. This study will provide reference for further illumination of the function and action mechanism of D. salina small GTP-binding protein DsRab.展开更多
Shot boundary detection is the fundamental part in many real applications as video retrieval and so on. This paper tackles the problem of video segment obtaining in complex movie videos. Firstly, intermediate descript...Shot boundary detection is the fundamental part in many real applications as video retrieval and so on. This paper tackles the problem of video segment obtaining in complex movie videos. Firstly, intermediate descriptor is proposed to depict the variation of both abrupt and gradual change in shot boundaries, which is formed by distance vector on Local Binary Pattern(LBP), GIST(GIST) or their fusion. Instead of just using the adjacent frames distance, intermediate descriptor keeps the distances between current frame and consecutive frames. It comprehensively characterizes local temporal structure, which is especially important for gradual change. For the excellent ability for feature fusion in random forests, it is adopted here to verify the fusion effect of intermediate descriptor on LBP and GIST. The whole experiments are designed on the subset of TRECVid 2013 INS(INstance Search) task to verify the effectiveness of proposed intermediate descriptor and the fusion ability for random forest. Compared with static and adaptive thresholds approaches, the best performance can be achieved by post-fusion of intermediate descriptor on LBP and GIST.展开更多
This paper presents a novel method, which enhances the use of external mechanisms by considering a multisensor system, composed of sonars and a CCD camera. Monocular vision provides redundant information about the loc...This paper presents a novel method, which enhances the use of external mechanisms by considering a multisensor system, composed of sonars and a CCD camera. Monocular vision provides redundant information about the location of the geometric entities detected by the sonar sensors. To reduce ambiguity significantly, an improved and more detailed sonar model is utilized. Moreover, Hough transform is used to extract features from raw sonar data and vision image. Information is fused at the level of features. This technique significantly improves the reliability and precision of the environment observations used for the simultaneous localization and map building problem for mobile robots. Experimental results validate the favorable performance of this approach.展开更多
In this paper a new method of passive underwater TMA (target motion analysis) using data fusion is presented. The findings of this research are based on an understanding that there is a powerful sonar system that cons...In this paper a new method of passive underwater TMA (target motion analysis) using data fusion is presented. The findings of this research are based on an understanding that there is a powerful sonar system that consists of many types of sonar but with one own-ship, and that different target parameter measurements can be obtained simultaneously. For the analysis 3 data measurements, passive bearing, elevation and multipath time-delay, are used, which are divided into two groups: a group with estimates of two preliminary target parameter obtained by dealing with each group measurement independently, and a group where correlated estimates are sent to a fusion center where the correlation between two data groups are considered so that the passive underwater TMA is realized. Simulation results show that curves of parameter estimation errors obtained by using the data fusion have fast convergence and the estimation accuracy is noticeably improved. The TMA algorithm presented is verified and is of practical significance because it is easy to be realized in one ship.展开更多
In order to reduce the cost of indoor localization system for autonomous mobile robots( AMRs) and to enhance the localization efficiency,this paper presents a localization approach using sequential time of flight( STO...In order to reduce the cost of indoor localization system for autonomous mobile robots( AMRs) and to enhance the localization efficiency,this paper presents a localization approach using sequential time of flight( STOF) measurements from a single receiver to localize AMRs in indoor environments. The STOF is a series of TOF measurements that are acquired by the mobile source in sequence. Combined with the pose estimation obtained from the Dead Reckoning( DR) method,the STOF measurements from a single receiver can be adapted and applied to the trilateration localization model to determine the indoor position of the AMRs. Based on the error analysis of the STOF localization,a double-layer Kalman filter( DLKF) is proposed to fuse multiple STOF localization results and further improve the localization accuracy. In the computer simulation experiments,an average ±20 mm positioning accuracy is attained with the presence of simulated noise that is similar to the realistic sensor noise in magnitude. The simulation results indicate the effectiveness and the potential value of the proposed localization scheme in the practical indoor localization application.展开更多
基金The support of this research was by Hubei Provincial Natural Science Foundation(2022CFB449)Science Research Foundation of Education Department of Hubei Province(B2020061),are gratefully acknowledged.
文摘The task of food image recognition,a nuanced subset of fine-grained image recognition,grapples with substantial intra-class variation and minimal inter-class differences.These challenges are compounded by the irregular and multi-scale nature of food images.Addressing these complexities,our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion,grounded in the ConvNeXt architecture.Our model employs hybrid attention(HA)mechanisms to pinpoint critical discriminative regions within images,substantially mitigating the influence of background noise.Furthermore,it introduces a multi-stage local fusion(MSLF)module,fostering long-distance dependencies between feature maps at varying stages.This approach facilitates the assimilation of complementary features across scales,significantly bolstering the model’s capacity for feature extraction.Furthermore,we constructed a dataset named Roushi60,which consists of 60 different categories of common meat dishes.Empirical evaluation of the ETH Food-101,ChineseFoodNet,and Roushi60 datasets reveals that our model achieves recognition accuracies of 91.12%,82.86%,and 92.50%,respectively.These figures not only mark an improvement of 1.04%,3.42%,and 1.36%over the foundational ConvNeXt network but also surpass the performance of most contemporary food image recognition methods.Such advancements underscore the efficacy of our proposed model in navigating the intricate landscape of food image recognition,setting a new benchmark for the field.
文摘To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.
文摘The localization of the blanket jamming is studied and a new method of solving the localization ambiguity is proposed. Radars only can acquire angle information without range information when encountering the blanket jamming. Netted radars could get position information of the blanket jamming by make use of radars' relative position and the angle information, when there is one blanket jamming. In the presence of error, the localization method and the accuracy analysis of one blanket jamming are given. However, if there are more than one blanket jamming, and the two blanket jamming and two radars are coplanar, the localization of jamming could be error due to localization ambiguity. To solve this confusion, the Kalman filter model is established for all intersections, and through the initiation and association algorithm of multi-target, the false intersection can be eliminated. Simulations show that the presented method is valid.
文摘For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion. The pedestrian’s localization in indoor environment is described as dynamic system state estimation problem. The algorithm combines the smart mobile terminal with indoor localization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization information have been used to improve the filtering precision and the stability. Moreover, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic changes of the indoor environment. Experimental results show that the algorithm based on multi-sensor improves the localization accuracy and robustness compared with the location algorithm based on Wi-Fi.
文摘s-Lap is a new gene sequence from pig retinal pigment epithelial(RPE) cells, which was found and cloned in the early period of apoptosis of RPE cells damaged with visible light. We cloned the coding area sequence of the novel gene of s-Lap and constructed its recombinant eukaryotic plasmid pcDNA3.1-GFP/s-lap with the recombinant DNA technique. The expression and localization of s-lap/GFP fusion protein in CHO and B_~16 cell lines were studied with the instantaneously transfected pcDNA3.1-GFP/s-lap recombinant plasmid. ~s-Lap/GFP fusion protein can be expressed in CHO and B_~16 cells with a high rate expression in the nuclei.
文摘现有的基于深度学习的医学图像分割方法,大多是利用大量的训练数据拟合检测网络,以获得优异的检测性能。这些方法往往需要较大的模型参数,导致检测实时性较差。为此,提出了基于局部上下文引导特征深度融合轻量级医学分割网络(local context guided feature deep fusion lightweight medical segmentation network,LCGML-net)。LCGML-net通过精确的特征选择与特征融合来减少模型拟合所需的参数数量,从而在保证检测精度的同时实现更小的模型参数。在特征提取阶段和映射阶段,分别通过提取和融合目标的多层次多尺度局部上下文特征来丰富特征表达和精准分割。在STARE、CHASEDB1和KITS19等多个基准数据集上开展的实验证明,与其他方法相比,所提出的LCGML-net具有最佳的检测性能和最小的模型参数。
基金supported by the National Natural Science Foundation of China(61304097)the Projects of Major International(Regional)Joint Research Program NSFC(61120106010)the Foundation for Innovation Research Groups of the National National Natural Science Foundation of China(61321002)
文摘A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.
基金Supported by the National Science Foundation of China(60972081)Hubei Natural Science Foundation of China(2013CFC118,2009CDA139)+3 种基金Special Funds for Shenzhen Strategic New Industry Development(JCYJ 20120616135936123)Special Project on the Integration of Industry,Education and Research of Ministry of Education of Guangdong Province(2011B090400477)Special Project on the Integration of Industry,Education and Research of Zhuhai City(2011A050101005,2012D0501990016)Zhuhai Key Laboratory Program for Science and Technique(2012D050 1990026)
文摘This paper presents a robust face recognition algorithm by using transform domain-based multiple feature fusion and lin- ear regression. Transform domain-based feature fusion can provide comprehensive face information for recognition, and decrease the effect of variations in illumination and pose. The holistic feature and local feature are extracted by discrete cosine transform and Gabor wavelet transform, respectively. Then the extracted holistic features and the local features are fused by weighted sum. The fused feature values are finally sent to linear regression classifier for recognition. The algorithm is evaluated on AR, ORL and Yale B face databases. Experiment results show that our proposed algo- rithm could be more robust than those single feature-based algo- rithms under pose and expression variations.
基金financially supported by the National Natural Science Foundation of China(Grant No.51879049)DK-I Dynamic Positioning System Console Project
文摘The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.
文摘Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. In order to retain useful information and get more reliable results, a novel medical image fusion algorithm based on pulse coupled neural networks (PCNN) and multi-feature fuzzy clustering is proposed, which makes use of the multi-feature of image and combines the advantages of the local entropy and variance of local entropy based PCNN. The results of experiments indicate that the proposed image fusion method can better preserve the image details and robustness and significantly improve the image visual effect than the other fusion methods with less information distortion.
基金Supported by National Natural Science Foundation of China(31472260)
文摘With the total RNA of Dunaliella salina as a template,the cD NA sequence of D. salina small GTP-binding protein gene was amplified by RT-PCR technique,and cloned onto pM Dl8-T simple vector,the recon was subjected to PCR detection and restriction endonuclease analysis,and the total sequence of DNA was determined. The results showed that the cloned fragment was 612 bp,and shared 100% homology with reported D. salina DsRab gene（ GenB ank： JN989548）. The target gene fragment was inserted downstream of pM DCG 35 S promoter,constructing subcellular localization recombinant vector pM DCG-DsRab. The successfully constructed subcellular localization recombinant vector pM DCG-DsRab was transformed into Agrobacterium tumefaciens LBA4404,and positive single clones were screened and used for transinfection of onion epidemical cells by Agrobacterium-mediated method,and the instant expression of DsRab was observed under fluorescence microscope. The results showed that the fusion protein GFP-DsRab was successfully expressed in onion epidemical cells,and mainly distributed on cytomembrane. This study will provide reference for further illumination of the function and action mechanism of D. salina small GTP-binding protein DsRab.
基金Supported by the Young Teacher Support Plan by Heilongjiang Province and Harbin Engineering University in China(No.1155G17)partially by the Fundamental Research Funds for the Central Universities Grant to X.Xiang
文摘Shot boundary detection is the fundamental part in many real applications as video retrieval and so on. This paper tackles the problem of video segment obtaining in complex movie videos. Firstly, intermediate descriptor is proposed to depict the variation of both abrupt and gradual change in shot boundaries, which is formed by distance vector on Local Binary Pattern(LBP), GIST(GIST) or their fusion. Instead of just using the adjacent frames distance, intermediate descriptor keeps the distances between current frame and consecutive frames. It comprehensively characterizes local temporal structure, which is especially important for gradual change. For the excellent ability for feature fusion in random forests, it is adopted here to verify the fusion effect of intermediate descriptor on LBP and GIST. The whole experiments are designed on the subset of TRECVid 2013 INS(INstance Search) task to verify the effectiveness of proposed intermediate descriptor and the fusion ability for random forest. Compared with static and adaptive thresholds approaches, the best performance can be achieved by post-fusion of intermediate descriptor on LBP and GIST.
基金supported by the National Natural Science Foundation of China (No.60805032)the National High Technology Research and Development Program (No.2006AA040202, 2007AA041703)
文摘This paper presents a novel method, which enhances the use of external mechanisms by considering a multisensor system, composed of sonars and a CCD camera. Monocular vision provides redundant information about the location of the geometric entities detected by the sonar sensors. To reduce ambiguity significantly, an improved and more detailed sonar model is utilized. Moreover, Hough transform is used to extract features from raw sonar data and vision image. Information is fused at the level of features. This technique significantly improves the reliability and precision of the environment observations used for the simultaneous localization and map building problem for mobile robots. Experimental results validate the favorable performance of this approach.
文摘In this paper a new method of passive underwater TMA (target motion analysis) using data fusion is presented. The findings of this research are based on an understanding that there is a powerful sonar system that consists of many types of sonar but with one own-ship, and that different target parameter measurements can be obtained simultaneously. For the analysis 3 data measurements, passive bearing, elevation and multipath time-delay, are used, which are divided into two groups: a group with estimates of two preliminary target parameter obtained by dealing with each group measurement independently, and a group where correlated estimates are sent to a fusion center where the correlation between two data groups are considered so that the passive underwater TMA is realized. Simulation results show that curves of parameter estimation errors obtained by using the data fusion have fast convergence and the estimation accuracy is noticeably improved. The TMA algorithm presented is verified and is of practical significance because it is easy to be realized in one ship.
基金Sponsored by the National Science and Technology Support Program(Grant No.012BAI33B04)
文摘In order to reduce the cost of indoor localization system for autonomous mobile robots( AMRs) and to enhance the localization efficiency,this paper presents a localization approach using sequential time of flight( STOF) measurements from a single receiver to localize AMRs in indoor environments. The STOF is a series of TOF measurements that are acquired by the mobile source in sequence. Combined with the pose estimation obtained from the Dead Reckoning( DR) method,the STOF measurements from a single receiver can be adapted and applied to the trilateration localization model to determine the indoor position of the AMRs. Based on the error analysis of the STOF localization,a double-layer Kalman filter( DLKF) is proposed to fuse multiple STOF localization results and further improve the localization accuracy. In the computer simulation experiments,an average ±20 mm positioning accuracy is attained with the presence of simulated noise that is similar to the realistic sensor noise in magnitude. The simulation results indicate the effectiveness and the potential value of the proposed localization scheme in the practical indoor localization application.
