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Multi-Sensor Technology for Target Tracking, Positioning and Navigation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 15 January 2025 | Viewed by 23854

Special Issue Editors

Dr. Bingbing Gao

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: multi-sensor technology; navigation and control technology; intelligent sensing and robot positioning technology; information fusion; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Gaoge Hu

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: multi-sensor fusion; positioning and navigation; SLAM; route planning; wireless network tracking; nonlinear filtering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multi-sensor technology aims to combine the information from more than one sensor to improve the system accuracy, leading to more specific inferences than the use of a single sensor. It has become an indispensable tool for information processing in numerous fields such as target tracking, positioning, navigation, control, attitude estimation, wireless network, and so on. Thus, with the development of the information sciences and sensor technology, the multi-sensor strategy has received more and more attention for practical application in recent years.

This Special Issue therefore aims to put together original research and review articles on recent advances, solutions, applications, and new challenges for the multi-sensor technology in the fields of target tracking, positioning, and navigation.

Potential topics include but are not limited to:

  • Multi-sensor technology for target tracking applications
  • Multi-sensor technology for positioning and navigation applications
  • Artificial-intelligence-based multi-sensor technology
  • Multi-sensor technology theory
  • Multi-sensor-based SLAM
  • Multi-sensor-based route planning
  • Recent development of multi-sensor technology
  • Multi-sensor-based navigation and control
  • Multi-sensor technology for wireless network localization

Dr. Bingbing Gao
Dr. Gaoge Hu
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multi-sensor technology
  • target tracking
  • positioning and navigation
  • SLAM
  • route planning
  • artificial intelligence

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Published Papers (15 papers)

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15 pages, 8494 KiB  
Article
YOLO-OD: Obstacle Detection for Visually Impaired Navigation Assistance
by Wei Wang, Bin Jing, Xiaoru Yu, Yan Sun, Liping Yang and Chunliang Wang
Sensors 2024, 24(23), 7621; https://doi.org/10.3390/s24237621 - 28 Nov 2024
Viewed by 726
Abstract
Visually impaired individuals frequently encounter difficulties in detecting and avoiding obstacles in the wild. To address this issue, we propose an obstacle detection method for visual navigation assistance, named YOLO-OD. To improve the ability to detect and differentiate between different sized obstacles in [...] Read more.
Visually impaired individuals frequently encounter difficulties in detecting and avoiding obstacles in the wild. To address this issue, we propose an obstacle detection method for visual navigation assistance, named YOLO-OD. To improve the ability to detect and differentiate between different sized obstacles in outdoor environments, we introduce the Feature Weighting Block (FWB), which improves feature importance discrimination. To address the challenges of detecting cluttered outdoor environments and handling occlusions, we introduce the Adaptive Bottleneck Block (ABB), which captures varying features across different scenes. To solve the problem of detecting relatively small obstacles in outdoor environments, we propose the Enhanced Feature Attention Head (EFAH). The proposed YOLO-OD achieves an average precision of 30.02% on a public dataset, making it a worth studying approach for blind and visually impaired navigation aids.Our study effectively addresses the navigation challenges faced by visually impaired individuals by improving model performance, thereby enhancing its practical values. The code for YOLO-OD has been made publicly available to ensure reproducibility and facilitate further research. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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21 pages, 3922 KiB  
Article
Event-Driven Maximum Correntropy Filter Based on Cauchy Kernel for Spatial Orientation Using Gyros/Star Sensor Integration
by Kai Cui, Zhaohui Liu, Junfeng Han, Yuke Ma, Peng Liu and Bingbing Gao
Sensors 2024, 24(22), 7164; https://doi.org/10.3390/s24227164 - 7 Nov 2024
Viewed by 582
Abstract
Gyros/star sensor integration provides a potential method to obtain high-accuracy spatial orientation for turntable structures. However, it is subjected to the problem of accuracy loss when the measurement noises become non-Gaussian due to the complex spatial environment. This paper presents an event-driven maximum [...] Read more.
Gyros/star sensor integration provides a potential method to obtain high-accuracy spatial orientation for turntable structures. However, it is subjected to the problem of accuracy loss when the measurement noises become non-Gaussian due to the complex spatial environment. This paper presents an event-driven maximum correntropy filter based on Cauchy kernel to handle the above problem. In this method, a direct installation mode of gyros/star sensor integration is established and the associated mathematical model is derived to improve the turntable’s control stability. Based on this, a Cauchy kernel-based maximum correntropy filter is developed to curb the influence of non-Gaussian measurement noise for enhancing the gyros/star sensor integration’s robustness. Subsequently, an event-driven mechanism is constructed based on the filter’s innovation information for further reducing the unnecessary computational cost to optimize the real-time performance. The effectiveness of the proposed method has been validated by simulations of the gyros/star sensor integration for spatial orientation. This shows that the proposed filtering methodology not only has strong robustness to deal with the influence of non-Gaussian measurement noise but can also achieve superior real-time spatial applications with a small computational cost, leading to enhanced performance for the turntable’s spatial orientation using gyros/star sensor integration. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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25 pages, 4182 KiB  
Article
W-VSLAM: A Visual Mapping Algorithm for Indoor Inspection Robots
by Dingji Luo, Yucan Huang, Xuchao Huang, Mingda Miao and Xueshan Gao
Sensors 2024, 24(17), 5662; https://doi.org/10.3390/s24175662 - 30 Aug 2024
Viewed by 931
Abstract
In recent years, with the widespread application of indoor inspection robots, high-precision, robust environmental perception has become essential for robotic mapping. Addressing the issues of visual–inertial estimation inaccuracies due to redundant pose degrees of freedom and accelerometer drift during the planar motion of [...] Read more.
In recent years, with the widespread application of indoor inspection robots, high-precision, robust environmental perception has become essential for robotic mapping. Addressing the issues of visual–inertial estimation inaccuracies due to redundant pose degrees of freedom and accelerometer drift during the planar motion of mobile robots in indoor environments, we propose a visual SLAM perception method that integrates wheel odometry information. First, the robot’s body pose is parameterized in SE(2) and the corresponding camera pose is parameterized in SE(3). On this basis, we derive the visual constraint residuals and their Jacobian matrices for reprojection observations using the camera projection model. We employ the concept of pre-integration to derive pose-constraint residuals and their Jacobian matrices and utilize marginalization theory to derive the relative pose residuals and their Jacobians for loop closure constraints. This approach solves the nonlinear optimization problem to obtain the optimal pose and landmark points of the ground-moving robot. A comparison with the ORBSLAM3 algorithm reveals that, in the recorded indoor environment datasets, the proposed algorithm demonstrates significantly higher perception accuracy, with root mean square error (RMSE) improvements of 89.2% in translation and 98.5% in rotation for absolute trajectory error (ATE). The overall trajectory localization accuracy ranges between 5 and 17 cm, validating the effectiveness of the proposed algorithm. These findings can be applied to preliminary mapping for the autonomous navigation of indoor mobile robots and serve as a basis for path planning based on the mapping results. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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21 pages, 4097 KiB  
Article
Limited Memory-Based Random-Weighted Kalman Filter
by Zhaohui Gao, Hua Zong, Yongmin Zhong and Guangle Gao
Sensors 2024, 24(12), 3850; https://doi.org/10.3390/s24123850 - 14 Jun 2024
Viewed by 965
Abstract
The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the [...] Read more.
The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions. To address this issue, this paper presents a new method by combining the random weighting concept with the limited memory technique to accurately estimate system noise statistics. To avoid the influence of excessive historical information on state estimation, random weighting theories are established based on the limited memory technique to estimate both process noise and measurement noise statistics within a limited memory. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. The proposed method improves the Kalman filtering accuracy by adaptively adjusting the weights of system noise statistics within a limited memory to suppress the interference of system noise on system state estimation. Simulations and experiments as well as comparison analysis were conducted, demonstrating that the proposed method can overcome the disadvantage of the traditional limited memory filter, leading to im-proved accuracy for system state estimation. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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15 pages, 14962 KiB  
Article
Multiscale Bayes Adaptive Threshold Wavelet Transform Geomagnetic Basemap Denoising Taking Residual Constraints into Account
by Pan Xiong, Gang Bian, Qiang Liu, Shaohua Jin and Xiaodong Yin
Sensors 2024, 24(12), 3847; https://doi.org/10.3390/s24123847 - 14 Jun 2024
Cited by 1 | Viewed by 685
Abstract
To achieve high-precision geomagnetic matching navigation, a reliable geomagnetic anomaly basemap is essential. However, the accuracy of the geomagnetic anomaly basemap is often compromised by noise data that are inherent in the process of data acquisition and integration of multiple data sources. In [...] Read more.
To achieve high-precision geomagnetic matching navigation, a reliable geomagnetic anomaly basemap is essential. However, the accuracy of the geomagnetic anomaly basemap is often compromised by noise data that are inherent in the process of data acquisition and integration of multiple data sources. In order to address this challenge, a denoising approach utilizing an improved multiscale wavelet transform is proposed. The denoising process involves the iterative multiscale wavelet transform, which leverages the structural characteristics of the geomagnetic anomaly basemap to extract statistical information on model residuals. This information serves as the a priori knowledge for determining the Bayes estimation threshold necessary for obtaining an optimal wavelet threshold. Additionally, the entropy method is employed to integrate three commonly used evaluation indexes—the signal-to-noise ratio, root mean square (RMS), and smoothing degree. A fusion model of soft and hard threshold functions is devised to mitigate the inherent drawbacks of a single threshold function. During denoising, the Elastic Net regular term is introduced to enhance the accuracy and stability of the denoising results. To validate the proposed method, denoising experiments are conducted using simulation data from a sphere magnetic anomaly model and measured data from a Pacific Ocean sea area. The denoising performance of the proposed method is compared with Gaussian filter, mean filter, and soft and hard threshold wavelet transform algorithms. The experimental results, both for the simulated and measured data, demonstrate that the proposed method excels in denoising effectiveness; maintaining high accuracy; preserving image details while effectively removing noise; and optimizing the signal-to-noise ratio, structural similarity, root mean square error, and smoothing degree of the denoised image. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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15 pages, 4611 KiB  
Article
VA-LOAM: Visual Assist LiDAR Odometry and Mapping for Accurate Autonomous Navigation
by Tae-Ki Jung and Gyu-In Jee
Sensors 2024, 24(12), 3831; https://doi.org/10.3390/s24123831 - 13 Jun 2024
Cited by 1 | Viewed by 1266
Abstract
In this study, we enhanced odometry performance by integrating vision sensors with LiDAR sensors, which exhibit contrasting characteristics. Vision sensors provide extensive environmental information but are limited in precise distance measurement, whereas LiDAR offers high accuracy in distance metrics but lacks detailed environmental [...] Read more.
In this study, we enhanced odometry performance by integrating vision sensors with LiDAR sensors, which exhibit contrasting characteristics. Vision sensors provide extensive environmental information but are limited in precise distance measurement, whereas LiDAR offers high accuracy in distance metrics but lacks detailed environmental data. By utilizing data from vision sensors, this research compensates for the inadequate descriptors of LiDAR sensors, thereby improving LiDAR feature matching performance. Traditional fusion methods, which rely on extracting depth from image features, depend heavily on vision sensors and are vulnerable under challenging conditions such as rain, darkness, or light reflection. Utilizing vision sensors as primary sensors under such conditions can lead to significant mapping errors and, in the worst cases, system divergence. Conversely, our approach uses LiDAR as the primary sensor, mitigating the shortcomings of previous methods and enabling vision sensors to support LiDAR-based mapping. This maintains LiDAR Odometry performance even in environments where vision sensors are compromised, thus enhancing performance with the support of vision sensors. We adopted five prominent algorithms from the latest LiDAR SLAM open-source projects and conducted experiments on the KITTI odometry dataset. This research proposes a novel approach by integrating a vision support module into the top three LiDAR SLAM methods, thereby improving performance. By making the source code of VA-LOAM publicly available, this work enhances the accessibility of the technology, fostering reproducibility and transparency within the research community. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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14 pages, 15837 KiB  
Article
Improving Optical Flow Sensor Using a Gimbal for Quadrotor Navigation in GPS-Denied Environment
by Jonathan Flores, Ivan Gonzalez-Hernandez, Sergio Salazar, Rogelio Lozano and Christian Reyes
Sensors 2024, 24(7), 2183; https://doi.org/10.3390/s24072183 - 28 Mar 2024
Cited by 1 | Viewed by 1553
Abstract
This paper proposes a new sensor using optical flow to stabilize a quadrotor when a GPS signal is not available. Normally, optical flow varies with the attitude of the aerial vehicle. This produces positive feedback on the attitude control that destabilizes the orientation [...] Read more.
This paper proposes a new sensor using optical flow to stabilize a quadrotor when a GPS signal is not available. Normally, optical flow varies with the attitude of the aerial vehicle. This produces positive feedback on the attitude control that destabilizes the orientation of the vehicle. To avoid this, we propose a novel sensor using an optical flow camera with a 6DoF IMU (Inertial Measurement Unit) mounted on a two-axis anti-shake stabilizer mobile aerial gimbal. We also propose a robust algorithm based on Sliding Mode Control for stabilizing the optical flow sensor downwards independently of the aerial vehicle attitude. This method improves the estimation of the position and velocity of the quadrotor. We present experimental results to show the performance of the proposed sensor and algorithms. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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17 pages, 3587 KiB  
Article
Constrained Cubature Particle Filter for Vehicle Navigation
by Li Xue, Yongmin Zhong and Yulan Han
Sensors 2024, 24(4), 1228; https://doi.org/10.3390/s24041228 - 15 Feb 2024
Cited by 3 | Viewed by 839
Abstract
In vehicle navigation, it is quite common that the dynamic system is subject to various constraints, which increases the difficulty in nonlinear filtering. To address this issue, this paper presents a new constrained cubature particle filter (CCPF) for vehicle navigation. Firstly, state constraints [...] Read more.
In vehicle navigation, it is quite common that the dynamic system is subject to various constraints, which increases the difficulty in nonlinear filtering. To address this issue, this paper presents a new constrained cubature particle filter (CCPF) for vehicle navigation. Firstly, state constraints are incorporated in the importance sampling process of the traditional cubature particle filter to enhance the accuracy of the importance density function. Subsequently, the Euclidean distance is employed to optimize the resampling process by adjusting particle weights to avoid particle degradation. Further, the convergence of the proposed CCPF is also rigorously proved, showing that the posterior probability function is converged when the particle number N → ∞. Our experimental results and the results of a comparative analysis regarding GNSS/DR (Global Navigation Satellite System/Dead Reckoning)-integrated vehicle navigation demonstrate that the proposed CCPF can effectively estimate system state under constrained conditions, leading to higher estimation accuracy than the traditional particle filter and cubature particle filter. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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13 pages, 3433 KiB  
Article
ConGPS: A Smart Container Positioning System Using Inertial Sensor and Electronic Map with Infrequent GPS
by Shan Huang, Zihan Song, Hyung-Rim Choi, Jae-Joong Kim, Do-Myung Park and Byung-Kwon Park
Sensors 2023, 23(22), 9198; https://doi.org/10.3390/s23229198 - 15 Nov 2023
Viewed by 1532
Abstract
Real-time global positioning is important for container-based logistics. However, a challenge in real-time global positioning arises from the frequency of both global positioning system (GPS) calls and GPS-denied environments during transportation. This paper proposes a novel system named ConGPS that integrates both inertial [...] Read more.
Real-time global positioning is important for container-based logistics. However, a challenge in real-time global positioning arises from the frequency of both global positioning system (GPS) calls and GPS-denied environments during transportation. This paper proposes a novel system named ConGPS that integrates both inertial sensor and electronic map data. ConGPS estimates the speed and heading direction of a moving container based on the inertial sensor data, the container trajectory, and the speed limit information provided by an electronic map. The directional information from magnetometers, coupled with map-matching algorithms, is employed to compute container trajectories and current positions. ConGPS significantly reduces the frequency of GPS calls required to maintain an accurate current position. To evaluate the accuracy of the system, 280 min of driving data, covering a distance of 360 km, are collected. The results demonstrate that ConGPS can maintain positioning accuracy within a GPS-call interval of 15 min, even if using low-cost inertial sensors in GPS-denied environments. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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14 pages, 5594 KiB  
Article
Application of Adaptive Robust Kalman Filter Base on MCC for SINS/GPS Integrated Navigation
by Linfeng Li, Jian Wang, Zhiming Chen and Teng Yu
Sensors 2023, 23(19), 8131; https://doi.org/10.3390/s23198131 - 28 Sep 2023
Cited by 2 | Viewed by 1210
Abstract
In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex [...] Read more.
In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex environment. Firstly, the Grubbs criterion was used to remove outliers, which are contained in the GPS measurement. Then, a fixed-length sliding window was used to estimate the decay factor adaptively. Based on the fixed-length sliding window method, the time-varying noises, which are considered in integrated navigation system, are addressed. Moreover, a MCC method is used to suppress the non-Gaussian noises, which are generated with external corruption. Finally, the method, which is proposed in this paper, is verified by the designed simulation and field tests. The results show that the influence of the non-Gaussian noise and time-varying noise of the GPS measurement is detected and isolated by the proposed algorithm, effectively. The navigation accuracy and stability are improved. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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17 pages, 4761 KiB  
Article
Multi-Sensors System and Deep Learning Models for Object Tracking
by Ghina El Natour, Guillaume Bresson and Remi Trichet
Sensors 2023, 23(18), 7804; https://doi.org/10.3390/s23187804 - 11 Sep 2023
Cited by 2 | Viewed by 2474
Abstract
Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement arises to accurately track and predict these objects’ trajectories. Three [...] Read more.
Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement arises to accurately track and predict these objects’ trajectories. Three deep recurrent network architectures were defined to achieve this, fine-tuning their weights to optimize the tracking process. The effectiveness of this proposed pipeline has been assessed, with diverse tracking scenarios demonstrated in both sub-urban and highway environments. The evaluations have yielded promising results, affirming the potential of this approach in enhancing autonomous navigation capabilities. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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24 pages, 8459 KiB  
Article
Robust Localization of Industrial Park UGV and Prior Map Maintenance
by Fanrui Luo, Zhenyu Liu, Fengshan Zou, Mingmin Liu, Yang Cheng and Xiaoyu Li
Sensors 2023, 23(15), 6987; https://doi.org/10.3390/s23156987 - 6 Aug 2023
Cited by 3 | Viewed by 1715
Abstract
The precise localization of unmanned ground vehicles (UGVs) in industrial parks without prior GPS measurements presents a significant challenge. Simultaneous localization and mapping (SLAM) techniques can address this challenge by capturing environmental features, using sensors for real-time UGV localization. In order to increase [...] Read more.
The precise localization of unmanned ground vehicles (UGVs) in industrial parks without prior GPS measurements presents a significant challenge. Simultaneous localization and mapping (SLAM) techniques can address this challenge by capturing environmental features, using sensors for real-time UGV localization. In order to increase the real-time localization accuracy and efficiency of UGVs, and to improve the robustness of UGVs’ odometry within industrial parks—thereby addressing issues related to UGVs’ motion control discontinuity and odometry drift—this paper proposes a tightly coupled LiDAR-IMU odometry method based on FAST-LIO2, integrating ground constraints and a novel feature extraction method. Additionally, a novel maintenance method of prior maps is proposed. The front-end module acquires the prior pose of the UGV by combining the detection and correction of relocation with point cloud registration. Then, the proposed maintenance method of prior maps is used to hierarchically and partitionally segregate and perform the real-time maintenance of the prior maps. At the back-end, real-time localization is achieved by the proposed tightly coupled LiDAR-IMU odometry that incorporates ground constraints. Furthermore, a feature extraction method based on the bidirectional-projection plane slope difference filter is proposed, enabling efficient and accurate point cloud feature extraction for edge, planar and ground points. Finally, the proposed method is evaluated, using self-collected datasets from industrial parks and the KITTI dataset. Our experimental results demonstrate that, compared to FAST-LIO2 and FAST-LIO2 with the curvature feature extraction method, the proposed method improved the odometry accuracy by 30.19% and 48.24% on the KITTI dataset. The efficiency of odometry was improved by 56.72% and 40.06%. When leveraging prior maps, the UGV achieved centimeter-level localization accuracy. The localization accuracy of the proposed method was improved by 46.367% compared to FAST-LIO2 on self-collected datasets, and the located efficiency was improved by 32.33%. The z-axis-located accuracy of the proposed method reached millimeter-level accuracy. The proposed prior map maintenance method reduced RAM usage by 64% compared to traditional methods. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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19 pages, 6558 KiB  
Article
Learning Modality Complementary Features with Mixed Attention Mechanism for RGB-T Tracking
by Yang Luo, Xiqing Guo, Mingtao Dong and Jin Yu
Sensors 2023, 23(14), 6609; https://doi.org/10.3390/s23146609 - 22 Jul 2023
Cited by 9 | Viewed by 1708
Abstract
RGB-T tracking involves the use of images from both visible and thermal modalities. The primary objective is to adaptively leverage the relatively dominant modality in varying conditions to achieve more robust tracking compared to single-modality tracking. An RGB-T tracker based on a mixed-attention [...] Read more.
RGB-T tracking involves the use of images from both visible and thermal modalities. The primary objective is to adaptively leverage the relatively dominant modality in varying conditions to achieve more robust tracking compared to single-modality tracking. An RGB-T tracker based on a mixed-attention mechanism to achieve a complementary fusion of modalities (referred to as MACFT) is proposed in this paper. In the feature extraction stage, we utilize different transformer backbone branches to extract specific and shared information from different modalities. By performing mixed-attention operations in the backbone to enable information interaction and self-enhancement between the template and search images, a robust feature representation is constructed that better understands the high-level semantic features of the target. Then, in the feature fusion stage, a modality shared-specific feature interaction structure was designed based on a mixed-attention mechanism, effectively suppressing low-quality modality noise while enhancing the information from the dominant modality. Evaluation on multiple RGB-T public datasets demonstrates that our proposed tracker outperforms other RGB-T trackers on general evaluation metrics while also being able to adapt to long-term tracking scenarios. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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13 pages, 4001 KiB  
Article
An Improved In-Motion Coarse Alignment Method for SINS/GPS Integration with Initial Velocity Error Suppression
by Yukun Wang, Xiuli Ning and Xiang Xu
Sensors 2023, 23(7), 3662; https://doi.org/10.3390/s23073662 - 31 Mar 2023
Cited by 1 | Viewed by 1486
Abstract
The integrated system with the strapdown inertial navigation system (SINS) and the global positioning system (GPS) is the most popular navigation mode. It has been used in many navigation fields. Before the integrated system works properly, it must determine the initial attitude for [...] Read more.
The integrated system with the strapdown inertial navigation system (SINS) and the global positioning system (GPS) is the most popular navigation mode. It has been used in many navigation fields. Before the integrated system works properly, it must determine the initial attitude for SINS. In SINS/GPS-integrated systems, the navigational velocity can be used to carry out the initial alignment when the system is installed in the in-motion vehicle. However, the initial velocity errors are not considered in the current popular in-motion alignment methods for SINS/GPS integration. It is well-known that the initial velocity errors must exist when the initial velocity is obtained from the GPS outputs. In this paper, an improved method was proposed to solve this problem. By analyzing the original observation vectors in the in-motion coarse alignment method, an average operation was used to construct the intermediate vectors, and the new observation vector can be calculated by subtracting the intermediate vector from the original observation vector. Then, the initial velocity errors can be eliminated from the newly constructed observation vector. Thus, the interferences of the initial velocity errors for the initial alignment process can be suppressed. The simulation and field tests are designed to verify the performance of the proposed method. The tests results showed that the proposed method can obtain the higher accurate results than the current methods when the initial velocity is considered. Additionally, the results of the proposed method were similar to the current methods when the initial velocity errors were not considered. This shows that the initial velocity errors were eliminated effectively by the proposed method, and the alignment accuracy were not decreased. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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17 pages, 10734 KiB  
Article
Multi-Scale Feature Interactive Fusion Network for RGBT Tracking
by Xianbing Xiao, Xingzhong Xiong, Fanqin Meng and Zhen Chen
Sensors 2023, 23(7), 3410; https://doi.org/10.3390/s23073410 - 24 Mar 2023
Cited by 6 | Viewed by 2929
Abstract
The fusion tracking of RGB and thermal infrared image (RGBT) is paid wide attention to due to their complementary advantages. Currently, most algorithms obtain modality weights through attention mechanisms to integrate multi-modalities information. They do not fully exploit the multi-scale information and ignore [...] Read more.
The fusion tracking of RGB and thermal infrared image (RGBT) is paid wide attention to due to their complementary advantages. Currently, most algorithms obtain modality weights through attention mechanisms to integrate multi-modalities information. They do not fully exploit the multi-scale information and ignore the rich contextual information among features, which limits the tracking performance to some extent. To solve this problem, this work proposes a new multi-scale feature interactive fusion network (MSIFNet) for RGBT tracking. Specifically, we use different convolution branches for multi-scale feature extraction and aggregate them through the feature selection module adaptively. At the same time, a Transformer interactive fusion module is proposed to build long-distance dependencies and enhance semantic representation further. Finally, a global feature fusion module is designed to adjust the global information adaptively. Numerous experiments on publicly available GTOT, RGBT234, and LasHeR datasets show that our algorithm outperforms the current mainstream tracking algorithms. Full article
(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)
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