With the rapid development of electronic information technology, Ground Penetrating radar (GPR) technology has become an indispensable tool in fields such as urban construction, archaeology, and the military. However,...
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Two current hurdles of quantum radar/LiDAR technology are i.) The use of joint measurement techniques, whereby the idler remains in a delay line or a quantum memory to be measured later with the returning signal, and ...
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ISBN:
(纸本)9781510674158;9781510674141
Two current hurdles of quantum radar/LiDAR technology are i.) The use of joint measurement techniques, whereby the idler remains in a delay line or a quantum memory to be measured later with the returning signal, and ii.) The difficulty in creating high photon flux signals for long range sensing. Our measurement and detection protocol using immediate-idler-detection (IID) helps to alleviate both of these issues. We present our recent experimental data from characterizing our proof-of-concept IID quantum LiDAR system and show that similar to delay line approaches, we achieve strong correlation even in extremely noisy channels where the noise level exceeds the signal strength by as much as one hundred times. We have found that even in very lossy channels, the integration time remains extremely short and roughly the same value even as the noise is increased. We also show preliminary results through foggy free space channels and found positive correlation SNR even when the visibility was as low as 15%. Our measurement and detection protocol was designed to align closely with classical radar and LiDAR signal processing to better align the quantum and classical sensor regimes and allows for the potential to scale upwards and produce higher photon-flux signals from multiple photon pair sources.
Despite the crucial role of the Global Navigation Satellite System (GNSS) for land vehicle navigation, it is prone to wireless signal degradation challenges that can compromise positioning accuracy and availability. I...
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ISBN:
(纸本)9798350384826;9798350384819
Despite the crucial role of the Global Navigation Satellite System (GNSS) for land vehicle navigation, it is prone to wireless signal degradation challenges that can compromise positioning accuracy and availability. Integrating GNSS with Dead Reckoning (DR) systems like the Inertial Navigation System (INS) offers a solution. Still, it is hindered by drifting errors and the limitations of Micro-Electro-Mechanical Systems (MEMS) technology. This paper aims to address the challenges faced by land vehicle navigation systems, particularly during extended GNSS outages, by leveraging the synergistic integration of data from multiple redundant Inertial Measurement Units (IMUs) and mmWave radartechnology. To be specific, a Kalman Filter framework is employed to fuse raw measurements from multiple homogeneous IMUs, yielding refined and robust measurements. An Autoencoder model calibrates and denoises raw radar measurements, ensuring accurate and reliable forward velocity estimates. The enhanced measurements are then processed by a mechanization algorithm, yielding a navigation solution with significantly reduced drifting errors. The effectiveness of this approach has been validated with real-world data collected from IMUs and radar during road tests in downtown Kingston, Ontario, Canada. Our comprehensive testing across various scenarios has shown that the proposed method significantly improves navigation performance, achieving average heading and position accuracy enhancements of 89% and 62%, respectively.
Real-time dashboarding software empowers organizations to harness the burgeoning IoT data stream, transforming raw sensor readings into actionable insights through a secure and scalable architecture. data from connect...
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Addressing the poor resolution challenge of traditional Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms in differentiating objects in close proximity, this paper introduces a multi-dime...
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ISBN:
(纸本)9798350389968
Addressing the poor resolution challenge of traditional Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms in differentiating objects in close proximity, this paper introduces a multi-dimensional DBSCAN (MD-DBSCAN) clustering approach, considering the Doppler characteristics of millimeter wave (mmW) radar. Our approach is based on the idea that the whole clustering procedure is divided into two stages, i.e., velocity clustering and spatial one. Specifically, for each group, the clusters of varying radial velocities are firstly segmented, then followed by spatial dimension clustering. Experimental results demonstrate a 14.2% improvement in number of point clouds with this approach over traditional methods, underscoring its effectiveness in more accurately distinguishing closely situated objects in autonomous driving scenarios.
Accurate classification of crops at the patch level based on nutrient status, particularly nitrogen (N) levels, is essential for advancing precision agriculture (PA). While recent advancements in remote sensing, scala...
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Accurate classification of crops at the patch level based on nutrient status, particularly nitrogen (N) levels, is essential for advancing precision agriculture (PA). While recent advancements in remote sensing, scalable computing, and visualization technologies have enabled high-resolution plant monitoring, the spectral similarity among crops remains a challenge for precise classification using remote sensing data. This study introduces a multisensor fusion approach, integrating terrestrial LiDAR point cloud data and WorldView-III multispectral imagery within a deep learning (DL) framework to classify cabbage, eggplant, and tomato across different N levels. By combining structural and spectral information, this method effectively captures N-induced growth variations, leading to improved crop discrimination. Our results demonstrate that applying a deep convolutional neural network (DCNN) model to the fused dataset enhances classification accuracy by 13%-16% compared to using multispectral data alone. The incorporation of LiDAR data plays a key role in capturing canopy structure, significantly improving classification performance. Additionally, our DL approach outperforms traditional machine-learning methods, including the random forest (RF) classifier, reinforcing the advantages of DL for N-sensitive crop classification. By leveraging multisensor integration and DL, this study presents a robust and scalable approach for enhancing crop classification accuracy, with significant potential for advancing PA and site-specific nutrient management.
The sample consensus (SAC) methods have become increasingly important in the field of radar-based dynamic object classification for applications such as ego-motion estimation and obstacle avoidance. This paper present...
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ISBN:
(纸本)9783031449802;9783031449819
The sample consensus (SAC) methods have become increasingly important in the field of radar-based dynamic object classification for applications such as ego-motion estimation and obstacle avoidance. This paper presents a comprehensive performance comparison of several SAC methods for classifying dynamic objects using radardata. Our extensive experiments show that each SAC method's pros and cons, and provide insights into the effective use of SAC methods for radar-based dynamic object classification. It is expected that this study will help to increase the accuracy of dynamic object classification in real-world scenarios and guide future research in this area.
MIMO radar networks are composed of multiple separated MIMO sensors and each radarsensor can be viewed as a subarray. Although the MIMO radar networks can obtain a larger virtual aperture, excessive node or subarray ...
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Multi-sensor fusion-based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather-s...
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ISBN:
(纸本)9798350323658
Multi-sensor fusion-based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather-sensitive, while the FMCW radar has strong adaptability but suffers from noise and ghost effects. In this paper, we propose a heterogeneous localization method of radar on LiDAR Map (RoLM), which can eliminate the accumulated error of radar odometry in real-time to achieve higher localization accuracy without dependence on loop closures. We embed the two sensor modalities into a density map and calculate the spatial vector similarity with offset to seek the corresponding place index in the candidates and calculate the rotation and translation. We use the ICP to pursue perfect matching on the LiDAR submap based on the coarse alignment. Extensive experiments on Mulran radardataset, Oxford radar RobotCar dataset, and our data verify the feasibility and effectiveness of our approach.
In order to solve the problems of short-time data loss, low positioning accuracy and asynchronous sensor frequency of agricultural unmanned vehicles, the application of multi-sensor fusion positioning algorithm of int...
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