The Target Vehicle Movement State Estimation Method with Radar Based on Kalman Filtering Algorithm

Xin, Jia; Wu, Zuo Long; Guan, Hongxia

doi:10.4028/www.scientific.net/amm.347-350.638

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…In 2013, 2015, and 2016 [75,100,101], the authors implemented a machine learning technique for the UAV network as a potential solution. The machine learning technique is seen as a promising approach in this field since it can predict node mobility.…”

Section: Related Workmentioning

confidence: 99%

Mobility Management of Unmanned Aerial Vehicles in Ultra–Dense Heterogeneous Networks

Al-Shaibani

Shayea

Çağlar

et al. 2022

Sensors

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The rapid growth of mobile data traffic will lead to the deployment of Ultra–Dense Networks (UDN) in the near future. Various networks must overlap to meet the massive demands of mobile data traffic, causing an increase in the number of handover scenarios. This will subsequently affect the connectivity, stability, and reliability of communication between mobile and serving networks. The inclusion of Unmanned Aerial Vehicles (UAVs)—based networks will create more complex challenges due to different mobility characterizations. For example, UAVs move in three–dimensions (3D), with dominant of line–of–sight communication links and faster mobility speed scenarios. Assuring steady, stable, and reliable communication during UAVs mobility will be a major problem in future mobile networks. Therefore, this study provides an overview on mobility (handover) management for connected UAVs in future mobile networks, including 5G, 6G, and satellite networks. It provides a brief overview on the most recent solutions that have focused on addressing mobility management problems for UAVs. At the same time, this paper extracts, highlights, and discusses the mobility management difficulties and future research directions for UAVs and UAV mobility. This study serves as a part of the foundation for upcoming research related to mobility management for UAVs since it reviews the relevant knowledge, defines existing problems, and presents the latest research outcomes. It further clarifies handover management of UAVs and highlights the concerns that must be solved in future networks.

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Section: Related Workmentioning

confidence: 99%

Mobility Management of Unmanned Aerial Vehicles in Ultra–Dense Heterogeneous Networks

Al-Shaibani

Shayea

Çağlar

et al. 2022

Sensors

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“…State estimation is divided into state prediction, measurement prediction, and state update, which is the same as the Kalman filter [22]. Assuming that the acceleration of the target is constant in a short time, a motion model with constant acceleration can be established.…”

Section: Tracking Processing Modulementioning

confidence: 99%

Research on Target Detection Based on Distributed Track Fusion for Intelligent Vehicles

Chen

Pei

Chen

2019

Sensors

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Accurate target detection is the basis of normal driving for intelligent vehicles. However, the sensors currently used for target detection have types of defects at the perception level, which can be compensated by sensor fusion technology. In this paper, the application of sensor fusion technology in intelligent vehicle target detection is studied with a millimeter-wave (MMW) radar and a camera. The target level fusion hierarchy is adopted, and the fusion algorithm is divided into two tracking processing modules and one fusion center module based on the distributed structure. The measurement information output by two sensors enters the tracking processing module, and after processing by a multi-target tracking algorithm, the local tracks are generated and transmitted to the fusion center module. In the fusion center module, a two-level association structure is designed based on regional collision association and weighted track association. The association between two sensors’ local tracks is completed, and a non-reset federated filter is used to estimate the state of the fusion tracks. The experimental results indicate that the proposed algorithm can complete a tracks association between the MMW radar and camera, and the fusion track state estimation method has an excellent performance.

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“…The main objective of this work is to develop a unified framework suitable to predict motion trajectories of mobile entities of different types. The core of our method is based on Kalman filtering with intermittent observation [34]. However, we use the object's type-specific motion properties to improve the prediction accuracy through deploying a novel generative model for the system input.…”

Section: Related Workmentioning

confidence: 99%

A unified framework for joint mobility prediction and object profiling of drones in UAV networks

et al. 2018

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In recent years, using a network of autonomous and cooperative unmanned aerial vehicles (UAVs) without command and communication from the ground station has become more imperative, in particular in search-and-rescue operations, disaster management, and other applications where human intervention is limited. In such scenarios, UAVs can make more efficient decisions if they acquire more information about the mobility, sensing and actuation capabilities of their neighbor nodes. In this paper, we develop an unsupervised online learning algorithm for joint mobility prediction and object profiling of UAVs to facilitate control and communication protocols. The proposed method not only predicts the future locations of the surrounding flying objects, but also classifies them into different groups with similar levels of maneuverability (e.g. rotatory, and fixed-wing UAVs) without prior knowledge about these classes. This method is flexible in admitting new object types with unknown mobility profiles, thereby applicable to emerging flying Ad-hoc networks with heterogeneous nodes.

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The Target Vehicle Movement State Estimation Method with Radar Based on Kalman Filtering Algorithm

Cited by 6 publications

References 4 publications

Mobility Management of Unmanned Aerial Vehicles in Ultra–Dense Heterogeneous Networks

Mobility Management of Unmanned Aerial Vehicles in Ultra–Dense Heterogeneous Networks

Research on Target Detection Based on Distributed Track Fusion for Intelligent Vehicles

A unified framework for joint mobility prediction and object profiling of drones in UAV networks

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