An improved approach for multi-target detection and tracking in automotive radar systems

Khalil, Mohammed M.; Eltrass, Ahmed S.; Elzaafarany, Omar; Galal, Basem; Walid, Khaled; Tarek, Ahmed; Ahmadien, Omar

doi:10.1109/iceaa.2016.7731433

Cited by 16 publications

(12 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The trajectory of each object is determined at the beginning of radar scanning; then, measurement information from the sensors is used to update the initially established target track. When measurement data (echo) from the sensor enters the target's tracking gate, the echo is called a valid measurement (or valid back) wave [28][29][30][31]. Even with only one target, there may be multiple valid measurements due to clutter interference.…”

Section: Target Tracking Backgroundmentioning

confidence: 99%

An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios

Tang

Hong

et al. 2019

Electronics

View full text Add to dashboard Cite

The 5G cellular network is expected to provide core service platform for the expanded Internet of Things (IoT) by supporting enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low latency communications (URLLC). Unmanned aerial vehicles (UAVs), also known as drones, provide civil, commercial, and government services in various fields. Particularly in a 5G IoT scenario, UAV-aided network communications will fulfill an increasingly important role and will require the tracking of multiple UAV targets. As UAVs move quickly, maintaining the stability of the communication connection in 5G will be a challenge. Therefore, it is necessary to track the trajectory of UAVs. At present, the GM-PHD filter has a problem that the new target intensity must be known, and it cannot obtain the moving target trajectory and the influence of the clutter is likely to cause false alarm. A UAV-PHD filter is proposed in this work to improve the traditional GM-PHD filter by applying machine learning to the emergency detection and trajectory tracking of UAV targets. An out-of-sight detection algorithm for multiple UAVs is then presented to improve tracking performance. The method is assessed by simulation using MATLAB, and OSPA distance is utilized as an evaluation indicator. The simulation results illustrate that the proposed method can be applied to the tracking of multiple UAV targets in future 5G-IoT scenarios, and the performance is superior to the traditional GM-PHD filter.

show abstract

Section: Target Tracking Backgroundmentioning

confidence: 99%

An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios

Tang

Hong

et al. 2019

Electronics

View full text Add to dashboard Cite

show abstract

“…In this study, the rapid chirps’ waveform is employed to obtain the targets’ ranges and velocities using frequency modulated CW radar architecture [4]. A sequence of waveform signals with up and down chirps (see Fig.…”

Section: Target Detection With Automotive Radarmentioning

confidence: 99%

“…Continuous wave (CW) radars are superior over pulsed radars in terms of the transmission bandwidth, target mobility, and safety [1, 3]. The most appropriate CW form for automotive radar applications is the rapid chirp waveform due to its ability to distinguish the position and velocity of multiple targets simultaneously and unambiguously using low power and memory requirements [1, 4]. The automotive radar tracking system consists of four main components: (i) the target dynamic model, (ii) state prediction and update, (iii) data association, and (iv) track management [4].…”

Section: Introductionmentioning

confidence: 99%

“…The most appropriate CW form for automotive radar applications is the rapid chirp waveform due to its ability to distinguish the position and velocity of multiple targets simultaneously and unambiguously using low power and memory requirements [1, 4]. The automotive radar tracking system consists of four main components: (i) the target dynamic model, (ii) state prediction and update, (iii) data association, and (iv) track management [4]. The most common motion models for target dynamics are constant velocity (CV), constant acceleration (CA), and constant turn rate (CTR).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automotive radar system for multiple‐vehicle detection and tracking in urban environments

Eltrass

Khalil

2018

IET Intelligent Transport Systems

Self Cite

View full text Add to dashboard Cite

“…The Constant Turn Rate and Acceleration (CTRA) model [23] considers the motion clothoid of the target. It yields better performance than other state-of-theart models such as the Constant Velocity (CV) and Constant Acceleration (CA) models for general motion tracking [24]. It assumes constant turn rate and tangential acceleration of the target.…”

Section: Introductionmentioning

confidence: 99%

Self-Corrective Sensor Fusion for Drone Positioning in Indoor Facilities

et al. 2021

View full text Add to dashboard Cite

Drones may be more advantageous than fixed cameras for quality control applications in industrial facilities, since they can be redeployed dynamically and adjusted to production planning. The practical scenario that has motivated this paper, image acquisition with drones in a car manufacturing plant, requires drone positioning accuracy in the order of 5 cm. During repetitive manufacturing processes, it is assumed that quality control imaging drones will follow highly deterministic periodic paths, stop at predefined points to take images and send them to image recognition servers. Therefore, by relying on prior knowledge about production chain schedules, it is possible to optimize the positioning technologies for the drones to stay at all times within the boundaries of their flight plans, which will be composed of stopping points and the paths in between. This involves mitigating issues such as temporary blocking of line-of-sight between the drone and any existing radio beacons; sensor data noise; and the loss of visual references. We present a self-corrective solution for this purpose. It corrects visual odometer readings based on filtered and clustered Ultra-Wide Band (UWB) data, as an alternative to direct Kalman fusion. The approach combines the advantages of these technologies when at least one of them works properly at any measurement spot. It has three method components: independent Kalman filtering, data association by means of stream clustering and mutual correction of sensor readings based on the generation of cumulative correction vectors. The approach is inspired by the observation that UWB positioning works reasonably well at static spots whereas visual odometer measurements reflect straight displacements correctly but can underestimate their length. Our experimental results demonstrate the advantages of the approach in the application scenario over Kalman fusion, in terms of stopping point detection and trajectory estimation error.INDEX TERMS Ultra-Wide Band, visual odometer, sensor fusion, wireless technologies, drone positioning, industry applications, Industry 4.0, quality control.

show abstract

An improved approach for multi-target detection and tracking in automotive radar systems

Cited by 16 publications

References 5 publications

An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios

An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios

Automotive radar system for multiple‐vehicle detection and tracking in urban environments

Self-Corrective Sensor Fusion for Drone Positioning in Indoor Facilities

Contact Info

Product

Resources

About