An improvement on GM-PHD filter for target tracking in presence of subsequent miss-detection

Yazdian-Dehkordi, Mahdi; Azimifar, Zohreh

doi:10.1109/iraniancee.2015.7146316

Cited by 4 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A multi-frame scheme is introduced in [29] to deal with estimates of undetected targets. To preserve the standard measurement model, a penalization scheme [30] and a a competitive algorithm [31] is introduced to renormalize weights along the objects and measurements.…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System

Lindenmaier

Aradi

Bécsi

et al. 2022

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Advanced driver assistance systems and highly automated driving functions require an enhanced frontal perception system. The requirements of a frontal environment perception system cannot be satisfied by either of the existing automotive sensors. A commonly used sensor cluster for these functions consists of a mono-vision smart camera and automotive radar. The sensor fusion is intended to combine the data of these sensors to perform a robust environment perception. Multi-object tracking algorithms have a suitable software architecture for sensor data fusion. Several multi-object tracking algorithms, such as JPDAF or MHT, have good tracking performance; however, the computational requirements of these algorithms are significant according to their combinatorial complexity. The GM-PHD filter is a straightforward algorithm with favorable runtime characteristics that can track an unknown and time-varying number of objects. However, the conventional GM-PHD filter has a poor performance in object cardinality estimation. This paper proposes a method that extends the GM-PHD filter with an object birth model that relies on the sensor detections and a robust object extraction module, including Bayesian estimation of objects' existence probability to compensate for drawbacks of the conventional algorithm.

show abstract

Section: A Related Workmentioning

confidence: 99%

“…In ( 30) and (32) Δt denotes the elapsed time between timestep k and k − 1. In (31) σ a refers to the acceleration scale.…”

Section: Model Descriptionmentioning

confidence: 99%

GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System

Lindenmaier

Aradi

Bécsi

et al. 2022

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…Intermittent measurements are a common occurrence in practical applications due to occlusions, detection blind zones (DBZs), and low frame rates caused by radar operation. To handle subsequent miss-detections, Mahdi proposed the Interacting Multiple Model PHD tracker [ 19 ], which does not output track labels. Another method, the multiple-model multiple-hypothesis PHD (MM-MH-PHD) filter, adopts a multiple-model approach to estimate motion states in blind zones [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Network-Based Tracklets Data Association for Multiple Extended Target Tracking with Intermittent Measurements

Jiang

et al. 2023

Sensors

View full text Add to dashboard Cite

The key issue of multiple extended target tracking is to differentiate the origins of the measurements. The association of measurements with the possible origins within the target’s extent is difficult, especially for occlusions or detection blind zones, which cause intermittent measurements. To solve this problem, a hierarchical network-based tracklet data association algorithm (ET-HT) is proposed. At the low association level, a min-cost network flow model based on the divided measurement sets is built to extract the possible tracklets. At the high association level, these tracklets are further associated with the final trajectories. The association is formulated as an integral programming problem for finding the maximum a posterior probability in the network flow model based on the tracklets. Moreover, the state of the extended target is calculated using the in-coordinate interval Kalman smoother. Simulation and experimental results show the superiority of the proposed ET-HT algorithm over JPDA- and RFS-based methods when measurements are intermittently unavailable.

show abstract

“…On one hand, the intensity of the new target needs to be known, On the other hand, it has no trajectory correlation function. Gaussian mixture probability hypothesis density (GM-PHD) filters are widely used to track multiple targets [21,22]. The GM-PHD filter can propagate the posterior probability density function related to multiple targets, and avoids the data interconnection problem between the target and the measured value through recursion.…”

mentioning

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

An improvement on GM-PHD filter for target tracking in presence of subsequent miss-detection

Cited by 4 publications

References 14 publications

GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System

GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System

Hierarchical Network-Based Tracklets Data Association for Multiple Extended Target Tracking with Intermittent Measurements

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

Contact Info

Product

Resources

About