107 research outputs found
A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking
[EN]We review some advances of the particle filtering (PF) algorithm that have been achieved
in the last decade in the context of target tracking, with regard to either a single target or multiple
targets in the presence of false or missing data. The first part of our review is on remarkable
achievements that have been made for the single-target PF from several aspects including importance
proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal
systems. The second part of our review is on analyzing the intractable challenges raised within
the general multitarget (multi-sensor) tracking due to random target birth and termination, false
alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream
multitarget PF approaches consist of two main classes, one based on M2T association approaches and
the other not such as the finite set statistics-based PF. In either case, significant challenges remain due
to unknown tracking scenarios and integrated tracking management
GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System
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 timevarying number of objects. However, the conventional GM-PHD\ud
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
A New Multiple Hypothesis Tracker Integrated with Detection Processing.
In extant radar signal processing systems, detection and tracking are carried out independently, and detected measurements are utilized as inputs to the tracking procedure. Therefore, the tracking performance is highly associated with detection accuracy, and this performance may severely degrade when detections include a mass of false alarms and missed-targets errors, especially in dense clutter or closely-spaced trajectories scenarios. To deal with this issue, this paper proposes a novel method for integrating the multiple hypothesis tracker with detection processing. Specifically, the detector acquires an adaptive detection threshold from the output of the multiple hypothesis tracker algorithm, and then the obtained detection threshold is employed to compute the score function and sequential probability ratio test threshold for the data association and track estimation tasks. A comparative analysis of three tracking algorithms in a clutter dense scenario, including the proposed method, the multiple hypothesis tracker, and the global nearest neighbor algorithm, is conducted. Simulation results demonstrate that the proposed multiple hypothesis tracker integrated with detection processing method outperforms both the standard multiple hypothesis tracker algorithm and the global nearest neighbor algorithm in terms of tracking accuracy
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