Results 241 to 250 of about 1,121,998 (275)
Some of the next articles are maybe not open access.
GM‐PHD Filter with State‐Dependent Clutter
Asian Journal of Control, 2016AbstractIn traditional filtering methods, clutter is often assumed to obey a uniform distribution over the entire monitoring area. For many sensors, however, clutter may concentrate in target‐containing regions. Under this condition, the performance of the traditional multi‐target tracking filter can be degraded.
Chen, Jinguang +4 more
openaire +2 more sources
Cluster-based efficient particle PHD filter
2015 International Conference on Control, Automation and Information Sciences (ICCAIS), 2015Particle probability hypothesis density filtering has become a tractable means for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in non-linear or non-Gaussian system in the presence of clutter and missing measurements.
Junjie Wang 0005 +4 more
openaire +1 more source
Approximate multisensor CPHD and PHD filters
2010 13th International Conference on Information Fusion, 2010The probability hypothesis density (PHD) filter and cardinalized probability hypothesis density (CPHD) filter are principled approximations of the general multitarget Bayes recursive filter. Both filters are single-sensor filters. Since their multisensor generalizations are computationally intractable, a further approximation-iterating their corrector ...
openaire +2 more sources
PHD and CPHD Filtering With Unknown Detection Probability
IEEE Transactions on Signal Processing, 2018A priori knowledge of target detection probability is of critical importance in the Gaussian mixture probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters. In addition, these two filters require that the process noise and measurement noise of the state propagated in the recursion be Gaussian.
Chenming Li +4 more
openaire +1 more source
A survey of PHD filter and CPHD filter implementations
SPIE Proceedings, 2007The probability hypothesis density (PHD) filter has attracted increasing interest since the author first introduced it in 2000. Potentially practical computational implementations of this filter have been devised, based on sequential Monte Carlo or on Gaussian mixture techniques.
openaire +1 more source
GM‐PHD Filter With Signal Features Of Emitter
Asian Journal of Control, 2014AbstractA new GM‐PHD filter for multiple emitter targets tracking is proposed in this paper. It integrates the signal features of emitter into the process of weights update. In the case of unknowing the distribution of signal features, the FCM algorithm is used for reference to calculate the correlation coefficients between the measurements and ...
Zhu, Youqing, Zhou, Shilin
openaire +1 more source
The Social Force PHD Filter for Tracking Pedestrians
IEEE Transactions on Aerospace and Electronic Systems, 2017This paper addresses the problem of tracking multiple pedestrians whose motion is dependent on one another. The behavior of a pedestrian may be often affected by the motion of other pedestrians, obstacles in the surrounding, and his/her intended destination. Hence, a motion modeling technique, which integrates the various factors that affect the motion
Krishnan Krishanth +4 more
openaire +1 more source
An Improved PHD Filter Based on Dynamic Programming
2018Traditional PHD filter for detecting and tracking weak targets does not work well in the case of low detection probability. In this paper, an improvement of PHD filtering based on dynamic programming is proposed. The method takes advantage of the correlation among the multi-frame data.
Meng Fang +3 more
openaire +2 more sources
A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment
IEEE Transactions on Intelligent Transportation Systems, 2018Environment monitoring remains a major challenge for mobile robots, especially in densely cluttered or highly populated dynamic environments, where uncertainties originated from environment and sensor significantly challenge the robot’s perception.
Hongqi Fan +4 more
openaire +2 more sources
SLAM with SC-PHD Filters: An Underwater Vehicle Application
IEEE Robotics & Automation Magazine, 2014The random finite-set formulation for multiobject estimation provides a means of estimating the number of objects in cluttered environments with missed detections within a unified probabilistic framework. This methodology is now becoming the dominant mathematical framework within the sensor fusion community for developing multiple-target tracking ...
Chee Sing Lee 0001 +4 more
openaire +4 more sources