Multiple-model Probability Hypothesis Density Smoother

2010 ◽  
Vol 36 (7) ◽  
pp. 939-950 ◽  
Author(s):  
Feng LIAN ◽  
Chong-Zhao HAN ◽  
Wei-Feng LIU ◽  
Xiang-Hui YUAN
Keyword(s):  
Multiple Model ◽  
Probability Hypothesis Density ◽  
Model Probability

scholarly journals Tracking multiple maneuvering targets hidden in the DBZ based on the MM-PHD filter

2020 ◽  
Author(s):  
Weihua Wu
Keyword(s):  
Label Propagation ◽  
Tracking Performance ◽  
Moving Target ◽  
Multiple Model ◽  
Probability Hypothesis Density ◽  
Maneuvering Target ◽  
Maneuvering Targets ◽  
Model Probability ◽  
Track Initiation ◽  
Ground Moving Target Indication

<p><a></a><a></a><a>For a ground moving target indication (GMTI) radar, the presence of </a><a></a><a></a><a></a><a>Doppler blind zone (DBZ)</a> results in many short tracks with frequent label switching, which seriously deteriorates the tracking performance. When the DBZ masking is coupled with targets maneuvering, tracking multiple maneuvering targets hidden in the DBZ becomes very challenging, which is reflected in the fact that there is no public research on this issue. To overcome this complicated problem, we propose a practical and fully functional GMTI multi-maneuvering-target tracker based on the multiple model probability hypothesis density (MM-PHD) filter. Unlike the standard MM-PHD filter, the proposed tracker utilizes the Doppler information and incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Furthermore, to cope with the problems of the fixed initiation and no label output of the standard MM-PHD filter, the resulting MM-PHD filter with the Doppler and MDV information is augmented with measurement-driven adaptive track initiation and track label propagation, which are necessary for a practical tracker and also required for evaluating the overall GMTI tracking performance. Finally, numerical examples show that the proposed tracker outperforms significantly the existing ones, thus verifying its effectiveness.</p> <p> </p>

scholarly journals Tracking multiple maneuvering targets hidden in the DBZ based on the MM-PHD filter

2020 ◽  
Author(s):  
Weihua Wu
Keyword(s):  
Label Propagation ◽  
Tracking Performance ◽  
Moving Target ◽  
Multiple Model ◽  
Probability Hypothesis Density ◽  
Maneuvering Target ◽  
Maneuvering Targets ◽  
Model Probability ◽  
Track Initiation ◽  
Ground Moving Target Indication

<p><a></a><a></a><a>For a ground moving target indication (GMTI) radar, the presence of </a><a></a><a></a><a></a><a>Doppler blind zone (DBZ)</a> results in many short tracks with frequent label switching, which seriously deteriorates the tracking performance. When the DBZ masking is coupled with targets maneuvering, tracking multiple maneuvering targets hidden in the DBZ becomes very challenging, which is reflected in the fact that there is no public research on this issue. To overcome this complicated problem, we propose a practical and fully functional GMTI multi-maneuvering-target tracker based on the multiple model probability hypothesis density (MM-PHD) filter. Unlike the standard MM-PHD filter, the proposed tracker utilizes the Doppler information and incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Furthermore, to cope with the problems of the fixed initiation and no label output of the standard MM-PHD filter, the resulting MM-PHD filter with the Doppler and MDV information is augmented with measurement-driven adaptive track initiation and track label propagation, which are necessary for a practical tracker and also required for evaluating the overall GMTI tracking performance. Finally, numerical examples show that the proposed tracker outperforms significantly the existing ones, thus verifying its effectiveness.</p> <p> </p>

scholarly journals Tracking of Multiple Maneuvering Random Hypersurface Extended Objects Using High Resolution Sensors

2021 ◽  
Vol 13 (15) ◽  
pp. 2963
Author(s):  
Lifan Sun ◽  
Haofang Yu ◽  
Jian Lan ◽  
Zhumu Fu ◽  
Zishu He ◽  
...  
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
Gaussian Mixture ◽  
Joint Estimation ◽  
Accurate Estimation ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Probability Hypothesis Density ◽  
Extended Object ◽  
Geometrical Significance

With the increased resolution capability of modern sensors, an object should be considered as extended if the target extent is larger than the sensor resolution. Multiple maneuvering extended object tracking (MMEOT) uses not only measurements of the target centroid but also high-resolution sensor measurements which may resolve individual features or measurement sources. MMEOT aims to jointly estimate object number, centroid states, and extension states. However, unknown and time-varying maneuvers of multiple objects produce difficulties in terms of accurate estimation. For multiple maneuvering star-convex extended objects using random hypersurface models (RHMs) in particular, their complex maneuvering behaviors are difficult to be described accurately and handled effectively. To deal with these problems, this paper proposes an interacting multiple model Gaussian mixture probability hypothesis density (IMM-GMPHD) filter for multiple maneuvering extended object tracking. In this filter, linear maneuver models derived from RHMs are utilized to describe different turn maneuvers of star-convex extended objects accurately. Based on these, an IMM-GMPHD filtering recursive form is given by deriving new update and merging formulas of model probabilities for extended objects. Gaussian mixture components of different posterior intensities are also pruned and merged accurately. More importantly, the geometrical significance of object extension states is fully considered and exploited in this filter. This contributes to the accurate estimation of object extensions. Simulation results demonstrate the effectiveness of the proposed tracking approach—it can obtain the joint estimation of object number, kinematic states, and object extensions in complex maneuvering scenarios.

scholarly journals A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2208 ◽  
Author(s):  
Xiaoli Wang ◽  
Liangqun Li ◽  
Weixin Xie
Keyword(s):  
Kalman Filter ◽  
Particle Filtering ◽  
Unscented Kalman Filter ◽  
Fuzzy Model ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Model Probability ◽  
Fuzzy Parameter ◽  
Takagi Sugeno

In this paper, we propose a novel fuzzy expectation maximization (FEM) based Takagi-Sugeno (T-S) fuzzy particle filtering (FEMTS-PF) algorithm for a passive sensor system. In order to incorporate target spatial-temporal information into particle filtering, we introduce a T-S fuzzy modeling algorithm, in which an improved FEM approach is proposed to adaptively identify the premise parameters, and the model probability is adjusted by the premise membership functions. In the proposed FEM, the fuzzy parameter is derived by the fuzzy C-regressive model clustering method based on entropy and spatial-temporal characteristics, which can avoid the subjective influence caused by the artificial setting of the initial value when compared to the traditional FEM. Furthermore, using the proposed T-S fuzzy model, the algorithm samples particles, which can effectively reduce the particle degradation phenomenon and the parallel filtering, can realize the real-time performance of the algorithm. Finally, the results of the proposed algorithm are evaluated and compared to several existing filtering algorithms through a series of Monte Carlo simulations. The simulation results demonstrate that the proposed algorithm is more precise, robust and that it even has a faster convergence rate than the interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF).

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