Road-Map Aided VSIMM-GMPHD Filter for Ground Moving Target Tracking

Zheng, Jihong; Gao, Meiguo; Yu, Haojie

doi:10.1109/compcomm.2018.8781023

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Using Equation (10) to update the particle weight is still difficult to operate. Here, we draw on the method of paper [23]. The weight of particles is updated in the unit of a grid cell.…”

Section: Particle Weights Updatementioning

confidence: 99%

“…When calculating the size and reference position of the rectangular box, not only the relevant occupancy grid cells is considered, but also the measured free space evidence adjacent to each object box is considered, making the estimation more robust and accurate. In the paper [23], the dynamic object is represented as a modified occupancy grid map. Unlike traditional occupancy grid representation, the modified grid map is only used to keep track of the grids that may be occupied at the next moment.…”

Section: Introductionmentioning

confidence: 99%

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A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment

2021

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The PHD (Probability Hypothesis Density) filter is a sub-optimal multi-target Bayesian filter based on a random finite set, which is widely used in the tracking and estimation of dynamic objects in outdoor environments. Compared with the outdoor environment, the indoor environment space and the shape of dynamic objects are relatively small, which puts forward higher requirements on the estimation accuracy and response speed of the filter. This paper proposes a method for fast and high-precision estimation of the dynamic objects’ velocity for mobile robots in an indoor environment. First, the indoor environment is represented as a dynamic grid map, and the state of dynamic objects is represented by its grid cells state as random finite sets. The estimation of dynamic objects’ speed information is realized by using the measurement-driven particle-based PHD filter. Second, we bound the dynamic grid map to the robot coordinate system and derived the update equation of the state of the particles with the movement of the robot. At the same time, in order to improve the perception accuracy and speed of the filter for dynamic targets, the CS (Current Statistical) motion model is added to the CV (Constant Velocity) motion model, and interactive resampling is performed to achieve the combination of the advantages of the two. Finally, in the Gazebo simulation environment based on ROS (Robot Operating System), the speed estimation and accuracy analysis of the square and cylindrical dynamic objects were carried out respectively when the robot was stationary and in motion. The results show that the proposed method has a great improvement in effect compared with the existing methods.

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“…Using Equation (10) to update the particle weight is still difficult to operate. Here, we draw on the method of paper [23]. The weight of particles is updated in the unit of a grid cell.…”

Section: Particle Weights Updatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment

2021

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“…Pasha et al proposed the GM-PHD filter using JMS models [21,22] and non-linear JMS models [23]. These methods have been implemented in many applications for manoeuvring target tracking [24][25][26][27][28][29]. In Refs.…”

Section: Introductionmentioning

confidence: 99%

Multiple‐model Gaussian mixture probability hypothesis density filter based on jump Markov system with state‐dependent probabilities

Sun

Kim

Hwang

2022

IET Radar Sonar & Navi

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The Gaussian mixture probability density (GM‐PHD) filter has become a popular approach to solve the multiple‐target tracking (MTT) problem because it can effectively and efficiently estimate the number of targets and target states that change over time from noisy measurements. In the GM‐PHD filter, the target detection and survival probabilities, and the birth rate are assumed to be constant, irrespective of the target state. However, in some applications, for example, when the target follows the planned trajectory, the detection, survival and birth of the target depend on its state. Besides, when the target reaches a waypoint along the planned trajectory, it will take a manoeuvre to go to the next waypoint, but the GM‐PHD filter does not accommodate manoeuvring targets that switch between several motion models. To address this, we propose a multiple‐model GM‐PHD filter with state‐dependent probabilities, which can explicitly consider the mode transition probabilities, the probabilities of detection and survival, and the birth rate as a function of the target state. The performance of the proposed algorithm is demonstrated by illustrative MTT scenarios which include target manoeuvres during occlusion.

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Dynamic Interference Source Tracking Based on Motion State Decomposition

Zhang,

Shi

et al. 2024

2024 International Wireless Communications and Mobile Computing (IWCMC)

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Road-Map Aided VSIMM-GMPHD Filter for Ground Moving Target Tracking

Cited by 3 publications

References 19 publications

A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment

A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment

Multiple‐model Gaussian mixture probability hypothesis density filter based on jump Markov system with state‐dependent probabilities

Dynamic Interference Source Tracking Based on Motion State Decomposition

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