Particle Filter Based Algorithm for Target Position Estimation Under Sparce Sensor Surveillance

2006 ◽  
Author(s):  
Adele Doser ◽  
Gregg Whitford
Keyword(s):  
Particle Filter ◽  
Target Position ◽  
Position Estimation

scholarly journals Calibration Method of Array Errors for Wideband MIMO Imaging Radar Based on Multiple Prominent Targets

2021 ◽  
Vol 13 (15) ◽  
pp. 2997
Author(s):  
Zheng Zhao ◽  
Weiming Tian ◽  
Yunkai Deng ◽  
Cheng Hu ◽  
Tao Zeng
Keyword(s):  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Target Position ◽  
Position Estimation ◽  
Least Square ◽  
Antenna Structure ◽  
Imaging Radar ◽  
Position Errors ◽  
Calibration Methods ◽  
Array Errors

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets.

scholarly journals A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 272 ◽  
Author(s):  
Ajmal Hinas ◽  
Roshan Ragel ◽  
Jonathan Roberts ◽  
Felipe Gonzalez
Keyword(s):  
Target Detection ◽  
Low Cost ◽  
Target Position ◽  
Position Estimation ◽  
Quality Data ◽  
Unmanned Aerial Systems ◽  
Modular Software ◽  
Ground Target ◽  
High Level ◽  
Aerial Systems

Small unmanned aerial systems (UASs) now have advanced waypoint-based navigation capabilities, which enable them to collect surveillance, wildlife ecology and air quality data in new ways. The ability to remotely sense and find a set of targets and descend and hover close to each target for an action is desirable in many applications, including inspection, search and rescue and spot spraying in agriculture. This paper proposes a robust framework for vision-based ground target finding and action using the high-level decision-making approach of Observe, Orient, Decide and Act (OODA). The proposed framework was implemented as a modular software system using the robotic operating system (ROS). The framework can be effectively deployed in different applications where single or multiple target detection and action is needed. The accuracy and precision of camera-based target position estimation from a low-cost UAS is not adequate for the task due to errors and uncertainties in low-cost sensors, sensor drift and target detection errors. External disturbances such as wind also pose further challenges. The implemented framework was tested using two different test cases. Overall, the results show that the proposed framework is robust to localization and target detection errors and able to perform the task.

GMSPPF for Magnetic Ellipsoid Tracking with a Magnetic Gradiometer Tensor

2013 ◽  
Vol 779-780 ◽  
pp. 1789-1792
Author(s):  
Zhi Dong Wu ◽  
Sui Hua Zhou ◽  
Hong Xing Zhang
Keyword(s):  
Particle Filter ◽  
Magnetic Moment ◽  
Target Position ◽  
Gaussian Mixture ◽  
State Density ◽  
Point Particle ◽  
Unknown Parameters ◽  
Sigma Point ◽  
Using Data

Magnetic ellipsoid tracking problem is characterized by high nonlinearity. In this study, the determination of target position, magnetic moment, and velocity is formulated as a Bayesian estimation problem for dynamic systems, a recursive approach is proposed to estimate the trajectory and magnetic moment component of the target using data collected with a magnetic gradiometer tensor. Particle filter provides a solution to this problem. In addition to the conventional particle filter, the proposed tracking and classification algorithm uses Gaussian mixed mode to represent the posterior state density of the unknown parameters, which is named as Gaussian mixture sigma point particle filters(GMSPPF). The performance of the proposed method has been evaluated through simulation experiment. The results indicate that the method has achieved the magnetic ellipsoid tracking and GMSPPF has better estimation performance and less computational complexity than other related algorithms.

Pedestrian Tracking with HOG and Color Histogram Features

2012 ◽  
Vol 241-244 ◽  
pp. 498-501
Author(s):  
Lie Guo ◽  
Guang Xi Zhang ◽  
Ping Shu Ge ◽  
Lin Hui Li
Keyword(s):  
Particle Filter ◽  
Target Position ◽  
Color Histogram ◽  
Experimental Results ◽  
Pedestrian Tracking ◽  
Particle Sampling ◽  
Histograms Of Oriented Gradients ◽  
Initial Target

To improve the effectiveness of pedestrian tracking, the histograms of oriented gradients (HOG) and color histogram characteristics are adopted to track pedestrian based on particle filter. Firstly, the pedestrian is detected using the HOG features to determine the initial target position. Then the target is tracked based on particle filter utilizing color histogram, during which the HOG is used to modify particle heavy weights and particle sampling. Experimental results verify the accurateness and efficiency of the proposed method.

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