scholarly journals Moving-Target Position Estimation Using GPU-Based Particle Filter for IoT Sensing Applications

2017 ◽  
Vol 7 (11) ◽  
pp. 1152 ◽  
Author(s):  
Seongseop Kim ◽  
Jeonghun Cho ◽  
Daejin Park
Keyword(s):  
Particle Filter ◽  
Target Position ◽  
Position Estimation ◽  
Moving Target ◽  
Sensing Applications

Distributed Particle Filter With Online Model Learning for Localization Using Time-Difference-of-Arrival (TDOA) Measurements

2020 ◽  
Author(s):  
Chandler J. Panetta ◽  
Osama N. Ennasr ◽  
Xiaobo Tan
Keyword(s):  
Particle Filter ◽  
Nonlinear Filtering ◽  
Probability Distributions ◽  
Position Estimation ◽  
Propagation Model ◽  
Time Difference ◽  
Moving Target ◽  
Model Learning ◽  
Movement Model ◽  
Time Difference Of Arrival

Abstract The problem of localizing a moving target arises in various forms in wireless sensor networks. Deploying multiple sensing receivers and using the time-difference-of-arrival (TDOA) of the target’s emitted signal is widely considered an effective localization technique. Traditionally, TDOA-based algorithms adopt a centralized approach where all measurements are sent to a predefined reference node for position estimation. More recently, distributed TDOA-based localization algorithms have been shown to improve the robustness of these estimates. For target models governed by highly stochastic processes, the method of nonlinear filtering and state estimation must be carefully considered. In this work, a distributed TDOA-based particle filter algorithm is proposed for localizing a moving target modeled by a discrete-time correlated random walk (DCRW). We present a method for using data collected by the particle filter to estimate the unknown probability distributions of the target’s movement model, and then apply the distribution estimates to recursively update the particle filter’s propagation model. The performance of the distributed approach is evaluated through numerical simulation, and we show the benefit of using a particle filter with online model learning by comparing it with the non-adaptive approach.

On strategy for determining displacement solely from perturbed phase of self mixing interferometry

2020 ◽  
Author(s):  
Junaid Khan
Keyword(s):  
Coupling Factor ◽  
Moving Target ◽  
Linear Phase ◽  
Spectral Filtering ◽  
Signal Shape ◽  
Phase Equation ◽  
Sensing Applications ◽  
Computationally Expensive ◽  
Fixed Point Methods ◽  
Factor C

While self mixing interferometry(SMI) has proven to be suitable for displacement measurement and other sensing applications,its characteristic self mixing signal shape is strongly governed by the non-linear phase equation which forms relation between perturbed and unperturbed phase of self mixing laser.Therefore, while it is desirable for robust estimation of displacement of moving target, the algorithms to achieve this must have an objective strategy which can be achieved by understanding the characteristic of extracting knowledge of perturbed phase from unperturbed phase. Therefore, it has been proved and shown that such strategy must not involve sole methods where perturbed phase is continuous function of unperturbed phase (e.g:Taylor series or fixed point methods) or through successive displacements (e.g: variations of Gauss Seidal method). Subset of this strategy is to perform spectral filtering of perturbed phase followed by perturbative or homotopic deformation. A less computationally expensive approach of this strategy is adopted to achieve displacement with mean error of 62.2nm covering all feedback regimes, when coupling factor 'C' is unknown.<br>

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.

Ground-Constrained Motion Target Tracking for Monocular Sequence Images

2018 ◽  
Vol 32 (12) ◽  
pp. 1850044 ◽  
Author(s):  
Ling Guo
Keyword(s):  
Background Subtraction ◽  
Target Position ◽  
Video Monitoring ◽  
Moving Target ◽  
Constrained Motion ◽  
Otsu Method ◽  
Test Platform ◽  
Monitoring Test ◽  
Nonlinear Compensation ◽  
Nonlinear Imaging

For the detection of a moving target position in video monitoring images, the existing locating tracking systems mainly adopt binocular or structured light stereoscopic technology, which has drawbacks such as system design complexity and slow detection speed. In light of these limitations, a tracking method for monocular sequence moving targets is presented, with the introduction of ground constraints into monocular visual monitoring; the principle and process of the method are introduced in detail in this paper. This method uses camera installation information and geometric imaging principles combined with nonlinear compensation to derive the calculation formula for the actual position of the ground moving target in monocular asymmetric nonlinear imaging. The footprint location of a walker is searched in the sequence imaging of a monitoring test platform that is built indoors. Because of the shadow of the walker in the image, the multi-threshold OTSU method based on test target background subtraction is used here to segment the images. The experimental results verify the effectiveness of the proposed method.

A 24 GHz ISM-Band Doppler Radar Antenna With High Isolation Characteristic for Moving Target Sensing Applications

2019 ◽  
Vol 18 (7) ◽  
pp. 1532-1536 ◽  
Author(s):  
Sungpeel Kim ◽  
Dong Kyoo Kim ◽  
Youjin Kim ◽  
Jaehoon Choi ◽  
Kyung-Young Jung
Keyword(s):  
Doppler Radar ◽  
Moving Target ◽  
Ism Band ◽  
High Isolation ◽  
Sensing Applications ◽  
Radar Antenna ◽  
24 Ghz
Sign in / Sign up

Export Citation Format

Share Document