scholarly journals A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 55665-55675
Author(s):  
Cheng Cheng ◽  
Jean-Yves Tourneret ◽  
Xiaodong Lu
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Measurement Model ◽  
Variational Inference ◽  
Model Uncertainties

Quantized feedback particle filter for unmanned aerial vehicles tracking with quantized measurements

2020 ◽  
pp. 095441002094268
Author(s):  
Yu Wang ◽  
Xiaogang Wang ◽  
Naigang Cui
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Unmanned Aerial Vehicles ◽  
Estimation Algorithm ◽  
Measurement Model ◽  
Measurement Noise ◽  
Quantization Noise ◽  
Passive Radar ◽  
Quantized Feedback ◽  
Aerial Vehicles

Many existing state estimation approaches assume that the measurement noise of sensors is Gaussian. However, in unmanned aerial vehicles tracking applications with distributed passive radar array, the measurements suffer from quantization noise due to limited communication bandwidth. In this paper, a novel state estimation algorithm referred to as the quantized feedback particle filter is proposed to solve unmanned aerial vehicles tracking with quantized measurements, which is an improvement of the feedback particle filter (FPF) for the case of quantization noise. First, a bearing-only quantized measurement model is presented based on the midriser quantizer. The relationship between quantized measurements and original measurements is analyzed. By assuming that the quantization satisfies [Formula: see text], Sheppard’s correction is used for calculating the variances of the measurement noise. Then, a set of controlled particles is used to approximate the posterior distribution. To cope with the quantization noise of passive radars, a new formula of the gain matrix is derived by modifying the measurement noise covariance. Finally, a typical two-passive radar unmanned aerial vehicles tracking scenario is performed by QFPF and compared with the three other algorithms. Simulation results verify the superiority of the proposed algorithm.

scholarly journals A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 743
Author(s):  
Xi Liu ◽  
Shuhang Chen ◽  
Xiang Shen ◽  
Xiang Zhang ◽  
Yiwen Wang
Keyword(s):  
State Estimation ◽  
Measurement Model ◽  
High Dimensional ◽  
Neural Firing ◽  
The Neural Network ◽  
Information Filter ◽  
Critical Technology ◽  
Dimensional Measurements ◽  
Non Gaussian ◽  
Low Dimensional

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.

scholarly journals Using UHF-RFID Signals for Robot Localization Inside Pipelines

2021 ◽  
Author(s):  
Amal Gunatilake ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda ◽  
Lasitha Piyathilaka ◽  
Poojaben Darji
Keyword(s):  
Particle Filter ◽  
Cost Effective ◽  
Measurement Model ◽  
Underground Water ◽  
Water Utilities ◽  
Robot Localization ◽  
Uhf Rfid ◽  
Localization Accuracy ◽  
Feature Based ◽  
Particle Filter Algorithm

<div>Underground water pipes are important to any country’s infrastructure. Overtime, the metallic pipes are prone to corrosion, which can lead to water leakage and pipe bursts. In order to prolong the service life of those assets, water utilities in Australia apply protective pipe linings. Long-term monitoring and timely intervention are crucial for maintaining those lining assets. However, the water utilities do not possess the comprehensive technology to achieve it. The main reasons for lacking such technology are the unavailability of sensors and accurate robot localization technologies. Feature based localization methods such as SLAM has limited use as the application of liners alters the features and the environment. Encoder based localization is not accurate enough to observe the evolution of defects over a long period of time requiring unique defect correspondence. This motivates us to explore accurate contact-less and wireless based localization methods. We propose a cost-effective localization method using UHFRFID signals for robot localization inside pipelines based on Gaussian process combined particle filter. Experiments carried out in field extracted pipe samples from the Sydney water pipe network show that using the RSSI and Phase data together in the measurement model with particle filter algorithm improves the localization accuracy up to 15 centimeters precision.</div>

A Diffraction Measurement Model and Particle Filter Tracking Method for RSS-Based DFL

2015 ◽  
Vol 33 (11) ◽  
pp. 2391-2403 ◽  
Author(s):  
Zhenghuan Wang ◽  
Heng Liu ◽  
Shengxin Xu ◽  
Xiangyuan Bu ◽  
Jianping An
Keyword(s):  
Particle Filter ◽  
Measurement Model ◽  
Diffraction Measurement ◽  
Tracking Method

scholarly journals Constrained Unscented Particle Filter for SINS/GNSS/ADS Integrated Airship Navigation in the Presence of Wind Field Disturbance

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 471 ◽  
Author(s):  
Zhaohui Gao ◽  
Dejun Mu ◽  
Yongmin Zhong ◽  
Chengfan Gu
Keyword(s):  
Wind Speed ◽  
Particle Filter ◽  
State Estimation ◽  
Wind Field ◽  
Satellite System ◽  
Integrated Navigation ◽  
Unscented Particle Filter ◽  
Field Disturbance ◽  
Global Navigation Satellite ◽  
Optimal State Estimation

Due to the disturbance of wind field, it is difficult to achieve precise airship positioning and navigation in the stratosphere. This paper presents a new constrained unscented particle filter (UPF) for SINS/GNSS/ADS (inertial navigation system/global navigation satellite system/atmosphere data system) integrated airship navigation. This approach constructs a wind speed model to describe the relationship between airship velocity and wind speed using the information output from ADS, and further establishes a mathematical model for SINS/GNSS/ADS integrated navigation. Based on these models, it also develops a constrained UPF to obtain system state estimation for SINS/GNSS/ADS integration. The proposed constrained UPF uses the wind speed model to constrain the UPF filtering process to effectively resist the influence of wind field on the navigation solution. Simulations and comparison analysis demonstrate that the proposed approach can achieve optimal state estimation for SINS/GNSS/ADS integrated airship navigation in the presence of wind field disturbance.

scholarly journals Iterative Truncated Unscented Particle Filter

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 214
Author(s):  
Yanbo Wang ◽  
Fasheng Wang ◽  
Jianjun He ◽  
Fuming Sun
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Process Models ◽  
Filter Method ◽  
Proposal Distribution ◽  
Truncation Method ◽  
Unscented Particle Filter

The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.

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