scholarly journals Particle Filter for Randomly Delayed Measurements with Unknown Latency Probability

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5689
Author(s):  
Ranjeet Kumar Tiwari ◽  
Shovan Bhaumik ◽  
Paresh Date ◽  
Thiagalingam Kirubarajan
Keyword(s):  
Particle Filter ◽  
State Of The Art ◽  
Measurement Model ◽  
Measurement Noise ◽  
Stationary Growth ◽  
Random Delays ◽  
Maximum Likelihood Criterion ◽  
Delayed Measurements ◽  
Noise Sequence ◽  
Probability Density Function Pdf

This paper focuses on developing a particle filter based solution for randomly delayed measurements with an unknown latency probability. A generalized measurement model that includes measurements randomly delayed by an arbitrary but fixed maximum number of time steps along with random packet drops is proposed. Owing to random delays and packet drops in receiving the measurements, the measurement noise sequence becomes correlated. A model for the modified noise is formulated and subsequently its probability density function (pdf) is derived. The recursion equation for the importance weights is developed using pdf of the modified measurement noise in the presence of random delays. Offline and online algorithms for identification of the unknown latency parameter using the maximum likelihood criterion are proposed. Further, this work explores the conditions that ensure the convergence of the proposed particle filter. Finally, three numerical examples, one with a non-stationary growth model and two others with target tracking, are simulated to show the effectiveness and the superiority of the proposed filter over the state-of-the-art.

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.

Systematic Literature Review on Smart Mobility: A Framework for Future “Quantitative” Developments

2021 ◽  
pp. 088541222199424
Author(s):  
Mauro Francini ◽  
Lucia Chieffallo ◽  
Annunziata Palermo ◽  
Maria Francesca Viapiana
Keyword(s):  
Cluster Analysis ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Performance Indicators ◽  
State Of The Art ◽  
Measurement Model ◽  
Broader Impacts ◽  
Research Goal ◽  
Smart Mobility ◽  
Definition Of

This work aims to reorganize theoretical and empirical research on smart mobility through the systematic literature review approach. The research goal is to reach an extended and shared definition of smart mobility using the cluster analysis. The article provides a summary of the state of the art that can have broader impacts in determining new angles for approaching research. In particular, the results will be a reference for future quantitative developments for the authors who are working on the construction of a territorial measurement model of the smartness degree, helping them in identifying performance indicators consistent with the definition proposed.

scholarly journals Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 606
Author(s):  
Alaa Jamal ◽  
Raphael Linker
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Data Assimilation ◽  
Particle Filter ◽  
Sequential Estimation ◽  
Measurement Noise ◽  
Genetic Operators ◽  
True Value

Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes. Various modifications of the original particle filter have been suggested in the literature, including integrating particle filter with Markov Chain Monte Carlo (PF-MCMC) and, later, using genetic algorithm evolutionary operators as part of the state updating process. In this work, a modified genetic-based PF-MCMC approach for estimating the states and parameters simultaneously and without assuming Gaussian distribution for priors is presented. The method was tested on two simulation examples on the basis of the crop model AquaCrop-OS. In the first example, the method was compared to a PF-MCMC method in which states and parameters are updated sequentially and genetic operators are used only for state adjustments. The influence of ensemble size, measurement noise, and mutation and crossover parameters were also investigated. Accurate and stable estimations of the model states were obtained in all cases. Parameter estimation was more challenging than state estimation and not all parameters converged to their true value, especially when the parameter value had little influence on the measured variables. Overall, the proposed method showed more accurate and consistent parameter estimation than the PF-MCMC with sequential estimation, which showed highly conservative behavior. The superiority of the proposed method was more pronounced when the ensemble included a large number of particles and the measurement noise was low.

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>

scholarly journals Digital entrepreneurship in Morocco: measurement model validation

2021 ◽  
Vol 119 ◽  
pp. 03004
Author(s):  
Zakia Saoura ◽  
Ahmed Abriane ◽  
Aniss Moumen
Keyword(s):  
Quantitative Research ◽  
State Of The Art ◽  
Measurement Model ◽  
Arab Countries ◽  
Three Dimensions ◽  
Measurement Scale ◽  
Global Entrepreneurship Monitor ◽  
Entrepreneurial Motivations ◽  
Digital Entrepreneurship ◽  
Do So

According to the 2017 Global Entrepreneurship Monitor report, there are 6.5 million adults aged 18-64 planning to start an entrepreneurial career by 2020. However, the gap between attempt and effective creations remains one of the largest within Arab countries (40% versus 9%). Given these statistics, we ask the question about the profile of the Moroccan entrepreneur. In this paper, we opted for a quantitative research methodology on an exploratory sample. We distributed a questionnaire to a sample of eighty Moroccan entrepreneurs representing different regions of Morocco. The objective of our study is to validate a measurement scale of three dimensions: 1/ entrepreneurial motivations, 2/ skills, and 3/ behaviour in the Moroccan context. To do so, we present, in the first part, a literature review on digital entrepreneurship. Then, we establish a state of the art of entrepreneurship in Morocco. Then, we show our methodology. Finally, we reveal and discuss the results of our study.

Improved Particle Filter

2014 ◽  
Vol 687-691 ◽  
pp. 4072-4075
Author(s):  
Tian Wang
Keyword(s):  
Particle Filter ◽  
System Simulation ◽  
Radar System ◽  
Measurement Noise ◽  
Approximate Algorithm ◽  
Bistatic Radar ◽  
Simulation Results

For the particle filter, the paper proposes an approximate algorithm for the case of unknown measurement noise and make a comparison between EKF algorithm and the approximate particle filter for estimating trajectory in a bistatic radar system. Simulation results show that the advantage of the particle filter and theavailability of the approximate particle filter.

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 A Novel EM Implementation for Initial Alignment of SINS Based on Particle Filter and Particle Swarm Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yanbing Guo ◽  
Lingjuan Miao ◽  
Yusen Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Filter ◽  
Particle Swarm ◽  
The State ◽  
Nonlinear Filters ◽  
Measurement Noise ◽  
Strong Nonlinearity ◽  
Initial Alignment ◽  
Swarm Optimization ◽  
Noise Parameters

For nonlinear systems in which the measurement noise parameters vary over time, adaptive nonlinear filters can be applied to precisely estimate the states of systems. The expectation maximization (EM) algorithm, which alternately takes an expectation- (E-) step and a maximization- (M-) step, has been proposed to construct a theoretical framework for the adaptive nonlinear filters. Previous adaptive nonlinear filters based on the EM employ analytical algorithms to develop the two steps, but they cannot achieve high filtering accuracy because the strong nonlinearity of systems may invalidate the Gaussian assumption of the state distribution. In this paper, we propose an EM-based adaptive nonlinear filter APF to solve this problem. In the E-step, an improved particle filter PF_new is proposed based on the Gaussian sum approximation (GSA) and the Monte Carlo Markov chain (MCMC) to achieve the state estimation. In the M-step, the particle swarm optimization (PSO) is applied to estimate the measurement noise parameters. The performances of the proposed algorithm are illustrated in the simulations with Lorenz 63 model and in a semiphysical experiment of the initial alignment of the strapdown inertial navigation system (SINS) in large misalignment angles.

scholarly journals Dual-Channel Particle Filter Based Track-Before-Detect for Monopulse Radar

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Cai ◽  
Hongqi Fan ◽  
Qiang Fu
Keyword(s):  
Particle Filter ◽  
Doppler Radar ◽  
Pulse Repetition Frequency ◽  
Measurement Model ◽  
Performance Comparison ◽  
Detection Performance ◽  
Actual Measurement ◽  
Low Snr ◽  
Dual Channel ◽  
Track Before Detect

A particle filter based track-before-detect (PF-TBD) algorithm is proposed for the monopulse high pulse repetition frequency (PRF) pulse Doppler radar. The actual measurement model is adopted, in which the range is highly ambiguous and the sum and difference channels exist in parallel. A quantization method is used to approximate the point spread function to reduce the computation load. The detection decisions of the PF-TBD are fed to a binary integrator to further improve the detection performance. Simulation results show that the proposed algorithm can detect and track the low SNR target efficiently. The detection performance is improved significantly for both the single frame and the multiframe detection compared with the classical detector. A performance comparison with the PF-TBD using sum channel only is also supplied.

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