scholarly journals A Particle Filter Approach to DGNSS Integrity Monitoring —Consideration of Non-Gaussian Error Distribution—

2008 ◽  
Vol 50 (170) ◽  
pp. 231-239
Author(s):  
Youngsun YUN ◽  
Doyoon KIM ◽  
Changdon KEE
Keyword(s):  
Particle Filter ◽  
Error Distribution ◽  
Integrity Monitoring ◽  
Filter Approach ◽  
Non Gaussian

Carrier Phase-based RAIM Algorithm Using a Gaussian Sum Filter

2010 ◽  
Vol 64 (1) ◽  
pp. 75-90 ◽  
Author(s):  
Ho Yun ◽  
Youngsun Yun ◽  
Changdon Kee
Keyword(s):  
Gaussian Distribution ◽  
Carrier Phase ◽  
Phase Error ◽  
Critical Role ◽  
Gaussian Mixture ◽  
Error Distribution ◽  
Integrity Monitoring ◽  
Gaussian Sum ◽  
Carrier Phase Error ◽  
Non Gaussian

Carrier phase measurements are used to provide high-accuracy estimates of position. For safety-of-life navigation applications such as precision approach and landing, integrity plays a critical role. Carrier phase-based Receiver Autonomous Integrity Monitoring (CRAIM) has been investigated for many years (Pervan et al, 1998; Feng et al, 2007). Assuming that the carrier phase error has a Gaussian distribution, conventional CRAIM algorithms were directly derived from the Pseudorange-based RAIM (PRAIM). However, the actual carrier phase error does not exactly follow the Gaussian distribution, hence the performance of the conventional CRAIM algorithm is not optimal.To approach this problem, this paper proposes a new CRAIM algorithm that uses Gaussian sum filters. A Gaussian sum filter can deal with any non-Gaussian error distribution and accurately present the posterior distributions of states. In this paper, a new method of making a Gaussian mixture model, which follows the true error distribution, is introduced. Additionally an integrity monitoring algorithm, using a Gaussian sum filter, is described in detail. The simulation results show that the proposed algorithm can have about 18% smaller Minimum Detectable Bias (MDB) and generates about 20% lower protection levels than those of the conventional CRAIM algorithm. In other words, by considering a non-Gaussian carrier phase error distribution, the new algorithm can improve the accuracy and the availability.

Dynamic Target Tracking Based on Particle Filter in Actual Environment

2013 ◽  
Vol 683 ◽  
pp. 824-827
Author(s):  
Tian Ding Chen ◽  
Chao Lu ◽  
Jian Hu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Monitoring System ◽  
Filter Method ◽  
Complex Scene ◽  
Actual Environment ◽  
Dynamic Target ◽  
Gaussian System ◽  
Non Gaussian

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

scholarly journals Non-Gaussian Probability Densities of Convection Initiation and Development Investigated Using a Particle Filter with a Storm-Scale Numerical Weather Prediction Model

2019 ◽  
Vol 148 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Takuya Kawabata ◽  
Genta Ueno
Keyword(s):  
Prediction Model ◽  
Particle Filter ◽  
Goodness Of Fit ◽  
Weather Prediction ◽  
Potential Temperature ◽  
Error Estimator ◽  
Observation Error ◽  
Adaptive Observation ◽  
Non Gaussian ◽  
Convection Initiation

Abstract Non-Gaussian probability density functions (PDFs) in convection initiation (CI) and development were investigated using a particle filter with a storm-scale numerical prediction model and an adaptive observation error estimator (NHM-RPF). An observing system simulation experiment (OSSE) was conducted with a 90-min assimilation period and 1000 particles at a 2-km grid spacing. Pseudosurface observations of potential temperature (PT), winds, water vapor (QV), and pseudoradar observations of rainwater (QR) in the lower troposphere were created in a nature run that simulated a well-developed cumulonimbus. The results of the OSSE (PF) show a significant improvement in comparison to ensemble simulations without any observations. The Gaussianity of the PDFs for PF in the CI area was evaluated using the Bayesian information criterion to compare goodness-of-fit of Gaussian, two-Gaussian mixture, and histogram models. The PDFs are strongly non-Gaussian when NHM-RPF produces diverse particles over the CI period. The non-Gaussian PDF of the updraft is followed by the upper-bounded PDF of the relative humidity, which produces non-Gaussian PDFs of QV and PT. The PDFs of the cloud water and QR are strongly non-Gaussian throughout the experimental period. We conclude that the non-Gaussianity of the CI originated from the non-Gaussianity of the updraft. In addition, we show that the adaptive observation error estimator significantly contributes to the stability of PF and the robustness to many observations.

Navigation strategies for cooperative localization based on a particle-filter approach

2007 ◽  
Vol 14 (3) ◽  
pp. 263-279 ◽  
Author(s):  
Fernando Gomez Bravo ◽  
Alberto Vale ◽  
Maria Isabel Ribeiro
Keyword(s):  
Particle Filter ◽  
Cooperative Localization ◽  
Filter Approach ◽  
Navigation Strategies
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