scholarly journals Multiple-Model Cardinality Balanced Multitarget Multi-Bernoulli Filter for Tracking Maneuvering Targets

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Xianghui Yuan ◽  
Feng Lian ◽  
Chongzhao Han
Keyword(s):  
Monte Carlo ◽  
Kalman Filtering ◽  
Sequential Monte Carlo ◽  
Nonlinear Models ◽  
Gaussian Mixture ◽  
Multiple Models ◽  
Multiple Model ◽  
Maneuvering Targets ◽  
Bernoulli Filter ◽  
Simulation Results

By integrating the cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter with the interacting multiple models (IMM) algorithm, an MM-CBMeMBer filter is proposed in this paper for tracking multiple maneuvering targets in clutter. The sequential Monte Carlo (SMC) method is used to implement the filter for generic multi-target models and the Gaussian mixture (GM) method is used to implement the filter for linear-Gaussian multi-target models. Then, the extended Kalman (EK) and unscented Kalman filtering approximations for the GM-MM-CBMeMBer filter to accommodate mildly nonlinear models are described briefly. Simulation results are presented to show the effectiveness of the proposed filter.

A Novel Sequential Monte Carlo-Probability Hypothesis Density Filter for Particle Impoverishment Problem

2016 ◽  
Vol 13 (10) ◽  
pp. 6872-6877
Author(s):  
Xu Cong-An ◽  
Xu Congqi ◽  
Dong Yunlong ◽  
Xiong Wei ◽  
Chai Yong ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Probability Hypothesis Density ◽  
Practical Applications ◽  
Probability Hypothesis Density Filter ◽  
Highly Nonlinear ◽  
Density Filter ◽  
Simulation Results ◽  
Application Prospects ◽  
Particle Impoverishment

As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, diversity loss of particles introduced by the resampling step, which can be called particle impoverishment problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this paper, a novel SMC-PHD filter based on particle compensation is proposed to solve the problem. Firstly, based on an analysis of the particle impoverishment problem, a new particle compensatory method is developed to improve the particle diversity. Then, all the particles are integrated into the SMC-PHD filter framework. Compared with the SMC-PHD filter, simulation results demonstrate that the proposed particle compensatory SMC-PHD filter is capable of overcoming the particle impoverishment problem, which indicate good application prospects.

scholarly journals Large Sample Properties of the Adaptive Gaussian Mixture Filter

2017 ◽  
Vol 145 (7) ◽  
pp. 2533-2553 ◽  
Author(s):  
Andreas S. Stordal ◽  
Hans A. Karlsen
Keyword(s):  
Monte Carlo ◽  
Particle Filter ◽  
Sample Size ◽  
Sequential Monte Carlo ◽  
Hybrid Methods ◽  
Gaussian Mixture ◽  
Error Norm ◽  
Filter Performance ◽  
Special Cases ◽  
Gaussian Mixture Filters

In high-dimensional dynamic systems, standard Monte Carlo techniques that asymptotically reproduce the posterior distribution are computationally too expensive. Alternative sampling strategies are usually applied and among these the ensemble Kalman filter (EnKF) is perhaps the most popular. However, the EnKF suffers from severe bias if the model under consideration is far from linear. Another class of sequential Monte Carlo methods is kernel-based Gaussian mixture filters, which reduce the bias but maintain the robustness of the EnKF. Although many hybrid methods have been introduced in recent years, not many have been analyzed theoretically. Here it is shown that the recently proposed adaptive Gaussian mixture filter can be formulated in a rigorous Bayesian framework and that the algorithm can be generalized to a broader class of interpolated kernel filters. Two parameters—the bandwidth of the kernel and a weight interpolation factor—determine the filter performance. The new formulation of the filter includes particle filters, EnKF, and kernel-based Gaussian mixture filters as special cases. Techniques from particle filter literature are used to calculate the asymptotic bias of the filter as a function of the parameters and to derive a central limit theorem. The asymptotic theory is then used to determine the parameters as a function of the sample size in a robust way such that the error norm vanishes asymptotically, whereas the normalized error is sample independent and bounded. The parameter choice is tested on the Lorenz 63 model, where it is shown that the error is smaller or equal to the EnKF and the optimal particle filter for a varying sample size.

Improved Current Statistic Model and Adaptive Filtering

2005 ◽  
Author(s):  
Hongtao Hu ◽  
Zhongliang Jing
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Model ◽  
Adaptive Filtering ◽  
Adaptive Algorithm ◽  
Tracking Algorithm ◽  
Unscented Transformation ◽  
Maneuvering Targets ◽  
Statistic Model ◽  
Simulation Results

Current statistical model needs to pre-define the value of maximum accelerations of maneuvering targets. So it may be difficult to meet all maneuvering conditions. In this paper a novel adaptive algorithm for tracking maneuvering targets is proposed. The algorithm is implemented with fuzzy-controlled current statistic model adaptive filtering and unscented transformation. The Monte Carlo simulation results show that this method outperforms the conventional tracking algorithm based on current statistical model.

ANALYSIS AND QUANTIFICATION OF DATA ASSIMILATION BASED ON SEQUENTIAL MONTE CARLO METHODS FOR WILDFIRE SPREAD SIMULATION

2010 ◽  
Vol 01 (04) ◽  
pp. 445-468 ◽  
Author(s):  
FENG GU ◽  
XIAOLIN HU
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Data Assimilation ◽  
Monte Carlo Methods ◽  
Sequential Monte Carlo ◽  
Sensor Data ◽  
Sequential Monte Carlo Methods ◽  
Systematic Analysis ◽  
Wildfire Spread ◽  
Simulation Results

Data assimilation is an important technique to improve simulation results by assimilating real time sensor data into a simulation model. A data assimilation framework based on Sequential Monte Carlo (SMC) methods for wildfire spread simulation has been developed in previous work. This paper provides systematic analysis and measurement to quantify the effectiveness and robustness of the developed data assimilation method. Measurement metrics are used to evaluate the robustness of SMC methods in data assimilation for wildfire spread simulation. Sensitivity analysis is carried out to examine the influences of important parameters to the data assimilation results. This work of analysis and quantification provides information to assess the effectiveness of the data assimilation method and suggests guidelines to further improve the data assimilation method for wildfire spread simulation.

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