Sample Impoverishment Research Articles

Particle filter with spline resampling and global transition model

The authors introduce the concept of a spline resampling in the particle filter to deal with high accuracy and sample impoverishment. The resampling is usually based on a linear transformation on the weights of the particles, so it affects the filtering accuracy. The spline resampling consists of two parts: the spline transformation of weights and the spread transformation of states. The former is based on a spline transformation on the weights of the particles to obtain highly accurate particle filtering, and the latter is based on a point spread transformation on states of particles to prevent sample impoverishment due to a decline in the diversity of hypothesis after resampling. Two transformations are sequentially implemented to incorporate with each other. Then, the authors propose a global transition model in the particle filter, which takes account of the background variation caused by the camera motion, to decrease error from real object position. The authors test the performance of their spline resampling and the global transition model in the particle filter in an object‐tracking scenario. Experimental results demonstrate that the particle filter with the spline resampling and the global transition model has promising discriminative capability in comparison with others.

Particle filter tracking for banana and contact lens problems

In this paper we present an approach for tracking with high-bandwidth radar in long-range scenarios. We consider both two- and three-dimensional measurements in polar and r-u-v, respectively. We show that in these scenarios the extended Kalman filter is not desirable, because it suffers from major consistency problems, and most types of particle filters suffer from a loss of diversity among particles after resampling. This leads to sample impoverishment and divergence of the filter. In the scenarios studied, this loss of diversity can be attributed to the very low process noise. However, a regularized particle filter is shown to avoid this diversity problem while producing consistent results. The regularization is accomplished using a generalized version of the Epanechnikov kernel.

An Improved Resampling Algorithm for Particle Filtering in Small Target Tracking

ABSTRACT Zhou, R. and Teng, J., 2015. An improved resampling algorithm for particle filtering in small target tracking. The sample impoverishment after resampling is the main reason of the decrease of the estimation accuracy in particle filtering. To solve the problem, a new resampling algorithm is proposed in this paper. First, the existing resampling algorithm is analyzed and its defects are demonstrated; then an improved algorithm is introduced to overcome the defects by constructing the new particles based on Gaussian distribution. Compared to the existing resampling algorithm, the proposed algorithm can maintain the diversity of particles and avoid the sample impoverishment in particle filtering, thus increase the estimation accuracy. Simulation results show that the accuracy of tracking small target in low signal-to-noise ratio optimal image sequence is increased in terms of the quantitative criteria-RMSE.

Unscented Particle Filtering Algorithm for Optical-fiber Sensing Intrusion Localization Based on Particle Swarm Optimization

To improve the convergence and precision of intrusion localization in optical-fiber sensing perimeter protection applications, we present an algorithm based on an unscented particle filter (UPF). The algorithm employs particle swarm optimization (PSO) to mitigate the sample degeneracy and impoverishment problem of the particle filter. By comparing the present fitness value of particles with the optimum fitness value of the objective function, PSO moves particles with insignificant UPF weights towards the higher likelihood region and determines the optimal positions for particles with larger weights. The particles with larger weights results in a new sample set with a more balanced distribution between the priors and the likelihood. Simulations demonstrate that the algorithm speeds up convergence and improves the precision of intrusion localization.

Particle Filtering Algorithm Based on Dynamic Multi-Feature Fusion

The target tracking technology in image sequence is of great meanings in the military and civilian areas, by using Monte Carlo method to complete the Bayesian recursive, particle filter is widely used in the systems of non-linear and non - Gaussian and good results are gained. However, particle filter there are also disadvantages in terms of sample impoverishment, the choosing of proper proposal distribution, real time and so on. In this paper, the particle filter is utilized to in the feature fusion of the moving target, and the experimental results show that the proposed algorithm has certain effect both on the remission of the sample impoverishment and the enhancement of the tracking robustness.

State Space Modeling & Bayesian Inference with Computational Intelligence

Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of nonlinear non-Gaussian dynamical systems. It enables us to reason under uncertainty and addresses shortcomings underlying deterministic systems and control theories which do not provide sufficient means of performing analysis and design. In addition, parametric techniques such as the Kalman filter and its extensions, though they are computationally efficient, do not reliably compute states and cannot be used to learn stochastic problems. We review recursive Bayesian estimation using sequential Monte Carlo methods highlighting open problems. Primary of these is the weight degeneracy and sample impoverishment problem. We proceed to detail synergistic computational intelligence sequential Monte Carlo methods which address this. We find that imbuing sequential Monte Carlos with computational intelligence has many advantages when applied to many application and problem domains.

A Ground-based Vision System for UAV Pose Estimation

We present a vision system based on a single frame of standard RGB digital camera to estimate the pose of an unmanned aerial vehicle (UAV). The envisaged application is of ground-based automatic landing, where the vision system is located on the ship’s deck and is used to estimate the UAV pose (3D position and orientation) during the landing process. Using a vision system located on the ship makes it possible to use an UAV with less processing power, decreasing its size and weight. The proposed method uses a 3D model based pose estimation approach that requires the 3D CAD model of the UAV. Pose is estimated in a particle filtering framework. The implemented particle filter is inspired in the evolution strategies present in the genetic algorithms avoiding sample impoverishment. Results show position and angular errors are compatible with automatic landing system requirements, even without temporal filtering. The algorithm is suitable for real time implementation in standard workstations with graphical processing units.

The Particle Filter Sample Impoverishment Problem in the Orbit Determination Application

The paper aims at discussing techniques for administering one implementation issue that often arises in the application of particle filters: sample impoverishment. Dealing with such problem can significantly improve the performance of particle filters and can make the difference between success and failure. Sample impoverishment occurs because of the reduction in the number of truly distinct sample values. A simple solution can be to increase the number of particles, which can quickly lead to unreasonable computational demands, which only delays the inevitable sample impoverishment. There are more intelligent ways of dealing with this problem, such as roughening and prior editing, procedures to be discussed herein. The nonlinear particle filter is based on the bootstrap filter for implementing recursive Bayesian filters. The application consists of determining the orbit of an artificial satellite using real data from the GPS receivers. The standard differential equations describing the orbital motion and the GPS measurements equations are adapted for the nonlinear particle filter, so that the bootstrap algorithm is also used for estimating the orbital state. The evaluation will be done through convergence speed and computational implementation complexity, comparing the bootstrap algorithm results obtained for each technique that deals with sample impoverishment.

An Improved Particle Filter for Phase Measurement of X-ray Pulsar Signal

In order to solve the defect of traditional method based on accumulate pulse proflle process X-ray pulse signal from Pulsar, this paper changes the measurement of the X-ray pulsar signal delay to the matching between standard pulse signal proflle and the actual measured value. At flrst, Obtain pulsar standard signal proflle function by artiflcial neural network, and then the particle fllter algorithm is used to online estimate the pulsar signal delay time of observation. Secondly, To solve the sample impoverishment phenomenon of standard particle fllter, an improved particle fllter was designed and proved, which does not exist the degradation phenomena, and has high adaptive ability. Finally, because of the special features of the system, the function which indicates the output value precision is derived, and it can provide a reference for spacecraft navigation strategy. The comparisons of the new fllter and standard fllter illustrate the new fllter has better adaptability and higher accuracy.

Particle Filtering-Based System Degradation Prediction Applied to Jet Engines


 
 
 This paper investigates a real-time fault detection and degradation prediction scheme for dynamical systems such as jet engines, based on Regularized Particle Filtering (RPF). Particle Filtering is a prognosis method for the prediction of state degradation and remaining useful life (RUL) due to its demonstrated performance in handling non-linear and non-Gaussian situations. RPF overcomes the problem of sample impoverishment among particles over the resampling process. Based on measured data from hybrid sensing and nonlinear models, which link system parameters and degradation state to the measurement, RPF has been applied to establishing a framework for both state and parameter estimation, to achieve prognosis at the component level. In addition, a modified system evolution model is proposed to track both exponential and transient types of system performance degradation. The developed method is evaluated using simulated data created with C- MAPSS, which contains measured parameters associated with engine degradation under nominal and varied fault types (fan, compressor and turbine) during a series of flights. The developed system-parameter estimation method is found effective in state estimation and degradation prediction in jet engines.
 
 

Visual tracking based on particle filter with spline resampling

We introduce the concept of a spline resampling in the particle filter to deal with the high accuracy and the sample impoverishment. The resampling is usually based on a linear transformation on the weights of the particles, so it affects the accurate filtering. The spline resampling consists of two parts: the spline transformation of weights and the spread transformation of states. The former is based on a spline transformation on the weights of the particles to obtain the high accuracy of particle filtering, and the latter is based on a point spread transformation on states of particles to prevent the sample impoverishment due to decline of the diversity of hypothesis after resampling. Two transformations are sequentially implemented to incorporate with each other. Then, we propose a global transition model in the particle filter, which takes account of the background variation caused by the camera motion model of object itself, to decrease error from real object position. We test the performance of our spline resampling and the global transition model in the particle filter in object tracking scenario. Experimental results demonstrate that particle filter with the spline resampling and the global transition model has the promising discriminative capability in comparison with other ones.

A Square Root Unscented FastSLAM With Improved Proposal Distribution and Resampling

An improved square root unscented fast simultaneous localization and mapping (FastSLAM) is proposed in this paper. The proposed method propagates and updates the square root of the state covariance directly in Cholesky decomposition form. Since the choice of the proposal distribution and that of the resampling method are the most critical issues to ensure the performance of the algorithm, its optimization is considered by improving the sampling and resampling steps. For this purpose, particle swarm optimization (PSO) is used to optimize the proposal distribution. PSO causes the particle set to tend to the high probability region of the posterior before the weights are updated; thereby, the impoverishment of particles can be overcome. Moreover, a new resampling algorithm is presented to improve the resampling step. The new resampling algorithm can conquer the defects of the resampling algorithm and solve the degeneracy and sample impoverishment problem simultaneously. Compared to unscented FastSLAM (UFastSLAM), the proposed algorithm can maintain the diversity of particles and consequently avoid inconsistency for longer time periods, and furthermore, it can improve the estimation accuracy compared to UFastSLAM. These advantages are verified by simulations and experimental tests for benchmark environments.

Fault Detection and Isolation for GPS RAIM Based on Genetic Resampling Particle Filter Approach

An integrity monitoring system is an inseparable part of global positioning system (GPS). According to the measurement noise feature of GPS receiver and the degeneracy phenomenon and alleviating the sample impoverishment problem in particle filter (PF). An approach to fault detection and isolation (FDI) for GPS receiver autonomous integrity monitoring (RAIM) based on genetic resampling particle filter is proposed. The genetic algorithm (GA) is melted into the re-sampling process of the basic particle filter to solve the particles degeneracy and impoverishment problem. A main GA-aided particle filter (GPF) is used to process all the measurements to produce the optimal state estimate, several auxiliary GPFs are used to process subsets of measurements to produce the state estimate as detection references. By setting up the log-likelihood ratio (LLR) test to check the consistency of test statistics.The particles in GPF are assorted by weights, in order to reduce the computation complexity of the algorithm, only the lower weight particles participate in genetic operations.By collecting the GPS data from the GPS reciver, the feasibility and effectiveness of the RAIM approach is verified, and comparing with extended Kalman filter (EKF) and PF algorithm. The results show that the approach in the case of non-Gaussian measurement noise can estimate the state accurately, also can successfully detect fault satellite, therefore, improve the reliability of GPS positioning. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4879

Intelligent video target tracking using an evolutionary particle filter based upon improved cuckoo search

The aim of this paper is to propose an evolutionary particle filter based upon improved cuckoo search algorithm which will overcome the sample impoverishment problem of generic particle filter. In our proposed method, improved cuckoo search (ICS) algorithm is embedded into particle filter (PF) framework. Improved cuckoo search algorithm uses levy flight for generating new particles in the solution and introduced randomness in samples by abandoning a fraction of these particles. The second important contribution in this article is introduction of new way for tackling scaling and rotational error in object tracking. Performance of proposed improved cuckoo particle filter is investigated and evaluated on synthetic and standard video sequences and compared with the generic particle filter and particle swarm optimization based particle filter. We show that object tracking using improved cuckoo particle filter provides more reliable and efficient tracking results than generic particle filter and PSO-particle filter. The proposed technique works for real time video objects tracking.

Object tracking with sparse representation and annealed particle filter

Recently, the L1 tracker is proposed for robust visual tracking. However, L1 tracker is still in traditional particle filter framework. As we know, particle filters suffer from some problems such as sample impoverishment. In this paper, we propose a new visual tracking algorithm, sparse representation based annealed particle filter, to further improve the performance of L1 tracker. As in L1 tracker, we find the tracking target at a new frame by sparsely representing each target candidate with both target and trivial templates. The sparsity is achieved by solving an $$\ell _{1}$$ -regularized least squares problem. The candidate with the largest likelihood is taken as the tracking target. But different from L1 tracker, instead of tracking objects in the common particle filter framework, we solve the sparse representation problem in an annealed particle filter (APF) framework. In the APF framework, the sampling covariance and annealing factors are incorporated into the tracking process. The annealing strategy can achieve “smart sampling” to avoid generating invalid particles corresponding to infeasible targets. Both qualitative and quantitative evaluations on challenging video sequences are implemented to demonstrate the favorable performance in comparison with several other state-of-the-art tracking schemes.

PARTICLE FILTER BASED VEHICLE TRACKING APPROACH WITH IMPROVED RESAMPLING STAGE

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GPS Receiver Autonomous Integrity Monitoring Algorithm Based on Improved Particle Filter

Reliability of a navigation system is one of great importance for navigation purposes. Therefore, an integrity monitoring system is an inseparable part of aviation navigation system. Failures or faults due to malfunctions in the systems should be detected and repaired to keep the integrity of the system intact. According to the characteristic of GPS (Global Positioning System) receiver noise distribution and particle degeneracy and sample impoverishment problem in particle filter, an improved particle filter algorithm based on genetic algorithm for detecting satellite failures is proposed. The combination of the re-sampling method based on genetic algorithm and basic particle filter is used for GPS receiver autonomous integrity monitoring (RAIM). Dealing with the low weight particles on the basis of the genetic operation, genetic algorithm is used to classify the particles. It brings the selection, crossover and mutation operation in genetic algorithm into the basis particle filter .The method for detecting satellite failures which affect only subsets of system measurement. In addition to a main particle filter, which processes all the measurements to give the optimal state estimate, a bank of auxiliary particle filters is also used, which process subsets of the measurements to provide the state estimates which serve as failure detection references. The consistency of test statistics for detection and isolation of satellite fault is established. The failure detection is undertaken by checking the system state logarithmic likelihood ratio (LLR). The RAIM algorithm combined the genetic particle filter and the likelihood method is illustrated in detail. Experimental results based on GPS real raw data demonstrate that the algorithm under the condition of non- Gaussian measurement noise can improve the accuracy of state estimation, effectively detect and isolate fault satellite, improve the performance of the fault detection. Experimental results demonstrate that the proposed approach is available and effective for GPS RAIM.

Study on stochastic perturbation strong tracking particle filter

To solve the degeneracy phenomenon and to improve the ability for tracking the breaking states are two difficult problems in the application of particle filter. Sequential important re-sampling can reduce orilliminate degeneracy, but the sample impoverishment is a secondary result. Extended particle filter can also reduce the degeneracy, but it cannot track the breaking states. The ability to track the breaking states can be improved by a strong tracking particle filter, but the degeneracy phenomenon will not be well solved still. A stochastic perturbation strong tracking particle filter is proposed for solving the above problems, in which a stochastically perturbative re-sampling is introduced into a strong tracking particle filter. Thus a stochastic perturbation is added to the particle with maximal weight to form some new particles, and the degenerative particles are displaced by the new particles to solve the degeneracy phenomenon and so the sample impoverishment improves the diversity of the samples. The ability of the proposed algorithm to track breaking states is also improved, and the feasibility and validity of the proposed algorithm are demonstrated by the simulation results of the standard validation model and the system with constants in different periods of time.

Dynamic resampling for alleviating sample impoverishment of particle filter

In particle filter, the use of resampling technique solves the problem of particle degeneracy to some extent, but introduces a new problem of particle impoverishment. To reconcile this dilemma, a dynamic resampling strategy is proposed, where the resampling operation is only performed on part of the particles in a step-by-step manner and the number of resampled particles is determined dynamically by a termination criterion based on the effective sample size. The new-born particles produced by resampling operations are helpful in alleviating particle degeneracy, whereas particles free from being resampled is conducive to improving the diversity of particles. Thus, a tradeoff between these two problems can be achieved. Also, two techniques, that is, recursive computation of the effective sample size and improved bisection method, for improving the computational efficiency are proposed. Simulation results conducted on two typical examples show the improved performance of the proposed method.

Ant Colony Estimator: An intelligent particle filter based on [formula omitted

Based on Ant Colony Optimization for Continuous Domains (ACOR) and Particle Filter (PF), an intelligent particle filter, namely Ant Colony Estimator (ACE), is proposed in this paper. Modeling and search abilities of ACOR are incorporated into the standard particle filtering framework to improve the estimation performance and overcome the well-known problems of Degeneracy and Sample Impoverishment. ACOR operators implicitly use measurement and previous particle information, to generate probably better particles. Simulation results are given for two examples and ACE is compared to other types of particle filters. The obtained results confirm the efficiency and applicability of ACE.