Proposed Particle Filter Algorithm Research Articles

Particle Filter Algorithm for DOA Tracking Using Co-Prime Array

In this letter, the problem of direction of arrival (DOA) tracking using co-prime array is studied. A novel particle filter (PF)-based DOA tracking algorithm is proposed to recursively estimate the DOAs based on the measurement model and the state transition model. A modified likelihood function model based on the propagator method (PM) pseudo-spectrum is devised to update the involved particles under lower SNR or less samples condition. Compared to the existing techniques, such as the spatial smoothing multiple signal classification method (SS-MUSIC), compressive sensing (CS) and conventional likelihood PF (CL-PF) methods, the proposed algorithm can exhibit improved performance in term of estimating accuracy and computational cost. Simulation studies are carried out to show the superiority of the proposed PF algorithm.

The estimation of time-invariant parameters of noisy nonlinear oscillatory systems

The inverse problem of estimating time-invariant (static) parameters of a nonlinear system exhibiting noisy oscillation is considered in this paper. Firstly, a Markov Chain Monte Carlo (MCMC) simulation is used for the time-invariant parameter estimation which exploits a non-Gaussian filter, namely the Ensemble Kalman Filter (EnKF) for state estimation required to compute the likelihood function. Secondly, a recently proposed Particle Filter (PF) (that uses the EnKF for its proposal density for the state estimation) has been adapted for combined state and parameter estimation. Numerical illustrations highlight the strengths and limitations of the MCMC, EnKF and PF algorithms for time-invariant parameter estimation. For low measurement noise and dense measurement data, the performances of the MCMC, EnKF and PF algorithms are comparable. For high measurement noise and sparse observational data, the EnKF fails to provide accurate parameter estimates. Hence the adapted PF algorithm becomes necessary in order to obtain parameter estimates comparable in accuracy to the MCMC simulation with EnKF. It highlights the fact that the augmented state space model for the combined state and parameter estimation contains stronger nonlinearity than the original state space model. Hence the EnKF effectively handles the state estimation of the original state space model, but it fails for the combined state and parameter estimation using the augmented system. The effectiveness of the EnKF for the state estimation is therefore leveraged in the MCMC simulation for the time-invariant parameter estimation. In order to obtain accurate parameter estimates using the augmented system, the adapted PF becomes necessary to match the parameter estimates obtained using the MCMC simulation complemented by EnKF for likelihood function computation.

Efficient particle filter algorithm for ultrasonic sensor-based 2D range-only simultaneous localisation and mapping application

Owing to low cost and relatively accurate range measurement, ultrasonic sensors are widely used in various simultaneous localisation and mapping (SLAM) applications. In spite of the abundance of ultrasonic sensor based SLAM applications, a simple and efficient algorithm for an ultrasonic sensor based positioning system with good accuracy and low computational complexity has not yet emerged. The major difficulty is the trade-off between localisation accuracy and computational complexity in most SLAM algorithms, such as extended Kalman filter (EKF) and particle filter. Typically, they improve localisation accuracy by increasing the density of the landmarks, as a result leading to high computational complexity of algorithms and limiting the utilisation of algorithms into online SLAM systems. This study addresses an improved particle filter algorithm to solve ultrasonic sensor based 2D range-only SLAM problem with relatively good accuracy and low computational complexity. This algorithm uses a simple four fixed features based system model to limit the density of the landmarks. A technique called map adjustment is proposed to increase the accuracy and efficiency of the algorithm. Using map adjustment, the proposed particle filter algorithm can improve localisation accuracy and lower computational complexity. The experiment results demonstrate that this algorithm has a better performance than conventional particle filter localisation algorithm.

Alignment of 3-D Optical Coherence Tomography Scans to Correct Eye Movement Using a Particle Filtering

Eye movement artifacts occurring during 3-D optical coherence tomography (OCT) scanning is a well-recognized problem that may adversely affect image analysis and interpretation. A particle filtering algorithm is presented in this paper to correct motion in a 3-D dataset by considering eye movement as a target tracking problem in a dynamic system. The proposed particle filtering algorithm is an independent 3-D alignment approach, which does not rely on any reference image. 3-D OCT data is considered as a dynamic system, while the location of each A-scan is represented by the state space. A particle set is used to approximate the probability density of the state in the dynamic system. The state of the system is updated frame by frame to detect A-scan movement. The proposed method was applied on both simulated data for objective evaluation and experimental data for subjective evaluation. The sensitivity and specificity of the x-movement detection were 98.85% and 99.43%, respectively, in the simulated data. For the experimental data (74 3-D OCT images), all the images were improved after z-alignment, while 81.1% images were improved after x-alignment. The proposed algorithm is an efficient way to align 3-D OCT volume data and correct the eye movement without using references.

3D OCT eye movement correction based on particle filtering.

Three-dimensional optical coherence tomography (OCT) is a new ophthalmic imaging technique offering more detailed quantitative analysis of the retinal structure. Eye movement during 3D OCT scanning, however, creates significant spatial distortions that may adversely affect image interpretation and analysis. Current software solutions must use additional reference images or B-scans to correct eye movement in a certain direction. The proposed particle filtering algorithm is an independent 3D alignment approach, which does not rely on any reference image. 3D OCT data is considered as a dynamic system, while location of A-scan is represented by the state space. A particle set is generated to approximate the probability density of the state. The state of the system is updated frame by frame to detect A-scan movement. Seventy-four 3D OCT images with eye movement were tested and subjectively evaluated by comparing them with the original images. All the images were improved after z-alignment, while 81.1% images were improved after x-alignment. The proposed algorithm is an efficient way to align 3D OCT volume data and correct the eye movement without using references.

Particle Filter-Based Synchronization of Chaotic Colpitts Circuits Combating AWGN Channel Distortion

In this paper, synchronization of chaotic Colpitts circuits using a particle filter (PF) to combat the additive white Gaussian noise (AWGN) channel effect is studied by numeric simulations. A novel PF algorithm suitable for chaos synchronization is proposed. With this algorithm, chaos synchronization of Colpitts circuits can be achieved and maintained in AWGN channels. Parameters in the proposed PF algorithm are studied to understand their effects on synchronization performance. The synchronization performance using the proposed PF algorithm is compared with those using other digital filters, such as the extended Kalman filter and the generic PF. It is found that the proposed PF algorithm performs better than the other digital filters. Simulation results also show that the particle number is not very critical to the synchronization performance when this PF algorithm is used.

Particle Filter with Integrated Voice Activity Detection for Acoustic Source Tracking

In noisy and reverberant environments, the problem of acoustic source localisation and tracking (ASLT) using an array of microphones presents a number of challenging difficulties. One of the main issues when considering real-world situations involving human speakers is the temporally discontinuous nature of speech signals: the presence of silence gaps in the speech can easily misguide the tracking algorithm, even in practical environments with low to moderate noise and reverberation levels. A natural extension of currently available sound source tracking algorithms is the integration of a voice activity detection (VAD) scheme. We describe a new ASLT algorithm based on a particle filtering (PF) approach, where VAD measurements are fused within the statistical framework of the PF implementation. Tracking accuracy results for the proposed method is presented on the basis of synthetic audio samples generated with the image method, whereas performance results obtained with a real-time implementation of the algorithm, and using real audio data recorded in a reverberant room, are published elsewhere. Compared to a previously proposed PF algorithm, the experimental results demonstrate the improved robustness of the method described in this work when tracking sources emitting real-world speech signals, which typically involve significant silence gaps between utterances.