Improved particle filter algorithm for INS/GPS integrated navigation system

Performance evaluation of Cubature Kalman filter in a GPS/IMU tightly-coupled navigation system

Particle filter is commonly used in various indoor positioning schemes, but sample impoverishment and weight degradation generally exist in particle filter. To solve this problem, this article uses the firefly algorithm to optimize particle filter. Using the global optimal value in the particle swarm to guide the remaining particles to update their positions, the particles tend to move to the high likelihood area, which can more accurately describe the true state of the target observation. At the same time, to avoid particles falling into local optimum and oscillating repeatedly at the extreme point, the mathematical model of the firefly algorithm is reconstructed in this article, which is named as adaptive optimization firefly algorithm (AOFA). The experimental results show that the positioning scheme can provide the positioning accuracy with an average error of less than 0.5 m. Compared with the traditional particle filter, the positioning accuracy is improved by 120%.

The distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particle filter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particle filter, especially in SLAM problem that involves a large number of dimensions. In this paper, particle filter use in distributed SLAM was improved in two aspects. First, we improved the important function of the local filters in particle filter. The adaptive values were used to replace a set of constants in the computational process of importance function, which improved the robustness of the particle filter. Second, an information fusion method was proposed by mixing the innovation method and the number of effective particles method, which combined the advantages of these two methods. And this paper extends the previously known convergence results for particle filter to prove that improved particle filter converges to the optimal filter in mean square as the number of particles goes to infinity. The experiment results show that the proposed algorithm improved the virtue of the DPF-SLAM system in isolate faults and enabled the system to have a better tolerance and robustness.

In this paper, we investigate the problem of autonomous integrated navigation of strapdown inertial navigation system (SINS)/ celestial navigation system (CNS) for a ballistic missile. To handle the nonlinear missile dynamic model, three filtering methods including extended Kalman filter (EKF), extended Kalman particle filter (EKPF) and unscented particle filter (UPF) are utilized to fusion the integrated navigation system and improve the navigation accuracy. The performance of the navigation system based on the three methods is analyzed and compared. Simulation results indicate that the UPF can provide the highest navigation accuracy with the price of highest computation burden.

In this work, we propose the cubature Kalman filter (CKF) based distributed object tracking algorithm in a visual sensor network (VSN). A VSN consists of several distributed smart cameras having the ability to process and analyze the retrieved data locally. The first objective is to optimize the tracking process within the VSN through the CKF. Under the conditions of non-linear motion and observation model, the CKF based method features a considerably better tracking accuracy than the extended Kalman filter (EKF) based method in terms of the mean square error (MSE). Although, the particle filter (PF) based method shows better performance than the CKF, it is computationally very complex. The second objective is to optimize the object tracking by aggregating the tracking results from multiple cameras. Assuming the VSN is a multi-camera network with overlapping field of views (FOVs), cameras having the same object in their FOV exchange their local estimates of the object's position and velocity. During the estimation process, each of the participating cameras aggregates the received states via a consensus algorithm. Thus, the object's real state is more accurately predicted by the resulting joint state than it would be by processing only a single camera's observation.

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

An adaptive cubature Kalman filter algorithm for inertial and land-based navigation system

Aiming at the integrated navigation system with model errors, the model errors is assumed as a process noise for Gaussian white noise to process in Kalman filtering, thus causing larger state estimation error of nonlinear filtering system and even divergent. This paper presents a nonlinear model predictive particle filtering method considers the model error of real-time estimation, and then corrects the nonlinear and non-Gaussian system model with model error. Compared and simulated the new method with the predictive filtering and the particle filtering in Strap-down Inertial Navigation System/Synthetic Aperture Radar integrated navigation system. Experiment and comparison results demonstrate that the proposed method can improve the filter performance and integrated navigation calculation accuracy significantly.

SummaryThis paper addresses the problem of nonlinear time‐varying state and parameter estimation of induction machines (IMs) on the basis of a third‐order electrical model. The objectives of this paper are threefold. The first objective is to propose the use of an improved particle filter (IPF) with better proposal distribution for nonlinear and non‐Gaussian state and parameter estimation. The second objective is to extend the state and parameter estimation techniques (i.e., extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and IPF) to better handle nonlinear and non‐Gaussian processes withouta prioristate information, by utilizing a time‐varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The third objective is to compare the performances of EKF, UKF, PF, and IPF in estimating the states of the power process model representing the IM (i.e, the rotor speed, the rotor flux, the stator flux, the rotor resistance, and the magnetizing inductance) and their abilities to estimate some of the key system parameters, which are needed to define the IM process model. The results show that the IPF provides a significant improvement over the PF because, unlike the PF, which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. This conclusion is also supported by the experimental results. Copyright © 2014 John Wiley & Sons, Ltd.

Induction machine is highly nonlinear model with states that change with operating point and temperature. In these cases, estimating these variables from other easily obtained measurements can be extremely useful. This paper deals with the problem of state estimation of induction machine on the basis of a third-order electrical model using Bayesian methods. The performances of Bayesian estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), the unscented Kalman filter (UKF), the particle filter (PF), and the developed improved particle filter (IPF). The estimation results, which are validated using simulations, show that IPF provides improved estimation performance over PF, even with abrupt changes in estimated states, and both of them can provide improved accuracy over UKF and EKF. These advantages of the IPF are due to the fact that it uses a better proposal distribution that takes the latest observation into account.

There are the scale factor error of LDV (laser Doppler velocimeter) and the misalignment between the SINS (Strapdown inertial navigation system) and the vehicle in a SINS/LDV integrated navigation system. In this paper, the effects of these errors on the attitude, velocity and position of dead reckoning are derived, and a new online calibration method aiming to calibrate the scale factor of LDV and the misalignment between the SINS and the vehicle for the integrated system is put forward. This method, which is utilize the velocity and position of the Global Position System (GPS) as references, use the velocity error and position error of dead reckoning to estimate these errors. Through simulation and experiment, the validity and feasibility of the method are verified. The results show that the scale factor and the misalignment can be calibrated with satisfying accuracy, and the related research can provide technical support for high precision navigation of SINS/LDV integrated navigation systems.

SINS/ANS integration for augmented performance navigation solution using unscented Kalman filtering

The human respiratory movement seriously affects the precision of the robotized radiotherapy treatment for lung cancer. In order to reduce the influence of respiratory movement, the most effective method is real-time tracking of respiratory tumor motion, which adopts tracking algorithm to predict the location of the tumor, and adjust the treatment beam to catch the motion of the target. This paper utilizes the particle filter as tracking algorithm to predict the tumor motion trajectory. Firstly, the principle and process of particle filter algorithm are introduced; Secondly, the probability correlation model based on UT transform are proposed as the observation model of the particle filter; Finally, the conventional particle filter and the UT transform modeling are used to predict the location of the tumor with respiratory motion. The effectiveness and accuracy of particle filter algorithm based on UT transform is verified by error analysis. The experiment results show that the particle filter algorithm based on UT transform has stronger robustness and higher prediction precision compared with the linear estimation and the traditional particle filter, so it can meet the requirements of real-time tumor tracking for radiotherapy robots.

In order to improve the positioning accuracy of two leader autonomous underwater vehicles (AUVs) in underwater cooperative navigation formation, a decentralized cooperative localization algorithm based on adaptive cubature Kalman filter (ACKF-DCL) for strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated navigation system is proposed. Firstly, the kinematic equations of the SINS are used to establish the direct filtering approach model for integrated navigation. Secondly, the Sage-Husa adaptive filtering algorithm and the sequential Kalman filtering (SKF) method are introduced into the nonlinear cubature Kalman filter (CKF) to adjust adaptively the measurement noise and maintain the positivity of the covariance matrix. Finally, the cross-correlation covariance between two AUVs is derived according to the characteristics of a decentralized data structure, so as to track the inter-AUV dependencies. And the acoustic ranging measurement is introduced to achieve relative measurement update. The lake trial demonstrates the proposed algorithm’s effectiveness and superiority, and the results show that the algorithm can effectively track the measurement noise covariance matrix and improve the positioning accuracy of both AUVs by position correction.

ABSTRACTSatellite observations of surface chlorophyll have been assimilated into a coupled biophysical model of the Indian Ocean with particle filter (PF). Following the approach advocated in an earlier study, we have operated in a low-dimensional error subspace. Two different versions of PF have been implemented. In the traditional PF, the ensemble is augmented with two biological parameters, which are resampled along with state vector, followed by perturbation of the parameters with zero-mean Gaussian noise. In the other version, called guided PF, there is additional guidance of the ensemble towards future observations. While the traditional PF has not been able to outperform the standard run, the guided PF has been able to do so quite emphatically. This superior performance has been reflected in the time series of averaged chlorophyll in two sub-regions as well as in the spatial distribution of averaged chlorophyll in the same two sub-regions.