A New Particle Filter Guided by Iterated EKF

This paper proposes a novel particle filter, namely, the auxiliary iterated extended Kalman particle filter (AIEKPF). To generate the importance density, based on the auxiliary particle filtering (APF) technique the proposed filter uses the iterated extended Kalman filter (IEKF) to integrate the latest measurements into state transition density. This new filter can match the posterior density well, because of the robustness of the APF and the importance density generated by the IEKF. The performance of the presented particle filter is evaluated by two different estimation problems with the noise of Gaussian distribution and Gamma distribution, respectively. The experimental results illustrate that the AIEKPF is superior to the extended Kalman filter and some existing particle filters, such as the standard particle filter (PF), the extended Kalman particle filter, the unscented Kalman particle filter (UKPF) and the auxiliary extended Kalman particle filter, where the number of particles is relatively small, such as 200 and 1,000. However, with an increase of particles, the superiority of the proposed method may decline compared with the PF and APF as showed in the experiments. Also, the AIEKPF has less running time than the UKPF under the same conditions, and from the viewpoint of the average effective sample sizes, it is clear that the AIEKPF has the slightest degeneracy in all filters presented in the experiments.

Accurate estimation of state of charge (SOC) is greatly crucial for safely and reliably charging and discharging the lithium-ion batteries, especially for those used in electric vehicles (EVs). Currently, a lot of algorithms have been proposed to estimate the battery SOC. In this paper, we compared four filter-based algorithms, including the standard particle filter (PF), the unscented Kalman filter, the unscented Kalman-particle filter (UPF), and the extended Kalman-particle filter (EPF), in terms of the estimate accuracy and convergence rate. The federal urban driving schedule (FUDS) and the urban dynamometer driving schedule (UDDS) were applied to evaluate the performance of these estimation algorithms. Comparison results showed that compared with the UKF, the PF can improve the estimate accuracy, however, it takes much more time to correct the initial SOC error. By introducing the EKF and UKF into the particle filter, the convergence rate can be greatly improved without the decrease in estimate accuracy, and convergence rate is very close to that of the UKF.

Performance comparison of Kalman−based filters for nonlinear structural finite element model updating

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 paper, an algorithm of 3-D single observer passive location for fixed emitters based on the extended kalman particle filter (EPF) using the azimuth angle and its changing rate is proposed. EPF is a method of particle filter (PF) using the extended Kalman filter (EKF) as the importance function. The method obtains target emitter's position by the principle of kinematic geometry. The raw location results are rectified and smoothed by EPF algorithm to realize fast and high precision passive target's location. Simulation results show that the performance of algorithm based on EPF is better than EKF's has faster and steady convergence speed, better filtering effect and better location precision.

With the wide application of lithium-ion battery in various fields, the State of Health (SOH) estimation has become a research hotspot for advanced battery management system (BMS). Accurate SOH estimation is helpful to ensure the safe operation of equipment or system in practical applications. Among various lithium-ion battery health diagnosis methods, particle filter and its variants are the mainstream with the significant advantages in non-linear and non-Gaussian system modeling. But in practical applications, the BMS always suffers from the limited power supplication and finite computing resources. Therefore, this paper implemented a comparative study on particle filter (PF) and typical variants, including extended Kalman particle filter (EPF), unscented particle filter (UPF), regularized particle filter (RPF). Through the NASA’s battery degradation model, the performance of the above particle filter algorithms is compared and analyzed. The experimental results show that UPF has the highest estimation accuracy, and it is more suitable for the situation with higher prediction accuracy requirements. PF has the least time consumption and is more suitable for on-line health assessment.

Based on the concept of sequential importance sampling (SIS) and the use of Bayesian theory, particle filter is particularly useful in dealing with nonlinear and non-Gaussian problems. In this paper, a new particle filter is proposed that uses a divided difference filter to generate the importance proposal distribution is proposed. The proposal distribution integrates the latest measurements into system state transition density so it can match the posterior density well. The simulation results show that the new particle filter performs superior to the generic particle filter and other particle filters such as the extended Kalman particle filter and the unscented particle filter.

This study focuses on a system identification technique for a hydraulic based flight motion simulator (FMS). Because nonlinearities such as stiction and nonlinear orifice flow rate are often inherent in hydraulic systems, the system identification of the FMS can be very challenging. Simple non-parametric methods such as frequency response and step response methods have previously been used to estimate the parameters of the FMS model. These methods can sometimes provide inadequate results since the parameters are timevariant. Sophisticated parametric methods such as Extended Kalman filter, Unscented Kalman filter and Particle filter have also been used to estimate dynamic responses of a nonlinear system. However, the performance of those filters, in terms of convergence stability and error, are subjected to the initial conditions of the filters. In practice, tuning of those initial conditions can be time consuming. This paper highlights a novel evolutionary system identification approach which takes advantage of the knowledge of the physical system, the power of a non-parametric method such as frequency response, and the simplicity of the linear Kalman filter. Experimental results have shown that the estimated response of the FMS matches very well with the measured response despite changes in the operating conditions.

This paper proposes two new particle filters, namely, the auxiliary extended Kalman particle filter (AEKPF) and the auxiliary unscented Kalman particle filter (AUKPF). The theory governing the newly proposed filtering techniques is developed and the algorithms are described and contrasted. Next, a series of tests is presented in which the new filters are compared against the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and several existing particle filters. The test results are from simulations with synthetic mathematical models that incorporate elements that are nonlinear, non-stationary, and stochastic. Performance results are presented for various degrees of model nonlinearity including first, second, and third order systems. Furthermore, experimental results are also reported comparing the filters performances with different signal to noise ratios and noise models, including Gaussian, Cauchy, and Gamma distributions. Various metrics are used to compare the filters performances and to make conclusions about future work. It is shown to be advantageous to use certain particle filters depending on the noise distribution of the system of interest. In particular, the AUKPF and the AEKPF outperform existing particle filters in many cases.

The iterative extended Kalman filter (IEKF) algorithm is dependent on the previous data and estimation accuracy of the noise. The noise will result in filtering precision decrease and divergence. This paper puts forward an adaptive algorithm based on Levenberg-Marquardt method and two-factor for IEKF, which optimizes the iterative process by using Levenberg-Marquardt method, adjusts the prediction covariance by suboptimal fading factor. A simplified calculation method of suboptimal fading factor is also presented. The previous process noise is suppressed by exponential fading factor, and the observation noise is corrected by the residual and innovation, so that filter gain becomes larger, which improves the accuracy and robustness of the filter algorithm. The experiment results indicate the improved algorithm can effectively suppress the filter divergence. Compared with the classical extended Kalman filter (EKF), IEKF, and the modified IEKF, the filter accuracy and robustness of the improved algorithm are sufficient, and the algorithm can better meet the requirements of engineering applications.

Digital input and output modules are widely used to connect digital sensors and actuators to automation systems. Digital I/O modules provide flexible connectivity extension to numerous sensors and actuators and protect systems from high voltages and currents by isolation. Components in digital I/O modules are inevitably affected by operating and environmental conditions, such as high voltage, high current, high temperature, and temperature cycling. Because digital I/O modules transfer signals or isolate the systems from unexpected voltage and current transients, their failures may result in signal transmission failures and damages to sensitive circuitry leading to system malfunction and system shutdown. In this study, the lifetime of optocouplers, one of the critical components in digital I/O modules, was predicted using Bayesian tracking approaches. Accelerated degradation tests were conducted for collecting the critical performance parameter of optocouplers, current transfer ratio (CTR), during their lifetime. Bayesian tracking approaches, including extended Kalman filter and particle filter, were applied to predict the failure. The performance of each prognostic algorithm was then compared using accuracy and robustness-based performance metrics.

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

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks (WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter (CKPF) is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter (CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter (UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.

Sigma point Kalman filter for bearing only tracking

Two estimation techniques, the extended Kalman filter (EKF) and the iterative extended Kalman filter (IEKF), have been applied to a nonlinear time-varying system that has nonmeasurable state variables. An iterative solution to a fed-batch fermentation process is reported using the EKF based on measurements of the oxygen and carbon dioxide concentrations. The results demonstrate that this estimation technique can be successfully applied to complex biological processes. If the nonlinearities of these systems are sufficiently important or if a long delay in the estimation cannot be permitted for a particular process, then the IEKF can be selected. >