Algorithm of 3-D Single Observer Passive Location with the Extended Kalman Particle Filter

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.

A particle filter guided by iterated extended Kalman filter (IEGPF) is proposed to solve the problem of particle impoverishment. Firstly, particles are divided into two groups adaptively according to the maximum likelihood ratio, which is defined to measure how well the particles, drawn from transition prior density, match the likelihood model. Particles in one group are drawn from transition prior density, while those in the other group are drawn from the Gaussian approximate posterior density, obtained by running an iterated extended Kalman filter (IEKF). The results of numerical simulations, conducted at different observation noise level, show that IEGPF can produce more accurate estimate than extended Kalman particle filter (EPF) and unscented Kalman particle filter (UPF), whereas the computational cost is comparable to that of SIR.

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.

A new physical parameter identification method with a name of EKPF-LS is proposed for shear frame structures under limited inputs and outputs by a combination of extended Kalman particle filter (EKPF) and least square (LS) algorithm. The basic principle of EKPF-LS is to establish the proposed distribution function of the particle filter through EKF-LS. In this method, EKPF is introduced to get rid of the restriction of Gaussian white noise model and reduce the linearization error caused by EKF. Meanwhile, LS is utilized to address the problem of unmeasured excitation estimations. The effectiveness and accuracy of the proposed EKPF-LS method is verified by a numerical example of a four-story hysteretic shear frame under an earthquake excitation and an experimental test of a four-story shear type frame using Gaussian white noise and sine sweep signal as excitations, respectively. Gaussian colored noises are then added to the solved and measured response signals in the numerical example and experimental test, respectively. The results demonstrate that the proposed method can identify the stiffness of shear frame structures effectively and is superior to the existed EKF-LS approach when the structural system is nonlinear structural system or Colored noise model.

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 paper presents a novel state estimation system for unmanned aerial vehicle landing. A novel vision algorithm that detects a portion of the marker is developed, and this algorithm extends the detectable range of the vision system for any known marker. A vision-aided navigation algorithm is derived within extended Kalman particle filter and Rao–Blackwellized particle filter frameworks in addition to a standard extended Kalman filter framework. These multihypothesis approaches not only deal well with a highly nonlinear and non-Gaussian distribution of the measurement errors of vision but also result in numerically stable filters. The computational costs are reduced compared to a naive implementation of particle filter, and these algorithms run in real time. This system is validated through numerical simulation, image-in-the-loop simulation, and flight tests.

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.

In order to compare the performance of different state estimation algorithms in electrochamical model-based SOC(state of charge) estimation for lithium-ion battery, this paper proposed a series of SOC estimation approaches which use different algorithms including extended Kalman filter(EKF), adaptive extended Kalman filter(AEKF), particle filter(PF) and dichotomy. Their accuracy, convergence and computation efficiency was examined at the end of the paper.

Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm

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.

The foot-mounted navigation system based on Micro Inertial Measurement Unit (MIMU) can estimate the position of pedestrians. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial sensor. However, the low accuracy and high noise of the MIMU will greatly affect the measurement accuracy of zero velocity detector, which will lead to poor 3D pedestrian positioning accuracy. An improved generalized likelihood ratio test (GLRT) method is proposed by establishing adaptive thresholds at different gait frequency. The core is to dynamically adjust the detection threshold according to different motion states, so as to determine the constraint relation for detection. In addition, the drift of sensors and the accumulated error of position integration affect the accuracy of attitude and position. Therefore, an improved extended Kalman particle filter (EKPF) is proposed. The improved EKPF constructs the position constraint model in altitude direction by introducing barometer information. The new particle filter distribution function is constructed by constrained multi-source observation information, which makes the posterior probability distribution closer to the real distribution. In order to verify the feasibility and effectiveness of this method, the inertial pedestrian navigation system is established. The experimental results demonstrate that the improved GLRT method performs well in detecting the zero velocity point of the foot, and improved EKPF method reduces the positioning error by 69.25% and 53.57% compared with the EKF and PF, respectively. Therefore, the proposed method can improved the 3D pedestrian positioning accuracy.

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.

Fatigue crack growth prognosis is important for prolonging service time, improving safety, and reducing maintenance cost in many safety-critical systems, such as in aircraft, wind turbines, bridges, and nuclear plants. Combining fatigue crack growth models with the particle filter (PF) method has proved promising to deal with the uncertainties during fatigue crack growth and reach a more accurate prognosis. However, research on prognosis methods integrating on-line crack monitoring with the PF method is still lacking, as well as experimental verifications. Besides, the PF methods adopted so far are almost all sequential importance resampling-based PFs, which usually encounter sample impoverishment problems, and hence performs poorly. To solve these problems, in this paper, the piezoelectric transducers (PZTs)-based active Lamb wave method is adopted for on-line crack monitoring. The deterministic resampling PF (DRPF) is proposed to be used in fatigue crack growth prognosis, which can overcome the sample impoverishment problem. The proposed method is verified through fatigue tests of attachment lugs, which are a kind of important joint component in aerospace systems.

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.

Accurate power system state estimation facilitates power system control, optimization, and security analysis. In this paper, a joint filter approach is proposed to estimate both the static states (voltage magnitude and phase angle of bus) and the dynamic states (rotor angle, rotor speed, and transient voltages of generator) utilizing the measurements of phasor measurement unit. First, due to the complexity and dynamics of power system, the power system model is usually not known accurately. The uncertainties include both model uncertainty and noise statistics uncertainty. Therefore, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter is applied to reliable estimation of the static states. As a link between the static state estimation and the dynamic state estimation, the estimated static states are fed to dynamic state estimation. Next, to deal with the nonlinearity of synchronous generator model, an extended Kalman particle filter is employed to accurately estimate the dynamic states. The numerical tests on IEEE 30-bus system show that the proposed method is into both the static state estimation and the dynamic state estimation.