New Approach for GPS/INS Integrated Navigation

This paper presents a new robust adaptive unscented particle filtering algorithm by adopting the concept of the adaptive robust filtering to the unscented particle filter. This algorithm adaptively determines the equivalent weight function and adaptively adjusts the adaptive factor constructed from predicted residuals to resist the disturbances of singular observations and the kinematic model noise, thus preventing particles from degeneracy. It also uses the unscented transformation to improve the accuracy of particle filtering, thus providing the reliable state estimation for improving the performance of adaptive robust filtering. Experiments and comparison analysis demonstrate that the proposed filtering algorithm can effectively resist disturbances due to system state noise and observation noise, and the filtering accuracy is much higher than the extended Kalman filter, unscented Kalman filter, standard particle filter and unscented particle filter.

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.

Storage battery is one of the most important power sources in portable devices, marine systems, automotive vehicles, aerospace systems, and so on. For this kind of battery, it is essential to prognose its remaining useful life before its end of life, which would reduce some unnecessary sudden disasters caused by battery failure. In this article, we propose an improved unscented particle filter method for prognosing the remaining useful life of storage battery, in which the sigma samples of unscented transformation in traditional unscented particle filter are generated by singular value decomposition, and then, those sigma points are propagated by the standard unscented Kalman filter to generate a sophisticated proposal distribution. When both improved unscented particle filter and unscented particle filter methods were used for prognosing the remaining useful life of storage battery, it shows that the performance of improved unscented particle filter is better than unscented particle filter; the proposed method is more robust in remaining useful life prognosis procedure.

A new quaternion-based method for the SINS/SAR (strap-down inertial navigation system/synthetic aperture radar) integrated navigation system is presented. This method overcomes the shortcomings due to the linear SINS error model used in the currently existing SINS/SAR integrated navigation systems. A quaternion-based matrix is derived for describing the attitude of SINS. Quaternion-based nonlinear error and observation models are established for the SINS/SAR integrated navigation system. An adaptive unscented particle filtering (UPF) algorithm is developed based on the quaternion-based nonlinear models for optimal data fusion in the SINS/SAR integrated navigation system. Experimental results demonstrate that the proposed quaternion based method can effectively reduce the navigation error and improve the navigational positioning precision.

For land-based navigation, Euler angles are often used in INS/GPS integrated navigation systems. However, the trigonometric operations required in the updates and forming of the rotation matrices for transforming the INS measurements from the body frame to the navigation frame turns the system model to be highly nonlinear. Besides, using low-cost MEMS-based IMUs, the gyroscope bias errors must be correctly estimated and compensated, which makes the nonlinearity problem a critical one. In this contribution, three Kalman filtering methods (i.e., Extended Kalman filter with simplified system model, Extended Kalman filter with linearized system model and Unscented Kalman filter with nonlinear system model) are utilized in INS/GPS tightly-coupled integration. Simulations and field experiments are conducted. Numerical results are compared in terms of both estimation accuracy and processing time.

Particle filter tracking, a type of sequential Monte Carlo method, has long been considered to be a very promising but time-consuming tracking technique. Methods have been developed to include a particle filter as part of a Variable Structure, Interactive Multiple Model (VS-IMM) structure and to integrate it into the Multiple Hypothesis Tracker (MHT) scoring structure. By integrating a particle filter as just one of many filters in Raytheon's MHT, the particle filter is applied sparingly on difficult off-road targets. This dramatically reduces the computation time as well as improves tracking performance in circumstances in which the other filters do not excel. Moreover, terrain information may be taken into account in the particle propagation process. In particular, an Unscented Particle Filter (UPF) was implemented in order to address the potential dominance of a small set of degenerate particles and/or poor prior distribution sampling from hampering the ability of the particle filter to accurately handle a maneuver. The Unscented Particle Filter treats every particle as its own Kalman filter. After the distribution of particles is adjusted in order to take into account the terrain, each particle is divided into sigma point states. These sigma points are propagated forward in time and then recombined to form a new composite particle state and covariance. These reformed particles are used in scoring and can be updated with a new observation. Since the Unscented Particle Filter includes the covariances in these calculations, this particle filter approach is more accurate and potentially requires fewer particles than an ordinary particle filter. By adding an Unscented Particle Filter to the other filters in an MHT tracker, the advantages of the UPF can be utilized in an efficient manner in order to enhance tracking performance.

As a new type of guidance technology, the strapdown imaging guidance technology, which helps improve the system reliability and reduce costs efficiently, has been paid great attention and developed quickly. However, the detecting information of strapdown seeker can’t be used to proportional navigation directly because it is coupled with missile attitude. A method to estimate the inertial Line-of-Sight (LOS) rate of strapdown imaging seeker based on Cubature Kalman filter (CKF) and Tracking-Differentiator (TD) was presented. As there were high nonlinearity in both state and measurement equations and more serious non-Gaussian noise in the measurements, the Extended Kalman filter (EKF) could not completely meet the requirements of filtering. Compared with EKF and Unscented Particle filter (UKF), CKF was a congruent method for states estimating in the conditions of nonlinearity and non-Gaussian noise. CKF was applied to estimate the LOS rate of strapdown imaging seeker. Because measurement noise of missile attitude will be reflected in estimation result, TD was used to decrease noise of the missile attitude measurements for improving the estimation precision. Monte Carlo simulation results show that the proposed method can improve the precision of guidance.

As one of the key functions in lithium-ion battery management system, the state-of-health (SOH) estimation is of great significance to ensure the safe and reliable operation and reduce the maintenance cost of the battery energy storage system. Unscented particle filter (UPF) algorithm is becoming a promising method for battery state estimation since it combines the latest measurement information to give the proposal distribution which is closer to the true posterior distribution. At the same time, UPF algorithm is able to represent the uncertainty involved in the estimation results, which makes great significance for battery SOH estimation. On the other hand, it is difficult to measure the battery actual capacity in practice despite the capacity is a direct indicator of battery SOH. In this paper, an on-line health indicator (HI) is extracted from the measurable parameters while battery is working. The mapping model between the extracted HI and battery SOH is established and applied as the observation in the state-space model. An on-line estimator based on UPF algorithm is developed for battery SOH assessment. The maximum estimation error based on battery cycling test data is less than 5%. This indicates that the proposed method has a good adaptability for lithium-ion battery degradation with non-linear and non-Gaussian characteristics. Additionally, the experiments on different types of lithium-ion battery show the good robustness and applicability of this approach.

To solve the problem of information fusion in the strapdown inertial navigation system (SINS)/celestial navigation system (CNS)/global positioning system (GPS) integrated navigation system described by the nonlinear/non-Gaussian error models, a new algorithm called the federated unscented particle filtering (FUPF) algorithm was introduced. In this algorithm, the unscented particle filter (UPF) served as the local filter, the federated filter was used to fuse outputs of all local filters, and the global filter result was obtained. Because the algorithm was not confined to the assumption of Gaussian noise, it was of great significance to integrated navigation systems described by the non-Gaussian noise. The proposed algorithm was tested in a vehicle’s maneuvering trajectory, which included six flight phases: climbing, level flight, left turning, level flight, right turning and level flight. Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter (FUKF). For instance, the mean of position-error decreases from (0.640×10−6 rad, 0.667×10−6 rad, 4.25 m) of FUKF to (0.403×10−6 rad, 0.251×10−6 rad, 1.36 m) of FUPF. In comparison of the FUKF, the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.

This paper proposes a new move toward to assess the performance of sensorless control of interior permanent magnet synchronous motor (IPMSM) drive along with electromagnetic noise effect by using EKF. Normally in rotary condition, rotor position and speed estimation of IPMSM drive are drawn through an Extended Kalman Filter (EKF) algorithm by measuring its voltages and currents of the stator. The main drawback in developing EKF is it may not proficient to consider the effect of electromagnetic noise which is mainly produced during the time of different speed ranges. Owing to this reason this may cause to vary the motor flux linkages which are significant to find the rotor position and speed by EKF method will give approximate results. To carry on this process, we present the simulation results for sensorless speed control of IPMSM drive by using EKF algorithm with the incorporation of a noise signal which is corresponding to the frequency of electromagnetic noise signal using MATLAB/Simulink software. The armature current, rotor position, and speed estimation are analyzed under this noise signal effect and the effectiveness of the EKF for sensorless control of IPMSM drive is observed.

Fast non-intrusive estimation of liquid-phase interface and boundary heat flux in participating media by the Kalman filtering methods

We present a new unscented particle filter for dynamic systems that outperforms the general particle filter and the unscented particle filter when the variance of the observation noise is small. Our algorithm uses a bank of unscented Kalman filters to refine the prediction in particle filter. The key difference with the traditional unscented particle filter is the introduction of an auxiliary model and a bank of unscented Kalman filter with this auxiliary model to generate the importance distribution in the particle filter. This structure makes efficient use of the latest observation information. Our new algorithm use fewer particles than the general particle filters and its performance outperforms them.

Combining the interacting multiple model (IMM) and the unscented particle filter (UPF), a new multiple model filtering algorithm is presented. Multiple models can adapt to targets’ high maneuvering. Particle filter can deal with the nonlinear or non-Gaussian problems and the unscented Kalman filter (UKF) may improve the approximate accuracy. Compared with other interacting multiple model algorithms in the target tracking simulations, the results demonstrate the validity of the new filtering method, that is, particle filter with the UKF proposal.

The signal collected by current sensor will contain various noises, which will have a negative impact on the its accuracy and calibration. In order to remove unwanted noises, an extended Kalman filter (EKF) method based on backpropagation (BP) neural network is proposed in this paper. BP neural network has good adaptive ability and non-linear mapping ability. EKF can effectively filter noise and improve the calibration accuracy for non-linear systems. The signal collected by current sensor is processed by EKF and is used as the input signal of BP neural network. The trained neural network can modify the output signal of EKF, so as to improve the calibration accuracy. The angle difference and ratio difference of current sensor calibration are below 0.1, which meets the national standard and shows the effectiveness of this method.

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