Unscented Kalman filter for a low-cost GNSS/IMU-based mobile mapping application under demanding conditions

For the last two decades,low-cost Global Navigation Satellite System(GNSS)receivers have been used in various applications.These receivers are mini-size,less expensive than geodetic-grade receivers,and in high demand.Irrespective of these outstanding features,low-cost GNSS receivers are potentially poorer hardwares with internal signal processing,resulting in lower quality.They typically come with low-cost GNSS antenna that has lower performance than their counterparts,particularly for multipath mitigation.Therefore,this research evaluated the low-cost GNSS device performance using a high-rate kinematic survey.For this purpose,these receivers were assembled with an Inertial Measurement Unit(IMU)sensor,which actively transmited data on acceleration and orientation rate during the observation.The position and navigation parameter data were obtained from the IMU readings,even without GNSS signals via the U-blox F9R GNSS/IMU device mounted on a vehicle.This research was conducted in an area with demanding conditions,such as an open sky area,an urban environment,and a shopping mall basement,to examine the device’s performance.The data were processed by two approaches:the Single Point Positioning-IMU(SPP/IMU)and the Differential GNSS-IMU(DGNSS/IMU).The Unscented Kalman Filter(UKF)was selected as a filtering algorithm due to its excellent performance in handling nonlinear system models.The result showed that integrating GNSS/IMU in SPP processing mode could increase the accuracy in eastward and northward components up to 68.28%and 66.64%.Integration of DGNSS/IMU increased the accuracy in eastward and northward components to 93.02%and 93.03%compared to the positioning of standalone GNSS.In addition,the positioning accuracy can be improved by reducing the IMU noise using low-pass and high-pass filters.This application could still not gain the expected position accuracy under signal outage conditions.

Robust Non-Coherent Demodulation Scheme for Bluetooth Voice Transmission Using Linear, Extended, and Unscented Kalman Filtering

This paper presents a novel and cost effective method to be used in the optimization of the Gaussian Frequency Shift Keying (GFSK) at the receiver of the Bluetooth communication system. The proposed method enhances the performance of the noncoherent demodulation schemes by improving the Bit Error Rate (BER) and Frame Error Rate (FER) outcomes. Linear, Extended, and Unscented Kalman Filters are utilized in this technique. A simulation model, using Simulink, has been created to simulate the Bluetooth voice transmission system with the integrated filters. Results have shown improvements in the BER and FER, and that the Unscented Kalman Filters (UKF) have shown superior performance in comparison to the linear Kalman Filter (KF) and the Extended Kalman Filter (EKF). To the best of our knowledge, this research is the first to propose the usage of the UKF in the optimization of the Bluetooth System receivers in the presence of additive white Gaussian noise (AWGN), as well as interferences.

一种基于模型概率单调性变化的自适应IMM-UKF改进算法

针对现有交互式多模型(IMM)算法模型间切换迟滞和转换速率慢的缺点,提出一种基于模型概率单调性变化的自适应交互式多模型无迹卡尔曼滤波改进算法(mIMM-UKF)。该算法利用后验信息模型概率的单调性,对马尔可夫转移概率矩阵及模型估计概率进行二次修正,加快了匹配模型的切换速度及转换速率。仿真结果表明,与现有算法相比,该算法通过快速切换匹配模型,有效提高了水下目标跟踪精度。

Unscented extended Kalman filter for target tracking

A new method of unscented extended Kalman filter （UEKF） for nonlinear system is presented. This new method is a combination of the unscented transformation and the extended Kalman filter （EKF）. The extended Kalman filter is similar to that in a conventional EKF. However, in every running step of the EKF the unscented transformation is running, the deterministic sample is caught by unscented transformation, then posterior mean of non- lineadty is caught by propagating, but the posterior covariance of nonlinearity is caught by linearizing. The accuracy of new method is a little better than that of the unscented Kalman filter （UKF）, however, the computational time of the UEKF is much less than that of the UKF.

Modified unscented Kalman filter using modified filter gain and variance scale factor for highly maneuvering target tracking

To improve the low tracking precision caused by lagged filter gain or imprecise state noise when the target highly maneuvers, a modified unscented Kalman filter algorithm based on the improved filter gain and adaptive scale factor of state noise is presented. In every filter process, the estimated scale factor is used to update the state noise covariance Qk, and the improved filter gain is obtained in the filter process of unscented Kalman filter （UKF） via predicted variance Pk｜k-1, which is similar to the standard Kalman filter. Simulation results show that the proposed algorithm provides better accuracy and ability to adapt to the highly maneuvering target compared with the standard UKF.

Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

An adaptive unscented Kalman filter （AUKF） and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter （UKF） when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.

A new real-time eye tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue

A new scheme for driver fatigue detection is presented, which is based on the nonlinear unscented Kalman filter and eye tracking. Assuming a probability distribution than to approximate an arbitrary nonlinear function or transformation, eye nonlinear tracking can be achieved using an unscented transformation (UT), which adopts a set of deterministic sigma points to match the posterior probability density function of the eye movement. Driver fatigue can be detected using the percentage of eye closure (PERCLOS) framework in a realistic driving condition after the eye nonlinear tracking. This system was tested adequately in realistic driving environments with subjects of different genders, with/without glasses, in day/night driving, being commercial/noncommercial drivers, in continuous driving time, and under different road conditions. The last experimental results show that the proposed method not only improves the robustness for nonlinear eye tracking, but also can provide more accurate estimation than the traditional Kalman filter.

Airship aerodynamic model estimation using unscented Kalman filter

An airship model is made-up of aerostatic,aerodynamic,dynamic,and propulsive forces and torques.Besides others,the computation of aerodynamic forces and torques is difficult.Usually,wind tunnel experimentation and potential flow theory are used for their calculations.However,the limitations of these methods pose difficulties in their accurate calculation.In this work,an online estimation scheme based on unscented Kalman filter(UKF)is proposed for their calculation.The proposed method introduces six auxiliary states for the complete aerodynamic model.UKF uses an extended model and provides an estimate of a complete state vector along with auxiliary states.The proposed method uses the minimum auxiliary state variables for the approximation of the complete aerodynamic model that makes it computationally less intensive.UKF estimation performance is evaluated by developing a nonlinear simulation environment for University of Engineering and Technology,Taxila(UETT)airship.Estimator performance is validated by performing the error analysis based on estimation error and 2-σ uncertainty bound.For the same problem,the extended Kalman filter(EKF)is also implemented and its results are compared with UKF.The simulation results show that UKF successfully estimates the forces and torques due to the aerodynamic model with small estimation error and the comparative analysis with EKF shows that UKF improves the estimation results and also it is more suitable for the under-consideration problem.

Phase noise filtering and phase unwrapping method based on unscented Kalman filter

This paper presents a new phase unwrapping algorithm based on the unscented Kalman filter（UKF） for synthetic aperture radar（SAR） interferometry.This method is the result of combining an UKF with path-following strategy and an omni-directional local phase slope estimator.This technique performs simultaneously noise filtering and phase unwrapping along the high-quality region to the low-quality region,which is also able to avoid going directly through the noisy regions.In addition,phase slope is estimated directly from the sample frequency spectrum of the complex interferogram,by which the underestimation of phase slope is overcome.Simulation and real data processing results validate the effectiveness of the proposed method,and show a significant improvement with respect to the extended Kalman filtering（EKF） algorithm and some conventional phase unwrapping algorithms in some situations.

基于改进的联邦UKF无人艇组合导航系统设计

针对无人艇在高海况下长航时,大幅度作业滤波精度较低的问题,提出一种基于联邦结构的无迹卡尔曼滤波(Unscented Kalman Filtering,UKF)算法,将其应用于自主研制的无人艇组合导航系统中。建立系统误差方程和量测方程;引入渐消因子、基于量测值与预测量测值差值的可变因子和自适应最优信息分配因子对联邦UKF算法进行改进,保持信息的强跟踪特性和组合导航系统的信息融合精度,得到全局最优估计值。开展湖试试验,验证该组合导航系统的有效性,结果表明该系统实时性、稳定性好,抗干扰能力强,能有效提高导航精度。该方法不仅能为无人艇作业提供安全保障,而且可供其他组合导航系统设计参考。

Interactive Kalman Filtering for Differential and Gaussian Frequency Shift Keying Modulation with Application in Bluetooth

Some applications are constrained only to implement low cost receivers. In this case, designers are required to use less complex and non-expensive modulation techniques. Differential Quadrature Phase Shift Keying (DQPSK) and Gaussian Frequency Shift Keying (GFSK) can be non-coherently demodulated with simple algorithms. However, these types of demodulation are not robust and suffer from poor performance. This paper proposes a new method to enhance the performance of DQPSK and GFSK using Interactive Kalman Filtering (IKF) technique, in which a one Unscented Kalman Filter (UKF) and two Kalman Filters (KF) are coupled to optimize the demodulated signals. This method consists of simple but very effective algorithms without adding complexity to the demodulators comparing to other very complex methods. UKF is used in this method due to its superiority in approximating and estimating nonlinear systems and its ability to handle non-Gaussian noise environments. The proposed method has been validated by creating a MATLAB/SIMULINK Bluetooth system model, in which the IKF is integrated into the receiver, which implement both DQPSK and GFSK, and run simulation in Gaussian and Non-Gaussian noise environments. Results have shown the effectiveness of this method in optimizing the received signals, and that the UKF outperforms the Extended Kalman Filter (EKF).

Fault tolerant navigation method for satellite based on information fusion and unscented Kalman filter

An effective autonomous navigation system for the integration of star sensor,infrared horizon sensor,magnetometer,radar altimeter and ultraviolet sensor is developed.The requirements of the integrated navigation system manager make optimum use of the various navigation sensors and allow rapid fault detection,isolation and recovery.The normal full fusion feedback method of federated unscented Kalman filter（UKF） cannot meet the needs of it.So a no-reset feedback federated Kalman filter architecture is developed and used in the autonomous navigation system.The minimal skew sigma points are chosen to improve the calculation speed.Simulation results are presented to demonstrate the advantages of the algorithm.These advantages include improved failure detection and correction,improved computational efficiency,and reliability.Additionally,its＇ accuracy is higher than that of the full fusion feedback method.

Control of epileptiform spikes based on nonlinear unscented Kalman filter

A new control strategy based on nonlinear unscented Kalman filter （UKF） is proposed for a neural mass model that serves as a model for simulating real epileptiform stereo-electroeneephalographic （SEEG） signals. The UKF is used as an observer to estimate the state from the noisy measurement because it has been proved to be effective for state estimation of nonlinear systems. A UKF controller is constructed via the estimated state and is illustrated to be effective for epileptiform spikes suppression of aforementioned model by numerical simulations.

基于ASIT-UKF算法的锂电池荷电状态估计

针对无迹卡尔曼滤波(unscented Kalman filter,UKF)算法估计锂电池荷电状态(state of charge,SOC)时精度低、稳定性差、产生的sigma点过多导致计算难度大等不足,提出一种基于自适应球形不敏变换方式的无迹卡尔曼滤波(unscented Kalman filter based on adaptive spherical insensitive transformation,ASIT-UKF)算法。该算法通过使用球形不敏变换方式选择权系数以及初始化一元向量对sigma点的产生进行选取。与UKF算法相比,ASIT-UKF算法产生的sigma点减少近50%,使得算法的计算复杂度大大降低。同时,将产生的所有sigma点进行单位球形面上的归一化处理,提高了数值的稳定性。考虑到实际运行中锂电池系统噪声干扰带来的不确定性,加入Sage-Husa自适应滤波器对不确定性噪声的干扰进行实时更新和修正,以达到提高在线锂电池SOC估计精度的目的。最后,将均方根误差和最大绝对误差计算公式引入到性能估计指标中。实验结果表明,ASIT-UKF算法在准确度、鲁棒性和收敛性方面具有优越的性能。

Application of Unscented Kalman Filter in Satellite Orbit Simulation

A new estimate method is proposed, which takes advantage of the unscented transform method, thus the true mean and covariance are approximated more accurately. The new method can be applied to non-linear systems without the linearization process necessary for the EKF, and it does not demand a Gaussian distribution of noise and what’s more, its ease of implementation and more accurate estimation features enables it to demonstrate its good performance in the experiment of satellite orbit simulation. Numerical experiments show that the application of the unscented Kalman filter is more effective than the EKF.

A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

采用自适应Unscented Kalman的粒子滤波

针对粒子滤波的粒子退化问题,使用自适应UKF进行重点采样。该方法使用自适应因子调整Unscented Kalman滤波的观测信息与动力学信息之间的权比,使滤波预测值的协方差更趋向真实值。与扩展Kalman滤波、自适应扩展Kalman滤波、Unscented Kalman滤波重点采样方法相比,自适应UKF重点采样进一步提高了粒子滤波的精度。

基于无迹Kalman滤波的车辆速度和质心侧偏角的估计

为满足车辆主动安全控制功能需求,需提升车辆在强非线性特性下的状态观测精度,提出一种基于无迹Kalman滤波(UKF)的模块化车辆横纵向速度状态观测器结构。该结构利用车载传感器信息,结合UKF观测纵向和横向速度,根据质心侧偏角的定义,计算车辆质心侧偏角。在干燥路面上,进行数字仿真以及实车实验。结果表明:在强非线性状态下,基于UKF的车辆质心侧偏角估计的仿真结果的均方根误差(RMSE)为0.425°,实车实验的RMSE为0.001°,而使用扩展Kalman滤波(EKF)估计的仿真结果 RMSE为0.968°,实车实验的RMSE为0.009°。因此,UKF可以抑制车辆行驶中的干扰对观测的影响,使本观测器结构有较高的观测精度,可满足工程需要。

基于UKF的四旋翼飞行器空气阻力系数辨识

针对微小型四旋翼飞行器动力学模型存在强非线性和强耦合性,空气阻力系数难以通过实验获得的问题,设计了一种机理建模与辨识建模相结合的方法,将待辨识参数作为状态变量增广到飞行器的动力学模型中,然后采用无迹卡尔曼滤波(UKF)算法对状态量进行估计。针对UKF算法发散的问题采用了一种基于状态方差阵对角相似分解的采样策略,最终有效辨识出了飞行器的三轴空气阻力系数。实验结果表明了UKF应用于四旋翼飞行器模型参数辨识的可行性与有效性。

基于TVFRLS和SVD-UKF的锂离子电池SOC估算

在汽车复杂运行工况下,传统离线参数辨识方法辨识准确度低,无迹卡尔曼滤波(UKF)算法在估计荷电状态(SOC)过程中,协方差矩阵非正定,导致算法估计SOC失败。提出采用时变遗忘因子递推最小二乘法(TVFRLS)与奇异值无迹卡尔曼滤波(SVD-UKF)算法进行联合在线SOC估计,提高复杂工况下算法的准确性与鲁棒性。通过城市道路循环(UDDS)工况对算法进行验证,TVFRLS与SVD-UKF联合算法模拟仿真的最大绝对误差(AEE)为1.31%、平均绝对误差(MEA)为0.56%、均方根误差(RMSE)为0.75%。相较于传统UKF算法,MEA与RMSE分别降低了60.0%和51.9%。