一种低动态高抖动环境下的GPS/INS紧组合方法

针对低动态高抖动环境下,影响GPS/INS紧组合精度的重要因素——惯性测量单元(IMU)数据中的噪声,该文提出利用小波降噪方法分离IMU数据中的噪声和有用信号以提高GPS/INS紧组合的精度。首先对IMU数据进行小波分解后得到的高频系数进行阈值量化处理,然后将GPS观测数据与降噪后的IMU数据进行GPS/INS紧组合解算,最终得到载体的导航信息。实例结果表明,该方法可以大幅提升GPS/INS紧组合的精度和稳定可靠性。     

Avoiding numerical stability problems of long duration DGPS/INS Kalman filters

A current pursuit of the geodetic community is the optimal integration of differential GPS (DGPS) and inertial navigation system (INS) data streams for precise and efficient position and gravity vector surveying. Therein a complete INS and multiple-antenna GPS receiver payload, mounted on a moving platform, is used in conjunction with a network of ground-fixed single antenna GPS receivers. This paper presents a complete, GPS-based, external updating measurement model for the applicable Kalman filter. The model utilizes four external observation types for every GPS satellite in-view: DGPS range differences, single phase differences, and single phase-rate differences; as well as the mobile, multipleantenna GPS receiver's measurement of theerrors in the INS's estimate of the phase difference between any two vehicle-borne GPS antennae. Although not widely conveyed in the geodetic world, the inertial navigation community has long known that traditional Kalman filter covariance propagation recurrences are inherently unstable when such highly accurate external updates are repeatedly applied (every 1 second) over long time durations. A hybrid square root covariance/U — D covariance factorization approach is a numerically stable alternative and is reviewed herein. The hybrid makeup of the algorithm is necessitated by the correlated nature of the fourth type of GPS external measurement listed above (each vehicle-borne GPS antenna formstwo baselines). Such measurement correlations require a functional transformation of the overall external updating model to permit the multiple updates (simultaneously available at each updating epoch) to be sequentially (and efficiently) processed. An appropriate transformation is given. Stable covariance propagation relationships are presented and the transformed Kalman gain is also furnished and its use in the determination of the externally updated error states is discussed. Specific DGPS/INS instabilities produced by the traditional recurrences are displayed. The stable alternative method requires about 25% more CPU time than the traditional Kalman recurrences. With the ever-increasing computational speeds of microprocessors, this added CPU time is of no real concern.     

Adaptive Kalman Filtering for INS/GPS

After reviewing the two main approaches of adaptive Kalman filtering, namely, innovation-based adaptive estimation (IAE) and multiple-model-based adaptive estimation (MMAE), the detailed development of an innovation-based adaptive Kalman filter for an integrated inertial navigation system/global positioning system (INS/GPS) is given. The developed adaptive Kalman filter is based on the maximum likelihood criterion for the proper choice of the filter weight and hence the filter gain factors. Results from two kinematic field tests in which the INS/GPS was compared to highly precise reference data are presented. Results show that the adaptive Kalman filter outperforms the conventional Kalman filter by tuning either the system noise variance–covariance (V–C) matrix `Q' or the update measurement noise V–C matrix `R' or both of them. Received: 14 September 1998 / Accepted: 21 December 1998     

扩展区间Kalman滤波器及其在GPS/INS组合导航中的应用

针对具有不确定动态模型参数的 GPS/INS 组合导航系统,首先介绍一种新型的区间Kalman滤波器,讨论了GPS/INS 组合系统中模型参数不确定性的问题,分析了惯性传感器建模中相关时间常数的区间特性,并建立了适合非线性特性的GPS/INS组合系统的扩展区间卡尔曼滤波器.计算结果表明,扩展区间卡尔曼滤波器对非线性GPS/INS组合系统是很有效的,它能给出组合系统导航误差的上下界,这对组合系统的设计具有指导的意义.     

GNSS/INS紧组合最大熵卡尔曼滤波算法

针对实际环境中量测噪声易被野值污染而呈现非高斯分布,进而导致传统卡尔曼滤波(KF)算法性能降低的问题,提出了最大熵卡尔曼滤波(MCKF)算法. 该算法基于最大熵准则(MCC)和M估计的思想推导得到. 与KF相比,所提算法能够给异常量测值分配较小的权重以减轻其对于状态估计的影响,与基于Huber函数的卡尔曼滤波(HKF)算法相比,其能够更有效地利用量测信息,因此所提算法相比于KF和HKF而言更加鲁棒. 在全球卫星导航系统(GNSS)与惯性导航系统(INS)的紧组合模式下进行车载实测实验,由于GNSS的伪距与伪距率等原始量测信息质量不佳,因此KF和HKF的性能均受到影响,而所提MCKF算法能够有效地抑制异常量测值的影响,能够更快地收敛且得到更高的估计精度.      

Adaptive GPS/INS integration for relative navigation

Relative navigation based on GPS receivers and inertial measurement units is required in many applications including formation flying, collision avoidance, cooperative positioning, and accident monitoring. Since sensors are mounted on different vehicles which are moving independently, sensor errors are more variable in relative navigation than in single-vehicle navigation due to different vehicle dynamics and signal environments. In order to improve the robustness against sensor error variability in relative navigation, we present an efficient adaptive GPS/INS integration method. In the proposed method, the covariances of GPS and inertial measurements are estimated separately by the innovations of two fundamentally different filters. One is the position-domain carrier-smoothed-code filter and the other is the velocity-aided Kalman filter. By the proposed two-filter adaptive estimation method, the covariance estimation of the two sensors can be isolated effectively since each filter estimates its own measurement noise. Simulation and experimental results demonstrate that the proposed method improves relative navigation accuracy by appropriate noise covariance estimation.     

Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter

Dead reckoning techniques such as inertial navigation and odometry are integrated with GPS to avoid interruption of navigation solutions due to lack of visible satellites. A common method to achieve a low-cost navigation solution for land vehicles is to use a MEMS-based inertial measurement unit (IMU) for integration with GPS. This integration is traditionally accomplished by means of a Kalman filter (KF). Due to the significant inherent errors of MEMS inertial sensors and their time-varying changes, which are difficult to model, severe position error growth happens during GPS outages. The positional accuracy provided by the KF is limited by its linearized models. A Particle filter (PF), being a nonlinear technique, can accommodate for arbitrary inertial sensor characteristics and motion dynamics. An enhanced version of the PF, called Mixture PF, is employed in this paper. It samples from both the prior importance density and the observation likelihood, leading to an improved performance. Furthermore, in order to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, the use of pitch and roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update is proposed in this paper. These updates aid the IMU and limit the positional error growth caused by two horizontal gyroscopes, which are a major source of error during GPS outages. The performance of the proposed method is examined on road trajectories, and results are compared to the three different KF-based solutions. The proposed Mixture PF with velocity, pitch, and roll updates outperformed all the other solutions and exhibited an average improvement of approximately 64% over KF with the same updates, about 85% over KF with velocity updates only, and around 95% over KF without any updates during GPS outages.     

UKF在GPS/INS组合导航系统中的应用

扩展卡尔曼滤波(EKF)是GPS/INS组合导航系统工程实现中常用的一种数据融合方式。但EKF线性化误差在一定程度上影响了GPS/INS组合导航系统精度的提高。Unscented卡尔曼滤波器(UKF)是一种非线性滤波器,它能有效地减小线性化误差对GPS/INS组合导航系统精度的影响。基于四元数法建立了GPS/INS组合导航系统的非线性误差方程模型;最后通过数字仿真验证了UKF组合导航系统应用中的性能。     

Application of Adaptive Kalman Filtering Algorithm in IMU/GPS Integrated Navigation System

The IMU(inertial measurement unit) error equations in the earth fixed coordinates are introduced firstly. A fading Kalman filtering is simply introduced and its shortcomings are analyzed, then an adaptive filtering is applied in IMU/GPS integrated navigation system, in which the adaptive factor is replaced by the fading factor. A practical example is given. The results prove that the adaptive filter combined with the fading factor is valid and reliable when applied in IMU/GPS integrated navigation system.     

Optimization of two GPS/MEMS-IMU integration strategies with application to sports

The application of low-cost L1 GPS receivers integrated with micro-electro-mechanical system (MEMS) inertial measurement units (IMU) allows the continuous observation of position, velocity and orientation which opens new possibilities for comparison of athletes’ performance throughout a racecourse. In this paper, we compare loosely and closely coupled integration strategies under realistic racing scenarios when GPS is partially or completely masked. The study reveals that both integration approaches have a similar performance when the satellite constellation is completed or the outages are short. However, for less than four satellites, the closely coupled strategy clearly outperforms the loosely coupled approach. The second part of the paper is devoted to the important problem of system initialization, because the conventional GPS/IMU alignment methods are no longer applicable when using MEMS-IMU. We introduce a modified coarse alignment method and a quaternion estimation method for the computation of the initial orientation. Simulations and practical experiments reveal that both methods are numerically stable for any initial orientation of the sensors with the error characteristics of MEMS-IMUs. Throughout the paper, our findings are supported by racing experiments with references provided in both, the measurement and the navigation domains.     

A wavelet-extreme learning machine for low-cost INS/GPS navigation system in high-speed applications

The combined navigation system consisting of both global positioning system (GPS) and inertial navigation system (INS) results in reliable, accurate, and continuous navigation capability when compared to either a GPS or an INS stand-alone system. To improve the overall performance of low-cost micro-electro-mechanical systems (MEMS)-based INS/GPS by considering a high level of stochastic noise on low-cost MEMS-based inertial sensors, a highly complex problems with noisy real data, a high-speed vehicle, and GPS signal outage during our experiments, we suggest two approaches at different steps: (1) improving the signal-to-noise ratio of the inertial sensor measurements and attenuating high-frequency noise using the discrete wavelet transform technique before data fusion while preserving important information like the vehicle motion information and (2) enhancing the positioning accuracy and speed by an extreme learning machine (ELM) which has the characteristics of quick learning speed and impressive generalization performance. We present a single-hidden layer feedforward neural network which is employed to optimize the estimation accuracy and speed by minimizing the error, especially in the high-speed vehicle and real-time implementation applications. To validate the performance of our proposed method, the results are compared with an adaptive neuro-fuzzy inference system (ANFIS) and an extended Kalman filter (EKF) method. The achieved accuracies are discussed. The results suggest a promising and superior prospect for ELM in the field of positioning for low-cost MEMS-based inertial sensors in the absence of GPS signal, as it outperforms ANFIS and EKF by approximately 50 and 70%, respectively.     

神经网络在SINS/GPS组合定位中的应用

地籍测量中,单一系统无法满足定位要求,组合定位技术应运而生.其中,捷联惯性导航系统(SINS)和GPS组合定位应用最为广泛.在卫星信号受到干扰失效区域,系统进入纯SINS解算,定位误差会逐渐累积,无法满足定位精度要求.针对此问题,提出一种长短期记忆(LSTM)神经网络辅助的组合定位算法.根据LSTM神经网络能够有效运用...     

基于抗差EKF的GNSS/INS紧组合算法研究

提出了GNSS/INS紧组合导航的抗差EKF算法,采用21状态GNSS/INS紧组合状态方程,根据多余观测分量及预测残差统计构造抗差等价增益矩阵,建立抗差EKF算法,通过迭代给出GNSS/INS组合导航的抗差解,并开发GNSS/INS紧组合导航模拟平台,通过对观测值加入单粗差、多粗差及缓慢增长三类误差,测试本文算法对不同粗差的抑制能力。分析表明,抗差EKF可以将三类粗差抑制在相应观测值的残差中,达到削弱其对状态参数估计的影响。本文算例证明,抗差EKF算法可将导航解的误差精度从dm级提高为cm级甚至mm级,导航精度及可靠性得到明显提高。     

惯性导航与卫星导航紧耦合技术发展现状

对紧耦合方案、最优估计滤波算法和硬件开发等几方面进行了总结分析。分析了传统的INS／GPS松组合系统和INS／GPS紧耦合系统的区别；给出了INS／GPS紧耦合系统国内外的发展状况；指出INS／GPS紧耦合系统所涉及的关键技术。     

BDS RTK／MEMS-INS组合数学模型研究与性能分析

卫星导航系统和惯性导航系统（INS）具有极强的互补性,两者组合能有效提高导航定位结果的可用性、连续性和可靠性. 随着北斗卫星导航系统（BDS）的快速发展和低成本惯导元件（IMU）性能的不断提高,进行基于BDS和低成本IMU的组合导航系统相关理论和技术研究具有很强的研究意义和实用价值. 本文首先对BDS RTK／M-EMS INS组合理论模型进行推导,并利用实测车载数据对组合系统的性能进行分析. 实验结果表明,在BDS中引入低成本IMU,可以在不损失定位精度的同时有效改善测速精度. 组合后在车载动态中定位精度影响为mm级,而速度误差改善在北、东、地方向达到了75.8％、79.5％、66.7％. 此外,在BDS+INS紧组合中使用双频数据可以改善测速定姿精度,速度误差改善为18.2％、33.3％、33.3％,姿态误差改善为41.1％、26.7％、59.0％.      

Design of minimax robust filtering for an integrated GPS/INS system

The problem of navigation systems with uncertain noise is considered. A minimax robust filtering which can minimize the worst performance under noise uncertainties using the game theory is proposed. This new filter is applied to an integrated GPS/INS navigation system. A high dynamics aircraft trajectory is designed to test the new filter. The results show that minimax robust filtering performs better than standard Kalman filtering when noise parameters of an inertial measurement unit change their statistical properties. Received: 21 October 1997 / Accepted: 26 May 1999     

Multisensor integration using neuron computing for land-vehicle navigation

Most of the present navigation sensor integration techniques are based on Kalman-filtering estimation procedures. Although Kalman filtering represents one of the best solutions for multisensor integration, it still has some drawbacks in terms of stability, computation load, immunity to noise effects and observability. Furthermore, Kalman filters perform adequately only under certain predefined dynamic models. Neuron computing, a technology of artificial neural network (ANN), is a powerful tool for solving nonlinear problems that involve mapping input data to output data without having any prior knowledge about the mathematical process involved. This article suggests a multisensor integration approach for fusing data from an inertial navigation system (INS) and differential global positioning system (DGPS) hardware utilizing multilayer feed-forward neural networks with a back propagation learning algorithm. In addition, it addresses the impact of neural network (NN) parameters and random noise on positioning accuracy. Electronic Publication     

State transformation extended Kalman filter for GPS/SINS tightly coupled integration

Extended Kalman filter (EKF) is a widely used estimator for integrated navigation systems, and it works well in general situations. However, in adverse conditions such as partially observable environments and highly dynamic maneuvers, the performance of the traditional EKF-based strap-down inertial navigation system (SINS)/GPS integrated navigation system is easily to be affected by the dynamic changes of the specific force, thus leading to the problem of error covariance inconsistency. Though the inconsistency problem can be overcome to some extent if the system matrix, the states and the error covariance matrix are propagated as fast as possible in the SINS calculation rate, the problem cannot be fully solved. State transformation extended Kalman filter (ST-EKF) mechanization, with a new converted velocity error model for the SINS, is proposed, which can also be used to solve the inconsistency problem. In the ST-EKF, the specific force vector in the system error model is replaced by the nearly constant gravity vector for local navigation. Since the propagation and the updating of the ST-EKF can be executed simultaneously in the updating interval, the computation cost is greatly reduced compared with the traditional EKF. Experiments for the GPS/SINS tightly coupled navigation, including linear vibration Monte Carlo test and an unmanned aerial vehicle flight test, are implemented to evaluate the performance of the proposed ST-EKF. The results show that the proposed ST-EKF has superior performance to the traditional EKF, especially in partially observable situations.