使用复合滤波器实现双差GPS/INS动态组合高精度定位定姿

提出了一种双差载波相位GPS/INS紧密组合的新形式,分析了紧密组合和松散组合的计算形式和特点,进而统一了紧密组合和松散组合的估计状态,并将此紧密组合和松散组合相结合,实现了一个复合滤波器。此滤波器可以根据GPS的实际观测情况动态选择合适的滤波方式,可兼顾滤波器的精度和可靠性。通过处理实测数据,验证了此滤波器在精度和可靠性上都优于任一种单一的组合模式。     

Hybrid derivative-free extended Kalman filter for unknown lever arm estimation in tightly coupled DGPS/INS integration

Differential carrier phase observations from GPS (Global Positioning System) integrated with high-rate sensor measurements, such as those from an inertial navigation system (INS) or an inertial measurement unit (IMU), in a tightly coupled approach can guarantee continuous and precise geo-location information by bridging short outages in GPS and providing a solution even when less than four satellites are visible. However, to be efficient, the integration requires precise knowledge of the lever arm, i.e. the position vector of the GPS antenna relative to the IMU. A previously determined lever arm by direct measurement is not always available in real applications; therefore, an efficient automatic estimation method can be very useful. We propose a new hybrid derivative-free extended Kalman filter for the estimation of the unknown lever arm in tightly coupled GPS/INS integration. The new approach takes advantage of both the linear time propagation of the Kalman filter and the nonlinear measurement propagation of the derivative-free extended Kalman filter. Compared to the unscented Kalman filter, which in recent years is typically used as a superior alternative to the extended Kalman filter for nonlinear estimation, the virtue of the new Kalman filter is equal estimation accuracy at a significantly reduced computational burden. The performance of the new lever arm estimation method is assessed with simulated and real data. Simulations show that the proposed technique can estimate the unknown lever arm correctly provided that maneuvers with attitude changes are performed during initialization. Field test results confirm the effectiveness of the new method.     

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     

New results in airborne vector gravimetry using strapdown INS/DGPS

A method for airborne vector gravimetry has been developed. The method is based on developing the error dynamics equations of the INS in the inertial frame where the INS system errors are estimated in a wave estimator using inertial GPS position as update. Then using the error-corrected INS acceleration and the GPS acceleration in the inertial frame, the gravity disturbance vector is extracted. In the paper, the focus is on the improvement of accuracy for the horizontal components of the airborne gravity vector. This is achieved by using a decoupled model in the wave estimator and decorrelating the gravity disturbance from the INS system errors through the estimation process. The results of this method on the real strapdown INS/DGPS data are promising. The internal accuracy of the horizontal components of the estimated gravity disturbance for repeated airborne lines is comparable with the accuracy of the down component and is about 4–8 mGal. Better accuracy (2–4 mGal) is achieved after applying a wave-number correlation filter (WCF) to the parallel lines of the estimated airborne gravity disturbances.     

Strapdown INS/DGPS airborne gravimetry tests in the Gulf of Mexico

Combining data from a Strapdown Inertial Navigation System and a Differential Global Positioning System (SINS/DGPS) has shown great promise in estimating gravity on moving platforms. Previous studies on a ground-vehicle system obtained 1–3 mGal precision with 2 km spatial resolution. High-accuracy Inertial Measurement Units (IMU) and cm-level positioning solutions are very important in obtaining mGal-level gravity disturbance estimates. However, these ideal configurations are not always available or achievable. Because the noise level in the SINS/DGPS gravimetric system generally decreases with an increase of speed and altitude of the platform, the stringent constraints on the IMU and GPS may be relieved in the airborne scenario. This paper presents an investigation of one navigation-grade and one tactical-grade IMU for the possibility of low-cost INS/GPS airborne gravimetry. We use the data collected during the Gravity-Lidar Study of 2006 (GLS06), which contains aerogravity, GPS, and INS along the northern coastline of the Gulf of Mexico. The gravity disturbance estimates from the navigation-grade IMU show 0.5–3.2 mGal precision compared with the onboard gravimeter’s measurements and better than 3 mGal precision compared with the upward continued surface control data. Due to relatively large (240 s) smoothing window, the results have about 34 km along-track resolution. But the gravity estimates from the tactical-grade IMU have much poorer precisions. Nonetheless, useful contributions from the tactical-grade IMU could be extracted for longer wavelengths.     

高精度差分GPS/INS组合系统中的模糊度处理策略

本文简要介绍了差分GPS/INS组合系统在单历元模式和多历元模式下适用的模糊度基本解算方法,分析了各自的优缺点,并提出了一种综合的GPS/INS组合系统模糊度处理策略。实验表明,在综合两种模式的动态模糊度解算方法的基础上引入基于周跳探测修复的模糊度传递以及INS辅助,对于不良观测条件下的模糊度固定具有十分显著的改善。     

INS／DGPS支持的机载线阵推扫影像几何校正

论述了利用INS／DGPS系统的导航输出计算外方位元素的原理，介绍了行方位数据支持下机载线阵CCD影像直接和间接法几何校正的基本方法，重点分析并解决了直接法纠正中的目标定位、灰度重采样及间接法校正中最佳扫描行的确定问题。PHI高光谱影像的实验结果表明，文中的校正算法可达到优于1．5m的绝对定位精度及0．7m的相对精度。     

Integrated GPS/INS navigation system with dual-rate Kalman Filter

A dual-rate Kalman Filter (DRKF) has been developed to integrate the time-differenced GPS carrier phases and the GPS pseudoranges with INS measurements. The time-differenced GPS carrier phases, which have low noise and millimeter measurement precision, are integrated with INS measurements using a Kalman Filter with high update rates to improve the performance of the integrated system. Since the time-differenced GPS carrier phases are only relative measurements, when integrated with INS, the position error of the integrated system will accumulate over time. Therefore, the GPS pseudoranges are also incorporated into the integrated system using a Kalman Filter with a low update rate to control the accumulation of system errors. Experimental tests have shown that this design, compared to a conventional design using a single Kalman Filter, reduces the coasting error by two-thirds for a medium coasting time of 30?s, and the position, velocity, and attitude errors by at least one-half for a 45-min field navigation experiment.     

基于伪距和多普勒观测值的DGPS/INS紧组合导航算法研究

GPS/INS组合导航具备数据更新率高、抗干扰、能够提供包括姿态在内的全部导航信息等优点,因此在测绘领域得到了广泛应用。但是目前国内GPS/INS组合导航研究主要集中在单GPS接收机与INS的组合模式,针对此问题,本文研究基于伪距和多普勒观测值的DGPS/INS紧组合导航算法,给出组合导航扩展卡尔曼滤波的状态方程和量测方程。实测数据解算表明,该组合模式下的导航精度得到显著提高。     

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.     

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。     

卡尔曼滤波与H∞滤波在INS／GPS组合导航中的应用

组合导航利用惯性导航（INS）和全球定位系统（GPS）较强的非相似性和互补性,将两者组合,可以取长补短,充分发挥各自的优点,提高导航系统性能。利用卡尔曼滤波能够有效提高其精度,但卡尔曼滤波的应用要求函数模型和随机模型已知,符合实际,这在实际应用中是很难保证的,一般都是通过经验信息确定。H滤波则具有很强的鲁棒性,抗干扰性强。通过仿真数据处理,结果表明：H滤波比卡尔曼滤波在噪声特性未知时更适用,精度更高。     

三频BDS/INS紧组合的抗差自适应Kalman滤波

构建了适合北斗三频信号的北斗/惯性紧组合模型。针对三频模糊度解算算法的错误整数解可能对滤波结果造成"污染"这一问题,应用了抗差自适应Kalman滤波算法,优化了所建的模型。车载组合导航实验结果表明:三频模糊度解算算法得到的模糊度正确率平均值为99.84%,直接用于紧组合模型将造成最高达0.7 m的天向位置误差;抗差自适应Kalman滤波算法能消除错误模糊度整数解的影响,东、北、天三个方向的位置最大偏差值在厘米级。此外,载体姿态和速度频繁变化造成的状态预测误差也被自适应处理校正,组合导航的位置均方根误差为东向0.007 m,北向0.014 m,天向0.023 m。研究表明,在三频信号条件下,所采用的抗差自适应Kalman滤波能够增强所构建的紧组合模型的可靠性。     

A JTIDS/INS/DGPS navigation system with pseudorange differential information transmitted over Link-16: design and implementation

As the battle environment becomes more complicated, the demand for higher accuracy and better anti-jam capacity of navigation has been increasing. The conventional JTIDS/INS/GPS integrated navigation cannot meet the demands of certain situations such as precision strike and formation flight. A new system that introduces the differential GPS into JTIDS/INS/GPS integration system is proposed to improve the navigation performance in the modern combined operations. In this system, the differential information of DGPS is transmitted through the communication data link of Link-16. As a result, the system resources are efficiently utilized and the controllability and anti-jam performance of the system are significantly enhanced. A hybrid slot allocation protocol (HSAP) that combines a static slot allocation algorithm and a dynamic slot allocation algorithm and the corresponding source-chosen mechanisms are proposed. The performance of the JTIDS/INS/GPS integration navigation using the differential GPS information from one or multiple base stations is studied and compared with that of the system without using the differential GPS information. Furthermore, the performance of the integration navigation using HSAP is compared with that of the system using static slot allocation algorithm. We show that navigation accuracy based on the differential GPS is improved, and using HSAP also leads to higher localization accuracy.     

GPS／INS组合中观测噪声方差阵的自适应估计方法研究

在GPS/INS松散组合滤波中,首先利用新息辨别异常的观测量,然后根据选权滤波的思想,通过调节滤波器中观测噪声方差阵改变系统对相应观测信息的信赖度.实验结果表明,改进的滤波算法不仅提高了滤波精度,而且较好地控制了异常点的误差影响.     

一种两步自适应抗差Kalman滤波在GPS/INS组合导航中的应用

当GPS观测可用时,如何提高组合导航的可靠性、连续性以及导航精度是组合导航重要的研究主题。针对伪距、伪距率紧组合导航精度低、姿态角误差修正不精确的缺点,本文从参数可观测性角度提出一种两步自适应Kalman滤波算法。首先简单介绍了紧组合Kalman滤波的过程,然后给出了两步自适应抗差滤波的公式和具体步骤,并且进行了分析和比较。最后用实测算例对提出的算法进行验证。结果表明,相比较于伪距、伪距率紧组合Kalman滤波,两步自适应抗差滤波能够控制动态扰动异常和观测异常的影响;导航精度不会随着组合周期的增长、INS惯性元件误差的增大而降低;在惯性元件误差较大的情形下也能够很好地估计元件误差,提高姿态角精度。     

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.     

利用随机系数矩阵的GNSS/INS组合导航Kalman滤波算法

在动力学模型可靠的情况下,为避免观测异常对滤波结果的影响,建立处理观测异常的观测模型集合,以观测模型集合中系数矩阵的期望来代替观测方程的系数矩阵,利用随机系数矩阵Kalman滤波算法来控制观测信息异常的影响。算例结果表明,该算法可以有效地控制观测值异常对滤波结果的影响。     

改进的UKF在GPS/INS组合导航中的应用

在GPS/INS组合导航中,传统UKF(Unscented Kalman Filter)计算量大,无法满足实时性要求。而且当动力学模型受到异常扰动误差影响时,其精度与稳定性易受到影响。针对以上问题,利用最小偏度单形采样策略降低UKF计算量以提高精度;通过自适应调整过程噪声以降低动态异常扰动误差对UKF精度与稳定性的影响。由此提出了一种改进UKF算法,用于GPS/INS组合导航。仿真实验结果表明,改进UKF算法用于GPS/INS组合导航的精度要优于UKF算法。