一种基于到达时间差的卡尔曼滤波定位算法

针对超宽带室内定位技术在复杂室内环境下易受到非视距误差影响造成定位精度下降的问题,基于到达时间差（TDOA）定位模型,对Chan算法和泰勒（Taylor）算法进行联合,并引入了无迹卡尔曼（UKF）滤波算法来进一步提高标签的定位精度,建立了改进的Chan-Taylor-SVDUKF联合定位算法。主要体现在：(1)采用双边双向测距（DSTWR）的方法来获取TDOA值;(2)采用改进的Chan-Taylor联合算法对受到非视距（NLOS）影响的观测值进行筛除;(3)针对TDOA模型中状态方程为线性的情况,用一步预测和一步预测协方差代替UKF中对状态方程进行的无迹（UT）变换;(4)针对UT变换中采用平方根法（Cholesky）分解易分解失败,采用奇异值分解代替Cholesky分解。实验结果证明,Chan-Taylor-SVDUKF算法提高了定位精度和稳定性。     

附加运动学约束的BDS抗差UKF导航算法

针对城市复杂环境中单一BDS导航受多路径（multipath,MP）和非视距（non-line-of-sight,NLOS）信号干扰导致精度下降的问题,提出一种附加运动学约束的抗差无迹卡尔曼滤波（unscented Kalman filter,UKF）算法。该算法基于新息向量构造等价权函数,克服了位置及接收机钟差初值不准确引起的抗差性能下降问题。同时,利用载体的近似运动方向和高程约束,进一步增强滤波解。实测车载试验结果表明,本文方法可有效抑制MP和NLOS信号的干扰,提高城市环境中的BDS导航精度。     

GPS/MEMS INS integrated system for navigation in urban areas

This paper evaluates the performance of a tightly coupled GPS/INS integrated system based on low cost MEMS IMUs in dense urban areas, and investigates two different methods to improve its performance. The first method used is to derive observations from two different constraint equations reflecting the behavior of a typical land vehicle. The first constraint equation is derived assuming that the vehicle does not slip and always remains in contact with the ground. If these assumptions are true the velocity of the vehicle in the plane perpendicular to the forward direction should be zero. The second constraint equation is derived from the fact that the height does not change much in a short time interval in a land vehicular environment. Thus, when a GPS outage occurs (partial/complete), the integrated system combines the INS and constraints-derived virtual measurements to keep the position and velocity errors bounded. This method is suitable for use in real-time applications. The second method is specifically suitable for a post-mission application and involves the use of Rauch-Tung-Striebel (RTS) smoother. The designed system performance is evaluated using two data sets collected in dense urban areas. The obtained results demonstrate the effectiveness of different algorithms considered, in controlling the INS error growth, and indicates the potential of MEMS IMUs for use in land vehicle navigation applications.     

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.     

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.     

神经网络辅助的GPS/INS组合导航自适应UKF算法

GPS/INS组合导航非线性系统最优估计算法中,基于统计信息和假设检验理论的多渐消因子自适应滤波算法的应用前提条件是残差向量为高斯白噪声。本文针对观测异常会影响残差向量的数字特性分布,提出了一种神经网络辅助的多重渐消因子自适应SVD-UKF算法。该算法采用神经网络算法削弱观测异常对残差序列高斯白噪声分布特性的影响,利用奇异值分解抑制UKF中先验协方差矩阵负定性变化,同时构造多重渐消因子对预测状态协方差阵进行调整,使得不同的滤波通道具有不同的调节能力,高效地应用于多变量复杂系统。最后利用车载实测数据进行了验证。结果表明,神经网络算法极大削弱了观测粗差对残差序列高斯白噪声分布特性的影响,拓展了多重渐消因子的应用范围,使其能在观测值含有粗差的条件下自适应调节不同滤波通道,消除滤波状态中的异常,提高组合导航解的精度和可靠性。     

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.     

Design of a low-cost attitude determination GPS/INS integrated navigation system

Due to their complementary features of GPS and INS, the GPS/INS integrated navigation system is increasingly being used for a variety of commercial and military applications. An attitude determination GPS (ADGPS) receiver, with multiple antennas, can be more effectively integrated with a low-cost IMU since the receiver gives not only position and velocity data but also attitude data. This paper proposes a low-cost attitude determination GPS/INS integrated navigation system. The proposed navigation system comprises an ADGPS receiver, a navigation computer unit (NCU), and a low-cost commercial MEMS IMU. The navigation software includes a fault detection and isolation (FDI) algorithm for integrity. In order to evaluate the performance of the proposed navigation system, two flight tests have been performed using a small aircraft. The first flight test confirmed the fundamental operation of the proposed navigation system and the effectiveness of the FDI algorithm. The second flight test evaluated the performance of the proposed navigation system and demonstrated the benefit of GPS attitude information in a high dynamic environment. The flight test results show that the proposed ADGPS/INS integrated navigation unit gives reliable navigation performance even when anomalous GPS data is provided and gives better navigation performance than a conventional GPS/INS unit.     

Reliable partial ambiguity resolution for single-frequency GPS/BDS and INS integration

GPS Solutions - Correctly fixing carrier phase integer ambiguities is a prerequisite to achieve high-precision positioning solutions from global navigation satellite system (GNSS). However, for the...     

一种利用BDS/INS的杆塔姿态测量算法

基于姿态信息是状态监测的关键参数这一特征,提出一种北斗卫星导航系统(BeiDou navigation satellite sys-tem,BDS)和惯性导航系统(inertial navigation system,INS)融合的杆塔姿态测量算法.由惯性传感器解算输出姿态信息得出基线向量搜索范围,提高了整周模糊度的求...     

A combined algorithm of improving INS error modeling and sensor measurements for accurate INS/GPS navigation

Although the integrated system of a differential global positioning system (DGPS) and an inertial navigation system (INS) had been widely used in many geodetic navigation applications, it has sometimes a major limitation. This limitation is associated with the frequent occurrence of DGPS outages caused by GPS signal blockages in certain situations (urban areas, high trees, tunnels, etc.). In the standard mechanization of INS/DGPS navigation, the DGPS is used for positioning while the INS is used for attitude determination. In case of GPS signal blockages, positioning is provided using the INS instead of the GPS until satellite signals are obtained again with sufficient accuracy. Since the INS has a very short-time accuracy, the accuracy of the provided INS navigation parameters during these periods decreases with time. However, the obtained accuracy in these cases is totally dependent on the INS error model and on the quality of the INS sensor data. Therefore, enhanced navigation parameters could be obtained during DGPS outages if better inertial error models are implemented and better quality inertial measurements are used. In this paper, it will be shown that better INS error models are obtained using autoregressive processes for modeling inertial sensor errors instead of Gauss–Markov processes that are implemented in most of the current inertial systems and, on the other hand, that the quality of inertial data is improved using wavelet multi-resolution techniques. The above two methods are discussed and then a combined algorithm of both techniques is applied. The performance of each method as well as of the combined algorithm is analyzed using land-vehicle INS/DGPS data with induced DGPS outage periods. In addition to the considerable navigation accuracy improvement obtained from each single method, the results showed that the combined algorithm is better than both methods by more than 30%.