Nonholonomic constraint (NHC) and odometer speed have been proven to significantly improve the navigation accuracy of a global navigation satellite system (GNSS)-aided inertial navigation system (INS) for land vehicular applications. Exploiting the full potential of the NHC and odometer aids requires the inertial measurement unit (IMU) mounting angles, i.e., angular misalignment with respect to the host vehicle, to be precisely known. We address the accurate estimation of the IMU mounting angles through an aided dead reckoning (DR) approach. In this method, DR using the GNSS/INS integrated attitude and distance traveled is fused with the GNSS/INS integrated position through a straightforward Kalman filter. Simulation and field tests are carried out to validate the proposed algorithm for different grade IMUs, including typical navigation-grade, tactical-grade and low-cost IMUs. The results demonstrate that the pitch and heading mounting angles can be estimated with a comparable accuracy with the GNSS/INS attitude solution, for example, 0.001° accuracy can be achieved for a navigation-grade GNSS/INS integrated system. The roll mounting angle can not be estimated due to lack of observability in this approach, and the heading mounting angle estimation may be influenced by the GNSS/INS heading accuracy drift to some extent for the low-cost IMUs.

GNSS/INS relative accuracy, which characterizes error variation or instability on different time/spatial scales, is increasingly required in some new applications, such as precise surveying and mapping, especially to represent the details of error variation over the short term for precise relative measurement. The INS, as an important GNSS/INS systems component, can provide high navigation accuracy over the short term, but INS navigation accuracy decreases with time due to IMU error sources, which might have a great influence on the GNSS/INS relative accuracy. This paper focuses on the impact assessment of IMU errors on GNSS/INS relative accuracy and proposes a hybrid simulation scheme based on signal grafting to analyze the impact of each IMU error on the relative accuracy. Allan variance, as one of the evaluation methods of GNSS/INS relative accuracy, is applied. The results show that the white noise of the gyroscope and accelerometer is the major factor affecting the GNSS/INS short-term accuracy, and the backward smoothing solution can further reduce the impact of nonwhite noise. This work can provide a reference for sensor selection and facilitate the use of low-end IMUs in applications with high demand for short-term relative accuracy.

As a common land vehicle motion constraint used in GNSS/INS integration system, the non-holonomic constraint (NHC) can effectively inhibit the navigation divergence of standalone inertial navigation system (INS) especially for micro-electro-mechanical system (MEMS) during GNSS signal outages. The correct utilization of non-holonomic constraint requires a compensation of lever arm between the IMU center and the valid point of this constraint on the vehicle. Non-holonomic constraint is not always valid for anywhere on the vehicle since rotation of the vehicle may produce a corresponding lateral/vertical velocity in motion. However, the lever arm impact to non-holonomic constraint has not been paid attention sufficiently and been researched thoroughly. The purpose of this paper is to investigate the effect of the lever arm error on the auxiliary ability of the non-holonomic constraint. The lateral/vertical velocity and additional position drift error during GNSS outages caused by the lever arm error were studied. Two integrated navigation systems with different grade of IMUs were used to analyze the accuracy requirements of the lever arm. Analysis results show that the forward lever arm is the most important influence factor, and the lever arm error should be controlled within sub-decimeter to ensure the accuracy and reliability of the non-holonomic constraint assistance.

Acquiring accurate and reliable pedestrian trajectories is essential for providing indoor location-based services. Although a foot-mounted inertial navigation system (Foot-INS) can acquire pedestrian trajectories in multistory buildings, it will inevitably encounter heading divergence because the constraint information is not always valid. Therefore, we proposed an accurate and convenient postprocessing indoor pedestrian positioning system (IPPS) to acquire pedestrian trajectories in multistory buildings such as shopping malls. Based on the hypotheses that the start and end points of the pedestrian trajectories on a single floor were closed, and the horizontal position of the closing point on each floor was identical. Therefore, in the single floor of multistory buildings, we use the closing point to control the trajectory drift error caused by the Foot-INS, and use a smoothing algorithm to reasonably distribute the drift error to the entire trajectory. Heading divergence is unavoidable with the Foot-INS, result in the pedestrian trajectories acquired on different floors were rotationally offset. Because pedestrian trajectories can epitomize the building orientation and provide an opportunity to align those trajectories on multistory buildings, an algorithm was proposed to match the trajectories acquired on different floors. A hybrid simulation experiment was conducted using an accurate reference object to evaluate the positioning performance of the proposed IPPS. The effectiveness of acquiring pedestrian trajectories was also confirmed by various experimental tests in a large shopping mall. The study findings suggest that the proposed IPPS is self-contained, low cost, and has the potential for large-scale applications.

GNSS is being extensively applied in different navigation applications. However, GNSS direct signals are easily affected by multipath and non-line-of-sight signals, resulting in severe deterioration of positioning. GNSS receiver output information, such as carrier-to-noise ratio (C/N ₀ ) and satellite elevation, cannot accurately reflect the pseudo-range quality, leading to a significant increase in positioning errors. This paper proposes an inertial navigation system (INS) aided GNSS pseudo-range error prediction approach based on machine learning for urban vehicle navigation. As an important feature, the pseudo-range residual estimated by INS is employed for model training, together with the carrier-to-noise ratio, satellite elevation, and pseudo-range rate consistency. The predicted model of the pseudo-range errors is obtained by an ensemble bagging decision tree learning method. Urban vehicle tests show that compared to GNSS single point positioning (SPP) with C/N ₀ -based weighting, the horizontal accuracy in the form of CEP95 of SPP with model-based weighting improves 52.81%, and the GNSS/INS horizontal positioning error in the form of CEP95 is reduced from 21.23m to 5.02m in deep urban environments.