Inertial navigation system

About: Inertial navigation system is a research topic. Over the lifetime, 14582 publications have been published within this topic receiving 190618 citations. The topic is also known as: intertial guidance system & inertial reference platform.

Relative Navigation of Air Vehicles

Abstract: This paper derives the full equations of motion for relative navigation of air vehicles. An extended Kalman filter is used for estimating the relative position and attitude of two air vehicles designated leader and follower. All leader states are assumed known, whereas the relative states are estimated using line-of-sight measurements between the vehicles along with acceleration and angular rate measurements of the follower. Noise is present on all measurements, whereas biases are present only on the latter two. The global attitude is parameterized using a quaternion, whereas the local attitude error is given by a three-dimensional attitude representation. An application of the new theoretical developments is also given, which involves determining an optimal trajectory to improve the estimation accuracy of the system. A cost function is derived based upon the relative position elements of the estimator covariance. State constraints are appended to the cost function using an exponential term. Results show that minimization of this cost function yields a trajectory that improves accuracy of both the position and attitude state estimates.

Closed-Loop Sequential Signal Processing and Open-Loop Batch Processing Approaches for GNSS Receiver Design

Abstract: Global navigation satellite system (GNSS) receiver design is considered in terms of closed-loop and open-loop receiver architectures that utilize sequential and batch processing techniques. The paper uses the Global Positioning System (GPS) as a case study to demonstrate that an open-loop approach that combines batch and sequential signal processing improves GNSS signal tracking characteristics as compared to the traditionally applied closed-loop sequential receiver design for a number of important application areas. Particularly, for flight test scenarios considered, it is demonstrated that open-loop batch/sequential processing improves the GPS tracking margin by 8 dB as compared to the closed-loop sequential tracking for the case of deep GPS/Inertial Navigation System (INS) integration mode that performs code and carrier phase tracking and data bit recovery.

A Hybrid Prediction Method for Bridging GPS Outages in High-Precision POS Application

Abstract: Position and orientation system (POS) is a key technology widely used in remote sensing applications, which integrates inertial navigation system (INS) and GPS using a Kalman filter (KF) to provide high-accuracy position, velocity, and attitude information for remote sensing motion compensation. However, when GPS signal is blocked, the POS accuracy will decrease owing to the unbounded INS error accumulation. To improve the reliability and accuracy of POS, this paper proposes a hybrid prediction method for bridging GPS outages. This method uses radial basis function (RBF) neural network coupled with time series analysis to forecast the measurement update of KF, resulting in reliable performance during GPS outages. In verifying the proposed hybrid prediction method, a flight experiment was conducted in 2011, based on a high-precision Laser POS. Experimental results show that the proposed hybrid prediction method is more effective than two other methods (KF and RBF neural network).

Vehicle Model Aided Inertial Navigation For A Uav Using Low-Cost Sensors

Abstract: This paper considers two different methods of using a dynamic vehicle model in order to aid pose estimates provided by an Inertial Navigation System (INS) for a Un-manned Aerial Vehicle (UAV). We consider low-cost inertial sensors in which errors from sensor noise, bias and scale-factor errors cause a significant growth in pose estimate errors when the navigation system is un-aided by external positioning information such as from GPS or terrain observations. It is shown that an accurate dynamic vehicle model can considerably reduce the error growth in vehicle pose estimates by enhancing the observability of the inertial sensor error sources. Furthermore we analyse the effectiveness of using the vehicle model information given that there are errors in the vehicle model.

A Performance Comparison of Tightly Coupled GPS/INS Navigation Systems Based on Extended and Sigma Point Kalman Filters

Abstract: In this paper, the fusion of GPS pseudorange and deltarange measurements with inertial sensor data is adressed. For many years, extended Kalman filters (EKF) have been applied for this task with great success. However, from a theoretical point of view, the EKF is a sub-optimal choice: The system dynamics model, which is given by the inertial navigation strapdown equations, as well as the pseudorange and deltarange measurement models are nonlinear. The EKF approximates the propagation of Gaussian random vectors through these nonlinear equations by a linear transformation. This allows to capture the variance-covariance matrix of the propagated Gaussian random vectors with first order accuracy only. The family of sigma-point Kalman filters (SPKF) offers an approximation of variance-covariance matrix which is accurate to at least second order. Therefore, the performance of EKF-based and SPKFbased tightly coupled GPS/INS systems is compared in numerical simulations. Different inertial sensor grades from MEMS to FOG and a variety of scenarios are investigated, including situations with less than four satellites in view. Additionally, the simulation results were confirmed by the post-processing of raw GPS and inertial sensor data that was recorded during a test drive. It was found that except for specific situations without practical relevance, EKF and SPKF offer an identical performance. This is due to the fact that for tightly coupled - as well as loosely coupled - GPS/INS integration the higher-order transformation terms are negligible, which is shown analytically.