Abstract:Traditionally, GNSS receiver autonomous integrity monitoring (RAIM) has been based upon single epoch solutions. RAIM can be improved considerably when available dynamic information is fused together with the GNSS range measurements in a Kalman filter. However, while the Kalman filtering technique is widely accepted to provide optimal estimates for the navigation parameters of a dynamic platform, assuming the state and observation models are correct, it is still susceptible to unmodelled errors. Furthermore, si… Show more
“…Therefore, to obtain the optimal solution, the reasonable stochastic model should be determined in real-time (Wang 2000;Moore andWang 2003, Hewitson andWang 2007;Geng and Wang 2008;Yang and Gao 2005). In this paper, without affecting the validity and efficiency of our WRA, for simplification, the covariance matrix Σ w was selected as a (6 × 6) matrix with spectral density of 0·2m 2 s − 3 as follows (Schwarz et al, 1989):…”
Section: Z E B O Z H O U a N D O T H E R S Vol 66mentioning
In kinematic Global Navigation Satellite Systems (GNSS) navigation, the Kalman Filter (KF) solution relies, to a great extent, on the quality of the dynamic model that describes the moving object's motion behaviour. However, it is rather difficult to establish a precise dynamic model that only connects the previous state and the current state, since these high-order quantities are usually unavailable in GNSS navigation receivers. To overcome such limitations, the Window-Recursive Approach (WRA) that employs the previous multiple states to predict the current one was developed in Zhou et al., (2010). Its essence is to adaptively fit the moving object's motion behaviour using the multiple historical states in a short time span. Up to now, the WRA method has been performed only using GNSS pseudorange measurements. However, in GNSS navigation fields, the strength of pseudorange observation model is usually weak due to various reasons, e.g., multi-path delay, outliers, insufficient visible satellites. As an important complementary measurement, Doppler can be used to aid Position and Velocity (PV) estimation. In this contribution, implementation of WRA will be developed using the pseudorange and Doppler measurements. Its corresponding state transition matrix is constructed based on the Newton's Forward Difference Extrapolation (NFDE) and Definite Integral (DI) methods for the efficient computation. The new implementation of WRA is evaluated using the real kinematic vehicular GNSS data with two sampling rates. The results show that: (ii) In high sampling rate, the WRA works best in the case of 2 epochs in time window, while in the low sampling rate, it obtains better solutions if more epochs involved in time window.(iii) Compared with KF with constant velocity dynamic model, the WRA demonstrates better in the self-adaptation and validity.(iv) As a benefit of WRA itself, the NFDE/DI-based state transition matrix for WRA can be previously computed offline without increasing the computation burdens.
“…The three gyroscope and three accelerometer bias states are modeled by first order Gauss-Markov processes and the receiver clock error is modeled by a two-state ͑bias and drift͒ random process model ͑Huddle 1983; Pue 2003͒. The KF state vector has 17 elements, including position, velocity, attitude errors, accelerometer and gyroscope biases, and receiver clock bias and drift, plus 12 or more GNSS range bias states ͑Hewitson 2006; Hewitson and Wang 2007͒. Regardless of the states and error model employed, the state evolution model of the discrete EKF is described by…”
The integration of globe navigation satellite system ͑GNSS͒ with inertial navigation system ͑INS͒ is being heavily investigated as it can deliver more robust and reliable systems than either of the individual systems. In order to ensure the integrity of navigation solutions, it is necessary to incorporate an effective quality control scheme which uses redundant information provided by both the measurement and dynamic models. As the GNSS receiver autonomous integrity monitoring ͑RAIM͒ algorithms are well developed, here they are adapted to integrated GNSS/INS systems referred as extended RAIM ͑eRAIM͒, which are derived from the least-squares estimators of the state parameters in a Kalman filter, to assess GNSS/INS performance for a tightly coupled scenario. In addition to the RAIM capabilities, eRAIM procedures are able to detect faults in the dynamic model and isolate them from the measurement model. The analysis includes outlier detection and identification capabilities, reliability and separability measures of integrated GNSS/INS systems. The performance of the system is also investigated with respect to diminishing satellite visibility conditions.
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