Comparative Analysis of Fusion Algorithms in a Loosely-Coupled Integrated Navigation System on the Basis of Real Data Processing

Bitar, N. Al; Gavrilov, A. I.

doi:10.1134/s2075108719040023

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…where C n b is the rotation matrix from the body frame (b-frame, Forward-Right-Down) to the navigation frame (n-frame, North-East-Down); f b is the specific force in the b-frame; ω n ie is the angular rate of the earth frame (e-frame) relative to the inertial frame (i-frame) in the n-frame; ω n en is the angular rate of the n-frame relative to the e-frame in the n-frame; v n is the velocity in the n-frame; g n l is the normal gravity in the local position in the n-frame; C e n is the rotation matrix from the n-frame to the e-frame, which represent the geodetic latitude and longitude; h and v D are the ellipsoid height and velocity in the down direction, respectively; ω b ib is the angular rate of the b-frame relative to the i-frame in the b-frame; ω n in is the angular rate of the n-frame relative to the i-frame in the n-frame; (×) is skew symmetric matrix. According to Equation (3), the unsimplified INS speed update can be written as follows: (7) where…”

Section: Ins Dynamic Modelmentioning

confidence: 99%

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Dynamic Adaptive Low Power Adjustment Scheme for Single-Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation in the Complex Urban Environment

et al. 2021

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Positioning accuracy and power consumption are essential performance indicators of integrated navigation and positioning chips. This paper proposes a single-frequency GNSS/MEMS-IMU/odometer real-time high-precision integrated navigation algorithm with dynamic power adaptive adjustment capability in complex environments. It is implemented in a multi-sensor fusion navigation SiP (system in package) chip. The simplified INS algorithm and the simplified Kalman filter algorithm are adopted to reduce the computation load, and the strategy of adaptively adjusting the data rate and selecting the observation information for measurement update in different scenes and motion modes is combined to realize high-precision positioning and low power consumption in complex scenes. The performance of the algorithm is verified by real-time vehicle experiments in a variety of complex urban environments. The results show that the RMS statistical value of the overall positioning error in the entire road section is 0.312 m, and the overall average power consumption is 141 mW, which meets the requirements of real-time integrated navigation for high-precision positioning and low power consumption. It supports single-frequency GNSS/MEMS-IMU/odometer integrated navigation SiP chip in real-time, high-precision, low-power, and small-volume applications.

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Section: Ins Dynamic Modelmentioning

confidence: 99%

“…GNSS and INS have strong complementarity with each other in many aspects such as error characteristics. Combining GNSS and INS can provide more accurate, continuous, and reliable navigation information (including position, velocity, and attitude) than individual GNSS or INS [6,7].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Adaptive Low Power Adjustment Scheme for Single-Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation in the Complex Urban Environment

et al. 2021

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“…. The continuous Markov process described by (27) can be discretised with step Δt and the following random sequence can be obtained…”

Section: Parameter Identification Of First-order Markov Processmentioning

confidence: 99%

“…The solution of the estimation problem can also be simplified by searching for estimates within some limited class, for example, the class of linear estimates [23,24]. Among other procedures used in this field, we should mention unscented transformation (Unscented KF, UKF) [25][26][27] and cubature formulas [28].…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that nonlinearities in state and measurement equations are often represented by polynomials. From this point of view, polynomial filtering methods have been quite successfully used to solve estimation problems [25][26][27][28][29][30][31][32][33][34][35][36][37][38]. In particular, in [24,31], the authors consider problems whose specific feature is nonlinearity of measurements, whereas the equations of the shaping filter that describe the estimated sequence are linear.…”

Section: Introductionmentioning

confidence: 99%

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Efficiency analysis of polynomial filtering algorithms in navigation data processing for a class of nonlinear discrete dynamical systems

Степанов

Vasiliev

Tupysev

et al. 2020

IET Control Theory & Appl

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A polynomial filtering algorithm is designed for estimating a Markov sequence based on linear measurements. The feature of the estimation problem is that the Markov sequence is described by a nonlinear shaping filter, which is a second-order polynomial with respect to the state vector components. The algorithm efficiency is illustrated by three examples of navigation data processing. It is shown that the polynomial filter provides accuracy close to the best potential one calculated using the particle filter. At the same time, the amount of computation required to implement this algorithm is much smaller than that for the particle filter. In addition, the polynomial filter provides a consistent calculated accuracy characteristic.

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A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

Bitar

Gavrilov

2021

Navigation

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Aiming to improve the position and velocity precision of the INS/GNSS system during GNSS outages, a novel system that combines unscented Kalman filter (UKF) and nonlinear autoregressive neural networks with external inputs (NARX) is proposed. The NARX‐based module is utilized to predict the measurement updates of UKF during GNSS outages. A new offline approach for selecting the optimal inputs of NARX networks is suggested and tested. This approach is based on mutual information (MI) theory for identifying the inputs that influence each of the outputs (the measurement updates of UKF) and lag‐space estimation (LSE) for investigating the dependency of these outputs on the past values of the inputs and the outputs. The performance of the proposed system is verified experimentally using a real dataset. The comparison results indicate that the NARX‐aided UKF outperforms other methods that use different input configurations for neural networks.

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Comparative Analysis of Fusion Algorithms in a Loosely-Coupled Integrated Navigation System on the Basis of Real Data Processing

Cited by 18 publications

References 17 publications

Dynamic Adaptive Low Power Adjustment Scheme for Single-Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation in the Complex Urban Environment

Dynamic Adaptive Low Power Adjustment Scheme for Single-Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation in the Complex Urban Environment

Efficiency analysis of polynomial filtering algorithms in navigation data processing for a class of nonlinear discrete dynamical systems

A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

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