Ultra-tight GPS/IMU Integration based Long-Range Rocket Projectile Navigation

Handong, Zhao; Li, Zhipeng

doi:10.14429/dsj.66.8326

Cited by 6 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, GNSS receivers provide accurate long-term position information at a significantly lower frequency (∼10 Hz), but can be easily spoofed and jammed [ 5 , 6 , 7 ]. Due to their evident complementarity, IMUs and GNSS are classically fused by different types of Kalman filters for trajectory estimation, as in [ 2 , 3 , 4 ].…”

Section: Related Workmentioning

confidence: 99%

“…Classically, IMU and GNSS measurements are combined with Kalman Filters to estimate a trajectory. The IMU measurements are integrated to predict the trajectory in order to be corrected by the GNSS receiver measurements [ 1 , 2 , 3 , 4 ]. Nevertheless, GNSS signals are not always available due to the environment configuration and are vulnerable to jamming and spoofing [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

LSTM-Based Projectile Trajectory Estimation in a GNSS-Denied Environment

Roux

Changey

Weber

et al. 2023

Sensors

View full text Add to dashboard Cite

This paper presents a deep learning approach to estimate a projectile trajectory in a GNSS-denied environment. For this purpose, Long-Short-Term-Memories (LSTMs) are trained on projectile fire simulations. The network inputs are the embedded Inertial Measurement Unit (IMU) data, the magnetic field reference, flight parameters specific to the projectile and a time vector. This paper focuses on the influence of LSTM input data pre-processing, i.e., normalization and navigation frame rotation, leading to rescale 3D projectile data over similar variation ranges. In addition, the effect of the sensor error model on the estimation accuracy is analyzed. LSTM estimates are compared to a classical Dead-Reckoning algorithm, and the estimation accuracy is evaluated via multiple error criteria and the position errors at the impact point. Results, presented for a finned projectile, clearly show the Artificial Intelligence (AI) contribution, especially for the projectile position and velocity estimations. Indeed, the LSTM estimation errors are reduced compared to a classical navigation algorithm as well as to GNSS-guided finned projectiles.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LSTM-Based Projectile Trajectory Estimation in a GNSS-Denied Environment

Roux

Changey

Weber

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The CKF algorithm includes two processes: the time update and the measurement update [ 29 ]. The time updating is as follows: The posteriori probability distribution of a given k − 1 moment is assumed to be:

Let:

Calculating state volume points:

Volume points transformed based on the state equation:

Weighted mean to compute the state quantity prediction value:

Calculate the covariance matrix of state prediction:

…”

Section: Ckf Algorithmmentioning

confidence: 99%

Robust Adaptive Cubature Kalman Filter and Its Application to Ultra-Tightly Coupled SINS/GPS Navigation System

Zhao

Yan

et al. 2018

Sensors

View full text Add to dashboard Cite

In this paper, we propose a robust adaptive cubature Kalman filter (CKF) to deal with the problem of an inaccurately known system model and noise statistics. In order to overcome the kinematic model error, we introduce an adaptive factor to adjust the covariance matrix of state prediction, and process the influence introduced by dynamic disturbance error. Aiming at overcoming the abnormality error, we propose the robust estimation theory to adjust the CKF algorithm online. The proposed adaptive CKF can detect the degree of gross error and subsequently process it, so the influence produced by the abnormality error can be solved. The paper also studies a typical application system for the proposed method, which is the ultra-tightly coupled navigation system of a hypersonic vehicle. Highly dynamical scene experimental results show that the proposed method can effectively process errors aroused by the abnormality data and inaccurate model, and has better tracking performance than UKF and CKF tracking methods. Simultaneously, the proposed method is superior to the tracing method based on a single-modulating loop in the tracking performance. Thus, the stable and high-precision tracking for GPS satellite signals are preferably achieved and the applicability of the system is promoted under the circumstance of high dynamics and weak signals. The effectiveness of the proposed method is verified by a highly dynamical scene experiment.

show abstract

“…However, accurate measurement of the attitude has been a major challenge for the last several years due to high rotational speeds and highly dynamic characteristics. With the development of microelectro-mechanical sensors (MEMS), microsensor systems such as solar sensor, inertial measurement unit (IMU), and magnetometer could be applied in a wide range of applications [1][2][3][4], significantly advancing the research on attitude measurement. However, measurement systems consisting of a single type of sensor have limitations.…”

Section: Introductionmentioning

confidence: 99%

Combinatory Attitude Determination Method for High Rotational Speed Rigid-Body Aircraft

Wang

Liu

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this paper, we present a novel multisensor combinatory attitude determination method that enables high-accuracy measurement of the attitude of a high rotational speed rigid-body aircraft. We analyze the external moments of the aircraft during flight and develop the method using theoretical deductions based on the motion equations of a rigid body rotating around the centroid. The proposed method fuses the data measured from GPS, gyrometer, and magnetometer and uses the improved unscented Kalman filter (UKF) algorithm to perform filtering. First, appropriate assumptions and simplifying approximations are made for around-centroid motion equations of a rigid body according to the motion characteristics of the high rotational speed aircraft. Using these assumptions and approximations, the constraint equations between the Euler attitude angles and flight-path angle, trajectory deflection angle are derived to serve as the state equation. Second, the roll angle with error is calculated using the geomagnetic field model and the geomagnetic intensity measured by a three-axis magnetometer and then fused with the angular velocity information obtained from the gyroscope for constructing the measurement equations. Finally, the state equations are discretized using the Runge–Kutta method during the UKF prediction stage, improving the prediction accuracy. Simulation results show that the proposed method can effectively determine the attitude information of the high rotational speed aircraft, achieving high level of reliability and accuracy thanks to the combination of information from GPS, gyroscope, and magnetometer.

show abstract

Ultra-tight GPS/IMU Integration based Long-Range Rocket Projectile Navigation

Cited by 6 publications

References 12 publications

LSTM-Based Projectile Trajectory Estimation in a GNSS-Denied Environment

LSTM-Based Projectile Trajectory Estimation in a GNSS-Denied Environment

Robust Adaptive Cubature Kalman Filter and Its Application to Ultra-Tightly Coupled SINS/GPS Navigation System

Combinatory Attitude Determination Method for High Rotational Speed Rigid-Body Aircraft

Contact Info

Product

Resources

About