A Gyroless Algorithm with Multi-Hypothesis Initialization for Projectile Navigation

Abstract

Projectiles are subjected to a high acceleration shock at launch (20,000 g and higher) and can spin very fast. Thus, the components of onboard navigation units must therefore withstand such constraints in addition to being inexpensive. This makes only a few inertial sensors suitable for projectiles navigation. Particularly, rate gyroscopes which are gun-hardened and have an appropriate operating range are not widely available. On the other hand, magneto-resistive sensors are inexpensive and can satisfy both gun-hardening and operating range requirements, making them an alternative for angular estimation in guided projectiles. This paper presents a gyroless navigation algorithm for projectiles. The lack of gyroscope is handled by the usage of attitude kinematics computed over past attitude estimates of the filter, coupled with a measurement model based on magnetometer and GPS observations of the attitude. The observability of the attitude when considering non-calibrated magnetometers and its dependency on the initialization is addressed. Then, to cope with the initialization dependency of the filter, we proposed a multi-hypothesis initialization algorithm. In terms of performance, the algorithm is shown to provide a high-rate navigation solution with an interesting performance.

The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications

“…In practice, those errors can be calibrated [ 21 , 22 , 23 , 24 ] and the residuals will add to the sensors bias and scale factor errors. A model that includes the magnetometer bias and scale factor in the filter can be found in [ 25 ].…”

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

“…This algorithm integrates gyrometer and accelerometer a measurements to estimate at each discrete time k : with the rotation matrix from the sensor frame s to the local navigation frame n , the constant gravity vector, and , respectively, the projectile position and velocity, and the skew matrix. This algorithm is generally used for Kalman filters for the prediction step to estimate trajectory, as presented in [ 2 , 3 , 4 , 8 , 24 , 40 ].…”

“…Projectile navigation requires sensors able to resist to extreme conditions (acceleration shocks around 50,000 g along the longitudinal axis, high rotation rates around 15,000 rpm for a 155 mm shell) as well as to be relatively small and inexpensive due to the space and cost constraints imposed on projectiles [ 22 , 23 , 24 ]. For this purpose, projectile navigation mainly uses IMUs (Inertial Measurement Units) composed by accelerometers, gyrometers, and magnetometers as well as GNSS (Global navigation satellite system) data.…”