Abstract
In modern-day multi-dimensional recreational drones (UAVs), the global navigation satellite system (GNSS) units in- use are commonly fraught with precise-point-positioning (PPP) data errors or inaccuracies, hence, necessitating this work. These data inaccuracies, occasioned by the system’s drawbacks such as sudden GPS lock or jamming, embedded device misalignment, drone’s limited communication coverage, signaling and detection, all contributes to the system’s PPP computation complexity. To mitigate PPP complexity, an intelligent and robust accurate continuous-discrete (ACD) based hybrid cubature-extended Kalman filter (C-EKF) computation model for an integrated GNSS unit is corroborated in this article. More precisely, time updates to the state and parameter sub-vectors for the GNSS unit is accomplished using the third-degree spherical-radial cubature rule. The system’s testbed simulation is then conducted using tightly-coupled units of (i) ring laser gyroscope (RLG) and (ii) micro-electro-mechanical system (MEMS) variants of the inertial measurement unit (IMU) to ascertain the PPP cooperative tendencies. Optimized performance comparisons of the proposed hybrid C-EKF over the existing cubature Kalman filter (CKF) and extended Kalman filter (EKF) models with-respect-to (w.r.t) its probabilistic outages, Yaw error-differences and ergodic capacities are demonstrated and presented.
Highlights
T HE frequent use of the inertial measurement unit (IMU) for industrial and space-based vehicular estimations have gained traction [1]-[6]
We investigated the system’s probabilistic capacity outages, Yaw error-differences and ergodic capacities in dissimilar situations of precise point positioning (PPP) caused by global navigation satellite system (GNSS) distortions and signal-to-noise ratio (SNR) thresholds
2) Testbed Evaluation A recapitulation of our work and its optimal performances were buttressed in the Fig. 8a - Fig. 8f, depicting the deployment, analysis and recorded outputs of the proposed hybrid cubature-extended Kalman filter (C-extended Kalman filter (EKF)) scheme
Summary
T HE frequent use of the inertial measurement unit (IMU) for industrial and space-based vehicular estimations have gained traction [1]-[6]. The loosely-coupled and the tightly-coupled GPS-IMU models exhibits nonlinear constraints with inconsistent system errors as prevalent in all nonlinear systems These are usually resolved using EKF [11]. The loosely-coupled and the tightly-coupled integrated GPSIMU models exhibit nonlinear constraints with inconsistent system errors as prevalent in all nonlinear systems These are usually resolved with the EKF methodology [11]. (CD)-Unscented Kalman Filter: Similar to the CD-EKF scheme, the CD-UKF scheme obtains its predicted state-error and covariance matrix values from [11] as follows: var [χ(t + δ)] ≈ var [χ(t + δ)] + δ(cov [χ(t), f (χ(t), t)] . Key parameters and factors such as the model’s interoperability with the introduced GPS-IMU devices, the signal’s beamforming models, the relative computational complexity and the model’s deployment purposes are all with the proposed hybrid scheme is deployed. Taylor’s 1st-order term for non-linear function f are used for the cross covariance matrix (P2,k) predictions
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