Precise-Point-Positioning Estimations for Recreational Drones Using Optimized Cubature-Extended Kalman Filtering

Abstract

In competing multi-dimensional gaming drones, inherent inaccuracies of the precisepoint-positioning (PPP) measurement of the global navigation satellite system (GNSS) have become rampant, hence, necessitating this work. These inaccuracies, occasioned by system drawbacks such as sudden GPS lock, device misalignment constraints, poor detection and communication signals, all lead to PPP computational complexities. To mitigate the inherent PPP complexities, robust and hybrid accurate continuous-discrete (ACD) cubature-extended Kalman filter (C-EKF) methodology for next-generation GNSS integrated system is corroborated. More precisely, time updates to the state and parameter subvectors were accomplished using the third-degree spherical-radial cubature rule. A testbed deployment of the system was then conducted and investigated using tightly-coupled (i) ring laser gyroscope (RLG) and (ii) micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) based devices to ascertain the PPP cooperative tendencies. Optimized performance comparisons of the proposed hybrid C-EKF over the existing cubature Kalman filter (CKF) and the extended Kalman filter (EKF) schemes with-respect-to (w.r.t) their probabilistic outages, Yaw error-differences and ergodic capacities were demonstrated in situations of inaccurate PPP caused by GNSS distortions.INDEX TERMS Gaming drones, global navigation satellite system (GNSS), inertial measurement unit (IMU), hybrid cubature-extended Kalman filter (C-EKF), precise point positioning (PPP).

Implementasi tapis Kalman pada tongkat adaptif untuk deteksi objek disabilitas netra berbasis LiDAR

“…Penggunaan algoritma tapis Kalman menghasilkan rata-rata akurasi lebih baik 0,8m. Algoritma tapis Kalman juga dikembangkan menjadi Cubature Extended Kalman Filter (C-EKF) dan diaplikasikan untuk mendapatkan titik koordinat dari perangkat GPS [4], [5]. Setiap pembacaan per satu detik menghasilkan perbaikan akurasi sebesar 18%.…”

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

“…Meanwhile, the majorization-minimization technique enhances the accuracy of SLAM estimation in the presence of outliers; known as perceptual aliasing when two different places are inferred as the same [144]. In EKF based SLAM, the required number of computations also increases in a quadratic manner with the number of landmarks [66]. Outlier mitigation leads to mutually consistent expectation maximization of inter-UAV measurements.…”

“…Outliers in visual SLAM can match a UAV to the wrong target. Lack of an outlier detection mechanism makes the system unstable [66].…”

“…EKF is effective when computing resources are limited or the state dimension is relatively small [139]. Another variation known as the unscented Kalman filter (UKF) aims to address challenges related to consistency, filter convergence, and data association in SLAM [66]. The UKF avoids linearization through mean and covariance parameterization [95] and assigns observations to landmarks using maximum likelihood.…”