Hybrid GNSS-ToA localization and tracking via cooperative unscented Kalman filter

Hybrid GNSS-Terrestrial Cooperative Positioning Based on Particle Filter

“…This section analyzes the performance of HC-PF in a realistic indoor scenario and compares it with the following state of the art algorithms, hybrid sum-product algorithm over a wireless network (H-SPAWN) [1], hybrid-cooperative UKF (HC-UKF) [2] and hybrid-cooperative weighted least squares (HC-WLS). The last one is an extension to the hybrid scenario of the cooperative WLS presented in [6].…”

“…Since the calculation in (17) is complex, we made the following approximations, similar to [2]. First of all, agents do not share their particle representations, but only a Gaussian approximation thereof.…”

“…On the contrary, in GNSSchallenged environments, such as urban canyons, under dense foliage and indoors, the line-of-sight (LoS) between satellites and the receiver's antenna is often obstructed and GNSSbased localization degrades or fails completely. Therefore, in such environments, hybrid localization methods, which fuses both pseudorange measurements from GNSS satellites and terrestrial-based ranging measurements between peers of a wireless network, have been recently proposed in the literature to improve both availability and positioning accuracy [1], [2]. In particular, [1] presented a cooperative approach based on the belief propagation (BP) algorithm, while [2] applies the unscented Kalman filter (UKF) theory to the hybrid and cooperative positioning problem.…”

“…Therefore, in such environments, hybrid localization methods, which fuses both pseudorange measurements from GNSS satellites and terrestrial-based ranging measurements between peers of a wireless network, have been recently proposed in the literature to improve both availability and positioning accuracy [1], [2]. In particular, [1] presented a cooperative approach based on the belief propagation (BP) algorithm, while [2] applies the unscented Kalman filter (UKF) theory to the hybrid and cooperative positioning problem. Other hybrid approaches exist in the literature such as [3] but they are not cooperative.…”

Robust all-source positioning of UAVs based on belief propagation

“…A big step forward for Kalman-based filtering is the invention of unscented Kalman filtering [10][11][12]. In cooperative positioning [13][14][15][16][17][18][19][20], nodes have not only pseudoranges from navigation satellites but also ranging information with wireless peers. Existing algorithms such as iterative least square and Kalman filters can be extended to cooperative positioning, which leads to cooperative least square and cooperative Kalman filtering algorithm [21].…”