Abstract
The advances in low-latency communications networks and the ever-growing amount of devices offering localization and navigation capabilities opened a number of opportunities to develop innovative network-based collaborative solutions to satisfy the increasing demand for positioning accuracy and precision. Recent research works indeed, have fostered the concept of networked Global Navigation Satellite System (GNSS) receivers supporting the sharing of raw measurements with other receivers within the same network. Such measurements (i.e. pseudorange and Doppler) can be processed through Differential GNSS (DGNSS) techniques to retrieve inter-agent distances which can be in turn integrated to improve positioning performance. This article investigates an improved Bayesian estimation algorithm for a sensorless, tight-integration of DGNSS-based collaborative measurements through a modified Particle Filter (PF), namely Cognitive PF. Differently from Extended Kalman Filter and Uscented Kalman Filter indeed, a PF natively support the non-Gaussian noise distribution which characterizes DGNSS-based inter-agent distances. The proposed Cognitive PF is hence designed, implemented and optimized according to the architecture of a proprietary Inertial Navigation System (INS)-free Global Navigation Satellite System (GNSS) software receiver. Experimental tests performed through realistic radio-frequency GNSS signals showed a remarkable improvement in positioning accuracy w.r.t. reference PF and EKF architectures.
Highlights
Global Navigation Satellite Systems (GNSSs) are exploited nowadays in a wide range of applications with the aim of providing positioning and navigation capabilities to a growing number of devices [1], [2]
Given that the stratified resampling algorithm has a negligible impact on the positioning accuracy, this section only includes the improvement provided by the implementation of adaptive covariance and adaptive likelihood characterizing the proposed Cognitive Particle Filter (PF)
When GNSS-only Collaborative Positioning (CP) is considered, PF can replace other Bayesian estimation filters such as EKF/Unscented Kalman Filter (UKF) if a parallel architecture can be implemented to compensate for the increased complexity of the sequential filter [30]
Summary
Global Navigation Satellite Systems (GNSSs) are exploited nowadays in a wide range of applications with the aim of providing positioning and navigation capabilities to a growing number of devices [1], [2]. Several recent studies on CP have been focused on ranging sensors and technologies such as Ultra-Wide Band (UWB), LiDAR, ultrasound [18], [19], while few relevant literature contributions approached inter-vehicular range estimation through Differential GNSS (DGNSS) methods [20]–[24]. The latter is expected to bring information about the positioning problem according to the statistics of the noise carried by the set of input measurements and to the relative spatial geometry of the involved receivers [11], [25].
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