SearcharxivSearch

arXiv subjects

Shuguo Pan

Publications and source records attributed to Shuguo Pan.

2 recordsLinked to original sources

Dual-Satellite Doppler Accuracy Prediction and Geometry Selection for Sparse LEO Signals of Opportunity

Low Earth Orbit (LEO) satellites have emerged as a promising complement to GNSS for positioning in signal challenged environments. In sparse LEO signals of opportunity scenarios, Doppler positioning often relies on only one or two satellite passes, making positioning accuracy highly dependent on pass geometry. This paper investigates dual satellite LEO Doppler accuracy prediction and geometry selection. A single pass Doppler accuracy model based on the Doppler Dilution of Precision (DDOP) framework is first validated using real Iridium measurements. An information domain fusion model is then developed to combine the effective position information from two satellite passes while accounting for pass specific clock parameters. Based on this model, an analytical relationship between the intersection angle of the two predicted error ellipses and the fused positioning accuracy is derived and verified through simulations. Long term ORBCOMM observations are further used to evaluate the practical availability of favorable satellite pairs. Results show that an intersection angle of about 20{\deg} is sufficient to achieve approximately 50 m theoretical positioning accuracy, and that such complementary satellite pairs are typically available within about 30 min. These results provide practical guidance for geometry aware satellite selection and observation scheduling in sparse LEO Doppler positioning.

eess.SP

A Preliminary Exploration of the Differences and Conjunction of Traditional PNT and Brain-inspired PNT

Developing universal Positioning, Navigation, and Timing (PNT) is our enduring goal. Today's complex environments demand PNT that is more resilient, energy-efficient and cognitively capable. This paper asks how we can endow unmanned systems with brain-inspired spatial cognition navigation while exploiting the high precision of machine PNT to advance universal PNT. We provide a new perspective and roadmap for shifting PNT from "tool-oriented" to "cognition-driven". Contributions: (1) multi-level dissection of differences among traditional PNT, biological brain PNT and brain-inspired PNT; (2) a four-layer (observation-capability-decision-hardware) fusion framework that unites numerical precision and brain-inspired intelligence; (3) forward-looking recommendations for future development of brain-inspired PNT.

cs.RO