AAE6102_Assignment2

AAE6102 Satellite Communication and Navigation Assignment 2 Analysis

GNSS Positioning Technologies Analysis

Comparative Analysis of GNSS Methods for Mobile Devices

Method Comparison Table

Method	Accuracy	Convergence	Infrastructure	Best Use Case	Limitations
DGNSS	1-3m	Instant	Reference stations	Urban navigation	Limited range from stations
RTK	cm-level	Instant	Base station (<20km)	AR applications	High power consumption
PPP	dm-level	10-30 min	Global corrections	Remote areas	Slow initialization
PPP-RTK	cm-level	2-5 min	Sparse networks	Precision delivery	Complex integration

Key Features

Differential GNSS (DGNSS)

• ✅ Cost-effective urban positioning
• ✅ 1-3m accuracy in metropolitan areas
• 🚫 Performance degrades beyond 100km from reference

Real-Time Kinematic (RTK)

• ✅ Centimeter-level precision
• ✅ Essential for AR applications
• 🚫 Requires dense base station network

Precise Point Positioning (PPP)

• ✅ Global coverage
• ✅ Works in remote regions
• 🚫 30+ minute convergence time

PPP-RTK

• ✅ Combines PPP and RTK advantages
• ✅ Faster convergence than PPP
• 🚫 High data requirements

Task 2 -- GNSS in Urban Areas

Objective: Improve GNSS positioning performance in urban environments using skymask data.

• Ground Truth:
  • Latitude: 22.3198722°
  • Longitude: 114.209101777778°
  • Altitude: 3.0 m
• Description:
  • This function is a critical component of the postnavigation phase, designed based on the method of Non-Line-of-Sight (NLOS).
  • Applies a sky mask to isolate the sky region from input data (satellite azimuth and elevation angles) collected during navigation.
  • Filters out non-sky elements to produce cleaner and more relevant data for downstream processing.
• Key Features:
  • Masking and filtering of irrelevant environmental components.
  • Enhances the reliability of subsequent computations.
• Role in Workflow:
  • The filtered sky data output from this function is used to inform and refine subsequent tasks—particularly the WLS positioning process in Task 3.

Task 3 -- RAIM

Objective: Develop a classic weighted RAIM algorithm to detect and exclude faulty GNSS measurements.

• File: leastSquarePos.m
• Functionality:
  • Executes Weighted Least Squares (WLS) calculations.
  • Integrates Receiver Autonomous Integrity Monitoring (RAIM) to evaluate and ensure the reliability of positioning results.
• Description:
  • The leastSquarePos.m file optimizes position estimates by leveraging WLS algorithms.
  • Operates on data refined through the chi2_detector process, enabling more accurate and noise-resilient calculations.
  • The inclusion of the RAIM algorithm strengthens system integrity by detecting and mitigating potential positioning errors.
• Key Features:
  • Built-in RAIM for integrity monitoring and error detection.
• Role in Workflow:
  • This function is typically called after sky masking (Task 2), using cleaner input data to enhance accuracy and integrity in the computed navigation solution.

Task 4 -- Challenges of Using LEO Satellites for GNSS Navigation

Low Earth Orbit (LEO) satellites, operating at 500–2,000 km, are pivotal in communication systems like Starlink, offering low latency and high data rates. However, adapting these satellites for Global Navigation Satellite Systems (GNSS) introduces formidable challenges compared to Medium Earth Orbit (MEO) constellations like GPS or Galileo. This essay explores four primary challenges—rapid satellite motion, limited coverage, signal design constraints, and infrastructure demands—using real-life examples to illustrate their impact.

1. Rapid Satellite Motion

LEO satellites travel at 7–8 km/s, resulting in 5–15-minute visibility windows and Doppler shifts exceeding 50 kHz.

• Technical Impact:
  • Signal Lock Challenges: Low-cost GNSS receivers (e.g., MediaTek MT3333) struggle to track rapidly moving satellites.
  • Computational Overhead: Compensating for Doppler shifts increases processing time by 40%, draining smartphone batteries.
  • Comparison with MEO: GPS satellites (20,000 km altitude) move slower, enabling stable signal tracking for agricultural machinery.

2. Limited Coverage and Constellation Size

LEO satellites have smaller coverage footprints (~1,000 km diameter) than MEO satellites (~12,000 km).

• Technical Impact:
  • Constellation Scalability: A global LEO GNSS requires 3,000–5,000 satellites, compared to 24–30 for MEO systems.
  • Orbit Maintenance: Atmospheric drag at LEO reduces satellite lifespans to 5–7 years, necessitating frequent replacements.
  • Comparison with MEO: Galileo’s MEO constellation provided uninterrupted coverage during the 2021 Suez Canal blockage.

Conclusion
LEO satellites face insurmountable hurdles in matching MEO GNSS performance. Rapid motion disrupts signal tracking, limited coverage demands unsustainable constellations, signal designs lack ranging optimization, and infrastructure costs are prohibitive. Until breakthroughs in clock miniaturization, signal redesign, and mega-constellation economics emerge, MEO systems like GPS will remain the backbone of global navigation.