Satellite-Communication-and-Navigation---Assignment-1

Task 1 – Signal Acquisition

The initial phase of GNSS signal processing is signal acquisition, where Intermediate Frequency (IF) data is analyzed using a GNSS Software-Defined Radio (SDR). The primary goal of this acquisition phase is to identify satellite signals and estimate their coarse Doppler shift and code phase.

The outcomes of this process provide a preliminary evaluation of signal availability, which is crucial for ensuring that satellites can be effectively tracked in the later stages. The acquisition results of the two datasets are shown below.

1. Urban

2. Opensky

As seen above, four satellites can be acquired in the Urban dataset, while five satellites can be acquired in the Opensky dataset.

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Task 2 – Signal Tracking

The tracking phase focuses on adjusting the tracking loop, particularly the Delay-Locked Loop (DLL), to maintain a consistent lock on satellite signals. This involves the use of multiple correlators to create correlation plots, facilitating a detailed assessment of tracking performance.

2.1 Evaluation of Tracking Performance

This section evaluates the tracking performance of various satellites based on:

• Carrier-to-Noise Ratio (C/N₀) – Provides insights into the strength and quality of the signals.
• DLL discriminator outputs – Reflects the accuracy of tracking.

Both metrics are vital for assessing the effectiveness of a GNSS receiver, especially in urban areas where multipath effects and signal obstructions are prevalent.

C/N₀ Performance

Urban Dataset

Opensky Dataset

Correlation between C/N₀ and DLL Performance

Urban Dataset

Opensky Dataset

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2.2 Effects of Urban Interference on Correlation Peaks

Urban environments significantly hinder GNSS tracking due to several factors:

1. Multipath Effects

   • Signals reflecting off buildings create delayed versions that interfere with direct signals.
   • This results in distorted correlation peaks, leading to increased DLL tracking errors.

2. Signal Blockage and Attenuation

   • Obstructions from structures, trees, and tunnels cause abrupt signal drops.
   • Affected signals exhibit erratic DLL outputs, indicating poor tracking stability.

3. Dynamic Signal Variations

   • Moving receivers (e.g., vehicles) cause sudden changes in signal strength, resulting in C/N₀ fluctuations.
   • Satellites with high variability in C/N₀ demonstrate inconsistent tracking accuracy.

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Task 3 – Decoding Navigation Data

After establishing tracking, the navigation message is decoded to extract essential parameters, including ephemeris data. This data provides accurate satellite position and clock information, which is critical for precise positioning.

At least one satellite's data has been successfully decoded, demonstrating the capability to retrieve key orbital parameters necessary for user position estimation.

Satellite (PRN 20) Data

• Satellite Position (X, Y, Z) in meters:
  • X: [150292, -1803699, -2309752, 610660, -2240428, -1005868, 1062324]
  • Y: [25408951, 1657080, 1334504, 2529012, 364135, 2070822, 1905366]
  • Z: [803555, 955353, 412700, -534250, 1375899, -1313148, 1488317]
• Satellite Clock Correction: 0.000366 seconds
• GPS Time: 388458.0076751 seconds

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Task 4 – Estimating Position and Velocity

Pseudorange measurements obtained during the tracking phase are utilized in a Weighted Least Squares (WLS) algorithm to compute the user's position and velocity.

Comparison of Positioning Errors

As seen in the above graphs:

• The maximum error in the Urban area is about 100m, which is much higher than that in open-sky areas (30m).

4.1 Discussion on Multipath Effects

Multipath effects arise when GPS signals reflect off nearby surfaces (such as buildings, water, or terrain) before reaching the receiver. This leads to distortions in pseudorange and Doppler measurements, resulting in positioning errors. Below, we analyze how multipath impacts the WLS solution in both open-sky and urban environments.

Multipath Effects in Open-Sky Conditions

• The error of estimation is minimal.
• Multipath primarily arises from ground reflection.

Multipath Effects in Urban Conditions

• Urban settings introduce significant multipath due to reflections from buildings.
• Pseudorange errors can be substantial, often exceeding tens of meters.
• Doppler shifts may also be influenced, resulting in errors in velocity estimation.
• Positioning accuracy is considerably lower compared to open-sky conditions.

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Task 5 – Positioning with Kalman Filter

To improve positioning accuracy, an Extended Kalman Filter (EKF) is developed using pseudorange and Doppler measurements.

EKF Applied to the Urban Dataset

Besides improving position accuracy, velocity error can also be significantly decreased.

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Conclusion

This document analyzed different stages of GNSS signal processing, including:

• Signal Acquisition – Identifying satellite signals.
• Signal Tracking – Evaluating C/N₀ and DLL performance.
• Navigation Data Decoding – Extracting satellite parameters.
• Position and Velocity Estimation – Evaluating errors in urban vs open-sky conditions.
• Kalman Filtering – Enhancing positioning accuracy.

Key Takeaways

✅ Urban environments introduce significant multipath errors.
✅ Opensky conditions provide better GNSS tracking accuracy.
✅ Kalman Filtering improves position estimation & velocity tracking.

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References

• GNSS SDR Processing Techniques
• Weighted Least Squares (WLS) Algorithm
• Extended Kalman Filter (EKF) for GNSS Applications