AAE6102 Assignment 1

Satellite Communication and Navigation (2024/25 Semester 2)

The Hong Kong Polytechnic University

Department of Aeronautical and Aviation Engineering

Overview

This assignment focuses on processing GNSS Software-Defined Receiver (SDR) signals to develop a deeper understanding of GNSS signal processing. Students will analyze two real Intermediate Frequency (IF) datasets collected in different environments: open-sky and urban. The urban dataset contains multipath and non-line-of-sight (NLOS) effects, which can degrade positioning accuracy.

Dataset Information

Environment	Carrier Frequency	IF Frequency	Sampling Frequency	Data Format	Ground Truth Coordinates	Data Length	Collection Date (UTC)
Open-Sky	1575.42 MHz	4.58 MHz	58 MHz	8-bit I/Q samples	(22.328444770087565, 114.1713630049711)	90 seconds	14/10/2021 12.21pm
Urban	1575.42 MHz	0 MHz	26 MHz	8-bit I/Q samples	(22.3198722, 114.209101777778)	90 seconds	07/06/2019 04.49am

Task and Solution of Assignment

Task 1

Process the IF data using a GNSS SDR and generate the initial acquisition results.

OpenSky Solution

Urban Solution

These are the acquired of satellites under OpenSky dataset.

Channel	PRN	Frequency	Doppler	Code Offset	Status
1	16	4.57976e+06	-240	31994	T
2	26	4.58192e+06	1917	57754	T
3	31	4.58107e+06	1066	18744	T
4	22	4.58157e+06	1571	55101	T
5	27	4.57678e+06	-3220	8814	T

Acquired Satelliites numbers:

Urban	OpenSky
1	16
3	22
11	26
18	27
	31

Task 2 – Tracking

Adapt the tracking loop (DLL) to generate correlation plots and analyze the tracking performance. Discuss the impact of urban interference on the correlation peaks.

Urban

OpenSky

Impack of urban interfaces

In urban scenario, comparing to the opensky, GNSS antenna is generally blocked by the building in city, which causes a worse tracking quality for satellites.

Task 3 – Navigation Data Decoding

Decode the navigation message and extract key parameters, such as ephemeris data, for at least one satellite. The detailed ephemeris data for the urban dataset is stored in the file eph_urban.mat.

The table below presents some of the factors:

No of PRN	C_ic	Omega_0	C_is	i_0	C_rc	Omega	OmegaDot	IODE_sf3	iDot	idValid	weekNumber	T_GD	IODC	t_oc	TOW
1	-7.45058059692383e-08	-3.10603580061844	1.60187482833862e-07	0.976127704025531	287.468750000000	0.711497598513721	-8.16962601200124e-09	72	-1.81078971237415e-10	[2,0,3]	1032	0	0	453600
3	1.11758708953857e-08	-2.06417843827738	5.21540641784668e-08	0.962858745925880	160.312500000000	0.594974558438532	-7.83246911092014e-09	72	4.81091467962126e-10	[2,0,3]	1032	0	0	453600
11	-3.16649675369263e-07	2.72577037566571	-1.32247805595398e-07	0.909806735685279	324.406250000000	1.89149296226273	-9.30431613354220e-09	83	1.28576784310591e-11	[2,0,3]	1032	0	0	453600
18	-2.53319740295410e-07	3.12182125430595	3.53902578353882e-08	0.954642600078998	280.156250000000	1.39301587576552	-8.61071581373341e-09	56	-1.61792453590826e-10	[2,0,3]	1032	0	0	453600

Task 4 – Position and Velocity Estimation

Using pseudorange measurements from tracking, implement the Weighted Least Squares (WLS) algorithm to compute the user's position and velocity.

• Plot the user position and velocity.
• Compare the results with the ground truth.
• Discuss the impact of multipath effects on the WLS solution. Positon and velocity estimation

The weighted Least Factor is defined under file weightedLeastSquarePos.m

 %--- Initialize variables at the first iteration --------------
for i == 1:
            weights = ones(1,nmbOfSatellites);
else:
    weights(i) = sin(el(i))^2;

OpenSky

Urban

Comparision with Ground Truth

Effect of Multipath

The satellite signals are reflected by narrow buildings, causing significant drift in localization accuracy. In comparison to the Opensky dataset, which provides a reliable solution with ground truth, the Urban dataset exhibits considerable errors in position estimation due to the multipath effects caused by these reflections. This leads to less accurate localization results in urban environments.

Task 5 – Kalman Filter-Based Positioning

Develop an Extended Kalman Filter (EKF) using pseudorange and Doppler measurements to estimate user position and velocity.

EKF on Velocity

EKF on Position