Skip to main content

Ionospheric Scintillation : How it disturbs GPS communication

 

How ionospheric scintillation disturbs GPS communication

Tropospheric lightning causes disturbances in the high atmosphere, particularly in the ionosphere. The ionosphere stretches from 60 to 1000 kilometers above the Earth. Lightning, thunderstorms, and other atmospheric phenomena are common in the troposphere layer of our planet. However, similar tropospheric phenomena are increasingly being found to disrupt GPS signals.

    

What is scintillation

When a molecule or ion enters a medium (which may or may not contain ionized particles), it disturbs the entire medium. It causes chemical reactions with the medium's molecules and ions in plain language. As a result, the electrons in outer orbits leave and move to the upper trajectories of molecules or ions. They try to get to stable states / prior states later. The electrons return to their original orbit, emitting light. This is how scintillation happens.


What is ionospheric scintillation

Ionosphere disturbances are sometimes caused by tropospheric (lower atmospheric layer of the Earth where we dwell) events such as lightning. The ionosphere, as we all know, contains ions. As a result, it is frequently referred to as the ionized atmospheric layer. The ionized structures of the layer's ion particles are disrupted by lighting. We know that electrons in the outer orbit absorb radiation, depart, and shift to the higher orbit. Later, it seeks to find stable states, emits energy, and returns to the previous rotation. A similar effect happens when lightning strikes. When the bottom layer of the ionosphere is disrupted, the ionic structure of that area of the ionosphere is also disturbed. It gradually concerns the entire ionospheric layer, changing the particle density of that layer in particular. It eventually releases the energy and becomes lighted.


How ionospheric scintillation disturbs the GPS signal

GPS satellites are known to fly in a medium earth orbit (MEO) at a height of about 20200 kilometers. As a result, they fly above the ionosphere. The ionospheric layer passes the GPS signal that allows us to navigate while on Earth. If the density of particles or ions in the ionosphere abruptly changes, the signal will become more multipath. The GPS signal will become more scattered in plain English than in typical conditions. Then the GPS signal may be less accurate when using GPS navigation to locate a location on Earth.

Satellites communicate with aircraft flying at very high altitudes. The communication between airplanes and satellites can be disrupted if the charged particle density in the ionosphere changes.


 

Q. Why does wireless communication in the troposphere layer?

A. This question is invalid because communicating satellites fly above the ionospheric layer of the atmosphere. Lower orbit satellites fly around 14,000 kilometers above the Earth's surface. Click here to learn more about ionospheric and terrestrial satellite communication.

People are good at skipping over material they already know!

View Related Topics to







Admin & Author: Salim

profile

  Website: www.salimwireless.com
  Interests: Signal Processing, Telecommunication, 5G Technology, Present & Future Wireless Technologies, Digital Signal Processing, Computer Networks, Millimeter Wave Band Channel, Web Development
  Seeking an opportunity in the Teaching or Electronics & Telecommunication domains.
  Possess M.Tech in Electronic Communication Systems.


Contact Us

Name

Email *

Message *

Popular Posts

MATLAB code for MSK

 Copy the MATLAB Code from here % The code is developed by SalimWireless.com clc; clear; close all; % Define a bit sequence bitSeq = [0, 1, 0, 0, 1, 1, 1, 0, 0, 1]; % Perform MSK modulation [modSignal, timeVec] = modulateMSK(bitSeq, 10, 10, 10000); % Plot the modulated signal subplot(2,1,1); samples = 1:numel(bitSeq); stem(samples, bitSeq); title('Original message signal'); xlabel('Time (s)'); ylabel('Amplitude'); % Plot the modulated signal subplot(2,1,2); samples = 1:10000; plot(samples / 10000, modSignal(1:10000)); title('MSK modulated signal'); xlabel('Time (s)'); ylabel('Amplitude'); % Perform MSK demodulation demodBits = demodMSK(modSignal, 10, 10, 10000); % Function to perform MSK modulation function [signal, timeVec] = modulateMSK(bits, carrierFreq, baudRate, sampleFreq) % Converts a binary bit sequence into an MSK-modulated signal % Inputs: % bits - Binary input sequence % carrierFreq - Carri...

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...

Modulation Constellation Diagrams BER vs. SNR BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ... What is Bit Error Rate (BER)? The abbreviation BER stands for bit error rate, which indicates how many corrupted bits are received (after the demodulation process) compared to the total number of bits sent in a communication process. It is defined as,  In mathematics, BER = (number of bits received in error / total number of transmitted bits)  On the other hand, SNR refers to the signal-to-noise power ratio. For ease of calculation, we commonly convert it to dB or decibels.   What is Signal the signal-to-noise ratio (SNR)? SNR = signal power/noise power (SNR is a ratio of signal power to noise power) SNR (in dB) = 10*log(signal power / noise power) [base 10] For instance, the SNR for a given communication system is 3dB. So, SNR (in ratio) = 10^{SNR (in dB) / 10} = 2 Therefore, in this instance, the s...

Constellation Diagrams of ASK, PSK, and FSK

BASK (Binary ASK) Modulation: Transmits one of two signals: 0 or -√Eb, where Eb​ is the energy per bit. These signals represent binary 0 and 1.    BFSK (Binary FSK) Modulation: Transmits one of two signals: +√Eb​ ( On the y-axis, the phase shift of 90 degrees with respect to the x-axis, which is also termed phase offset ) or √Eb (on x-axis), where Eb​ is the energy per bit. These signals represent binary 0 and 1.  BPSK (Binary PSK) Modulation: Transmits one of two signals: +√Eb​ or -√Eb (they differ by 180 degree phase shift), where Eb​ is the energy per bit. These signals represent binary 0 and 1.    Simulator for BASK, BPSK, and BFSK Constellation Diagrams SNR (dB): 15 Add AWGN Noise Modulation Type BPSK BFSK ...

Fundamentals of Channel Estimation

Channel Estimation Techniques Channel Estimation is an auto-regressive process that may be performed with a number of iterations. There are commonly three types of channel estimation approaches. 1. Pilot estimation  2. Blind estimation  3. Semi-blind estimation. For Channel Estimation,  CIR [↗] is used. The amplitudes of the impulses decrease over time and are not correlated. For example, y(n) = h(n) * x(n) + w(n) where y(n) is the received signal, x(n) is the sent signal, and w(n) is the additive white gaussian noise At the next stage, h(n+1) = a*h(n) + w(n) The channel coefficient will be modified as stated above at the subsequent stage. The scaling factor "a" determines the impulse's amplitude, whereas "h(n+1)" represents the channel coefficient at the following stage. Pilot Estimation Method To understand how a communication medium is currently behaving, a channel estimate is necessary. In order to monitor a channel's behavior in practice communication ...

Comparisons among ASK, PSK, and FSK | And the definitions of each

Modulation ASK, FSK & PSK Constellation MATLAB Simulink MATLAB Code Comparisons among ASK, PSK, and FSK    Comparisons among ASK, PSK, and FSK Comparison among ASK,  FSK, and PSK Performance Comparison: 1. Noise Sensitivity:    - ASK is the most sensitive to noise due to its reliance on amplitude variations.    - PSK is less sensitive to noise compared to ASK.    - FSK is relatively more robust against noise, making it suitable for noisy environments. 2. Bandwidth Efficiency:    - PSK is the most bandwidth-efficient, requiring less bandwidth than FSK for the same data rate.    - FSK requires wider bandwidth compared to PSK.    - ASK's bandwidth efficiency lies between FSK and PSK. Bandwidth Calculator for ASK, FSK, and PSK The baud rate represents the number of symbols transmitted per second Select Modulation Type: ASK...

Difference between AWGN and Rayleigh Fading

Wireless Signal Processing Gaussian and Rayleigh Distribution Difference between AWGN and Rayleigh Fading 1. Introduction Rayleigh fading coefficients and AWGN, or additive white gaussian noise [↗] , are two distinct factors that affect a wireless communication channel. In mathematics, we can express it in that way.  Fig: Rayleigh Fading due to multi-paths Let's explore wireless communication under two common noise scenarios: AWGN (Additive White Gaussian Noise) and Rayleigh fading. y = h*x + n ... (i) Symbol '*' represents convolution. The transmitted signal  x  is multiplied by the channel coefficient or channel impulse response (h)  in the equation above, and the symbol  "n"  stands for the white Gaussian noise that is added to the signal through any type of channel (here, it is a wireless channel or wireless medium). Due to multi-paths the channel impulse response (h) changes. And multi-paths cause Rayleigh fa...

Constellation Diagram of FSK in Detail

  Binary bits '0' and '1' can be mapped to 'j' and '1' to '1', respectively, for Baseband Binary Frequency Shift Keying (BFSK) . Signals are in phase here. These bits can be mapped into baseband representation for a number of uses, including power spectral density (PSD) calculations. For passband BFSK transmission, we can modulate signal 'j' with a lower carrier frequency and signal '1' with a higher carrier frequency while transmitting over a wireless channel. Let's assume we are transmitting carrier signal fc1 for the transmission of binary bit '1' and carrier signal fc2 for the transmission of binary bit '0'. Simulator for 2-FSK Constellation Diagram Simulator for 2-FSK Constellation Diagram SNR (dB): 15 Add AWGN Noise Run Simulation ...

Gaussian minimum shift keying (GMSK)

Dive into the fascinating world of GMSK modulation, where continuous phase modulation and spectral efficiency come together for robust communication systems! Core Process of GMSK Modulation Phase Accumulation (Integration of Filtered Signal) After applying Gaussian filtering to the Non-Return-to-Zero (NRZ) signal, we integrate the smoothed NRZ signal over time to produce a continuous phase signal: θ(t) = ∫ 0 t m filtered (Ï„) dÏ„ This integration is crucial for avoiding abrupt phase transitions, ensuring smooth and continuous phase changes. Phase Modulation The next step involves using the phase signal to modulate a high-frequency carrier wave: s(t) = cos(2Ï€f c t + θ(t)) Here, f c is the carrier frequency, and s(t) represents the continuous-phase modulated carrier wave. Quadrature Modulation (Optional) ...