Satellite AIS Receiver Demodulation for Doppler and Packet Collisions
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Solution Overview
Problem
Satellite-based Automatic Identification Systems (AIS) face challenges such as packet collisions, Doppler shift, and reduced signal-to-noise ratio, leading to high packet error rates (PER) and bit error rates (BER), especially in heavy traffic conditions and near the equator.
Innovation Solution
A method involving normalization of signal samples to unity magnitude, followed by estimation and compensation of time and frequency offsets, and the use of a Soft Input Soft Output (SISO) algorithm for improved demodulation, along with post-processing techniques to correct errors, specifically inverting symbol pairs to enhance decoding accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional demodulation methods are used in satellite-based AIS systems, then the system can operate with basic receiver architecture, but packet error rate and bit error rate increase significantly in heavy traffic conditions and equatorial regions
Solution Approach 1:
The patent applies preliminary action by performing normalization of signal samples to unity magnitude before demodulation processing. This pre-processing step ensures that subsequent correlation and detection operations operate on standardized input data, improving reliability in heavy traffic conditions without requiring fundamentally more complex hardware architecture.
Solution Approach 2:
The patent changes the parameter of signal sample magnitude by normalizing all samples to unity magnitude. This parameter transformation simplifies the mathematical operations in the demodulation process and improves the robustness of signal detection, thereby reducing packet error rates while maintaining computational efficiency.
2Reliability
If traditional demodulation methods are used in satellite-based AIS systems, then the system can process signals with standard algorithms, but bit error rate increases in heavy traffic and equatorial regions
Solution Approach 1:
The patent applies preliminary action by performing normalization of signal samples to unity magnitude before demodulation processing. This pre-processing step ensures that subsequent correlation and detection operations operate on standardized input data, improving reliability in heavy traffic conditions without requiring fundamentally more complex hardware architecture.
Solution Approach 2:
The patent changes the parameter of signal sample magnitude by normalizing all samples to unity magnitude. This parameter transformation simplifies the mathematical operations in the demodulation process and improves the robustness of signal detection, thereby reducing packet error rates while maintaining computational efficiency.
3Measurement precision
If signal samples are normalized to unity magnitude, then demodulation accuracy improves and error rates reduce, but processing computational load increases
Solution Approach 1:
The patent changes the parameter of signal sample magnitude by normalizing all samples to unity magnitude. This parameter transformation simplifies the mathematical operations in the demodulation process and improves the robustness of signal detection, thereby reducing packet error rates while maintaining computational efficiency.
Solution Approach 2:
The patent creates a normalized copy of the original signal samples with unity magnitude, which is then used for all subsequent demodulation operations. This copying approach preserves the original signal for potential re-processing while enabling accurate demodulation through the normalized version, optimizing the balance between accuracy and computational efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Significantly reduces PER and BER in satellite-based AIS systems, particularly in heavy traffic and equatorial regions, while also increasing processing efficiency and allowing for more effective pre-detection, detection, and post-processing stages.
Implementation Method 1
normalizing a sequence of samples generated from the received signal to a normalized sequence of samples, wherein an amplitude of each sample of the normalized sequence of samples has an absolute value equal to unity
Implementation Method 2
satellite motion with respect to the emitters induces a significant Doppler shift of the carrier frequency
Implementation Method 3
estimating, on the basis of the normalized sequence of samples, a time offset and a frequency offset of the received signal, and using the estimated time offset and the estimated frequency offset for compensating the normalized sequence of samples for the time and frequency offsets
Data Source
AI summary
A method for demodulating a received signal relating to a sequence of transmitted symbols that have been modulated by continuous phase modulation includes normalizing samples of a sequence of samples generated from the received signal, to obtain a normalized sequence of samples, wherein an amplitude of each sample of the normalized sequence of samples has an absolute value equal to unity; estimating, on the basis of the normalized sequence of samples, a time offset and a frequency offset of the received signal, and using the estimated time offset and the estimated frequency offset for compensating the normalized sequence of samples for the time and frequency offsets, to obtain a compensated sequence of samples; and determining a sequence of symbols corresponding to the transmitted sequence of symbols on the basis of the compensated sequence of samples. Also disclosed is a receiver for demodulating a received signal relating to a sequence of transmitted symbols that have been modulated by continuous phase modulation.