Signal Processing Method for Digital TV Equalizer Training
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Solution Overview
Problem
In digital television broadcasting, the alternating transmission of odd and even field SYNC data with inverted pseudo-random sequences complicates the training of equalizer coefficients, and interference from other data segments hinders accurate signal processing.
Innovation Solution
A signal processing method that combines odd and even field SYNC data to neutralize their differences, allowing for improved training of equalizer coefficients or channel estimation by generating a combined SYNC data set that eliminates the need to determine pseudo-random sequence inversion and reduces interference from other data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If odd and even field SYNC data are transmitted alternately with inverted pseudo-random sequences, then data transmission completeness is improved, but the complexity of training equalizer coefficients increases
Solution Approach 1:
The patent combines odd field SYNC data and even field SYNC data by adding them together. Since the pseudo-random sequences are inverted between odd and even fields, their sum neutralizes the inverted sequences, producing a combined SYNC data set free from inversion effects. This merging approach simplifies equalizer training by eliminating the need to detect and handle pseudo-random sequence inversions.
2Productivity
If a single field SYNC data is repeated multiple times for training, then training speed is improved, but interference from other data segments remains
Solution Approach 1:
Instead of repeating a single field SYNC data, the patent merges odd and even field SYNC data through addition. This combination maintains the training functionality while neutralizing the inverted pseudo-random sequences, thereby reducing interference from other data segments that would otherwise contaminate the training process.
Solution Approach 2:
The patent converts the harmful effect of inverted pseudo-random sequences into a beneficial neutralization effect. By adding odd and even field SYNC data together, the inverted sequences cancel each other out, transforming what was previously a source of interference into a mechanism for eliminating interference and improving training accuracy.
3Measurement precision
If pseudo-random sequence inversion is detected before training, then training accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary neutralization by adding odd and even field SYNC data before the training process begins. This preliminary action eliminates the need for subsequent inversion detection, saving processing time while maintaining training accuracy. The neutralized combined SYNC data is then directly used for equalizer training without additional detection steps.
Data Source
AI summary
A signal processing method by adding odd and even field SYNC data for neutralized effects including the steps of receiving an odd field SYNC data of an odd field, which is different at a certain data segment when compared with an even field SYNC data of an even field, and the even field SYNC data of the even field; adding the odd field SYNC data and the even field SYNC data to neutralize the odd and even field SYNC data so as to generate a combined odd and even field SYNC data; and performing a predetermined signal processing on an input signal according to the combined odd and even field SYNC data.