Adaptive Recursive Filter for Narrowband Interference Removal
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Solution Overview
Problem
Broadband RF signals are susceptible to narrowband interference signals, which can increase error rates or jam the signal, and existing systems face challenges in mitigating these interferences due to unknown frequency bands, signal strengths, or numbers of interference signals.
Innovation Solution
An adaptive filter that recursively filters interference signals by generating an autocorrelation signal, calculating a ratio to detect narrowband interference, identifying their frequencies, and filtering them out within specified latency constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recursive filtering is applied to remove narrowband interference signals, then decoding accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary autocorrelation analysis and frequency identification on signal frames before final decoding. By pre-identifying narrowband interference frequencies through autocorrelation peak detection, the system prepares filtered signal versions in advance, allowing the decoder to use clean signals without experiencing time penalties during actual decoding operations.
Solution Approach 2:
The patent divides the broadband signal into multiple signal frames and processes each frame independently through the recursive filtering pipeline. This segmentation allows parallel processing of different time segments, reducing overall processing time while maintaining high decoding accuracy for each segment through individual interference removal.
2Reliability
If adaptive filtering is used to address unknown interference characteristics, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service through automatic interference detection and characterization. The autocorrelation function automatically identifies narrowband interference frequencies by detecting peaks in the autocorrelation sequence, and the system adaptively adjusts filtering parameters based on detected interference characteristics without external intervention. This automation improves reliability while managing complexity through algorithmic self-adjustment rather than manual configuration.
Solution Approach 2:
The patent changes filtering parameters dynamically based on detected interference characteristics. The recursive filter adapts its transfer function parameters according to identified narrowband frequencies, allowing the same hardware to handle diverse interference scenarios. This parameter adaptation improves reliability across different interference conditions while avoiding the need for multiple dedicated filtering systems.
3Object-affected harmful factors
If multiple iterations of recursive filter are applied, then interference removal effectiveness is improved, but computational load increases
Solution Approach 1:
The patent applies partial iterations of the recursive filter based on interference characteristics. For strong narrowband interferers, multiple iterations are performed to ensure complete removal. For weaker or absent interference, fewer iterations are sufficient. This adaptive iteration count removes harmful factors effectively while avoiding unnecessary computational energy expenditure on already-clean signals.
Data Source
AI summary
An adaptive recursive filter is disclosed. The filter includes a filtering device to iteratively apply a recursive filter to signal frames corresponding to portions of a sampled broadband signal. Each iteration of the recursive filter may include generating an autocorrelation signal of an input signal frame, calculating a ratio of a magnitudes of a pair of successive points of the autocorrelation signal, comparing the ratio to a selected signal detection threshold such that a value of the ratio greater than a threshold indicates at least one signal of interest, calculating an energy-weighted average frequency of the input signal frame as a frequency of interest, comparing a spectral energy of the input signal frame at the frequency of interest to spectral energies of surrounding frequencies to identify a frequency of a narrowband interference signal, and filtering the input signal frame at the identified frequency to remove the identified narrowband interference signal.