QR-RLS Adaptive Filter Pipeline for Stable Weak Signal Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
QR-RLS adaptive filters face challenges in wireless communication, including slow computation times, excessive resource usage, divergence due to high S/N ratios, and difficulties in detecting weak signals amidst strong signals, as well as issues with impulse noise and asynchronous spreading codes, which affect the reliability of signal modulation and packet transmission.
Innovation Solution
The implementation of an N-tap QR-RLS filter with a pipeline architecture using 18-bit multipliers and a custom floating-point block for dynamic range adjustment, combined with methods for impulse removal, modulation scheme detection, and genetic algorithms for asynchronous spreading code identification, ensures fast computation, reliable weak signal detection, and optimal sampling window selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If standard RLS algorithm is used, then fast convergence speed is achieved, but numerical instability and divergence occur when inverse correlation matrix loses positive definiteness
Solution Approach 1:
The patent replaces the standard RLS algorithm's direct computation of the inverse correlation matrix with a QR decomposition-based approach. Instead of computing P(n) = E[xx^H]^-1 directly, the system uses QR decomposition of the data matrix to implicitly maintain the inverse correlation matrix properties, substituting the mechanical computation process with a numerically stable factorization approach that guarantees positive definiteness.
Solution Approach 2:
The patent changes the computational parameters by using QR decomposition factors (Q and R matrices) instead of directly computing the inverse correlation matrix. This parameter transformation allows the system to maintain numerical stability while achieving fast convergence, as the QR decomposition inherently preserves the positive definiteness property required for stable RLS operation.
2Measurement precision
If computational removal of strong signals is performed before detecting weak signals, then weak signal detection is enabled, but significant delay (latency) is introduced
Solution Approach 1:
The patent applies preliminary action by pre-whitening the input signal using the QR-RLS filter before detection. This preliminary transformation of the signal creates a whitened version where strong signals are suppressed and weak signals are enhanced, enabling direct detection without requiring separate removal steps. The pre-processing action of signal whitening simultaneously achieves both strong signal suppression and weak signal enhancement.
Solution Approach 2:
The patent introduces an intermediary transformation step where the QR-RLS filter acts as a mediator between the raw input signal and the detection process. Instead of directly removing strong signals or detecting weak signals in the original domain, the system transforms the signal through QR decomposition and whitening operations, creating an intermediate representation where detection is more efficient and faster.
3Productivity
If QR-RLS filter uses more computational resources, then faster computation and better signal processing are achieved, but excessive resource usage occurs
Solution Approach 1:
The patent segments the computational process into distinct stages: QR decomposition of the data matrix, calculation of the whitening transform, and application of the filter. By dividing the computation into these modular segments, the system can optimize each stage independently and manage computational resources more efficiently, avoiding the need for excessive overall resource consumption while maintaining fast computation speed.
Solution Approach 2:
The patent implements dynamic computation by using an adaptive QR-RLS filter that adjusts its parameters based on the input signal characteristics. The filter dynamically updates the QR decomposition and whitening transform according to changing signal conditions, allowing efficient resource usage when signals are stable while providing enhanced computation speed when rapid adaptation is needed.
4Speed
If adaptive filter adjusts to strong signal impulses, then short-term response is improved, but long-term performance degrades due to lasting effects on filter parameters
Solution Approach 1:
The patent implements feedback control by monitoring the filter's performance and using the QR-RLS adaptation mechanism to adjust parameters based on ongoing signal characteristics. The feedback loop continuously evaluates the signal environment and modifies the filter's QR decomposition and whitening transform accordingly, allowing the system to recover from impulse effects and maintain long-term performance while preserving short-term responsiveness.
Solution Approach 2:
The patent applies beforehand cushioning by using the QR-RLS filter's inherent numerical stability and positive definiteness guarantees to prevent excessive adaptation to impulse signals. The structured QR decomposition approach provides a form of cushioning that limits the impact of strong impulses on the filter parameters, protecting long-term performance while still allowing necessary short-term adjustments.
Data Source
AI summary
A QR-RLS adaptive digital filter provides fast computation without excessive computational resources. 18-bit multipliers enhance speed, and a floating point inverse square root block adjusts dynamic range in 12-dB steps. A memory stores two P-matrix copies, one being delivered with rows shifted according to the clock speed so as to enhance pipeline processing. Embodiments reliably detect modulation schemes, demodulate strong signals by passing feedback bits between multiple stages, remove impulses due to lightening, etc, erase symbol estimates which exceed an error threshold, and add high frequency noise to avoid mathematical divergence caused by excessive S/N. A genetic method is provided for identifying asynchronous spreading codes with minimum correlations, whereby randomly selected candidates compete based on Frobenius norms of their correlations, the weakest being discarded and the process being iterated. A method is provided for selecting optimal filter sampling windows for simultaneously detected symbol streams having relative timing delays.