Cognitive Signal Processor Denoising Ultrawide Bandwidth Signals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional cognitive signal processing systems face challenges in efficiently processing ultrawide bandwidth signals due to high computational complexity, memory requirements, and power demands, particularly in real-time applications, where high sampling rates and large size, weight, and power (SWaP) constraints are limiting factors.
Innovation Solution
The implementation of a cognitive signal processor (CSP) that uses a reservoir computer with a block-diagonal reservoir connectivity matrix and delay embedding to compute output layer weights over multiple clock cycles, allowing for efficient denoising of signals across a wide bandwidth while maintaining high processing rates, utilizing field-programmable gate arrays (FPGAs) or digital complementary metal-oxide-semiconductor (CMOS) hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If high sampling rate ADCs are used to process ultrawide bandwidth signals, then signal bandwidth processing capability is improved, but cost and power consumption increase significantly
Solution Approach 1:
The system segments the ultrawide bandwidth processing into multiple narrower bandwidth channels using a bank of channelizers. Each channelizer processes a specific frequency band at lower sampling rates, avoiding the need for a single high-rate ADC. The segmented channels are then processed independently through detection, localization, and classification algorithms.
Solution Approach 2:
The system transforms the time-domain high sampling rate problem into a frequency-domain solution by using FFT-based channelizers. Instead of processing all frequencies simultaneously at high rate in time domain, the system divides the bandwidth into multiple frequency channels that can be processed in parallel at lower rates.
2Measurement precision
If FFT-based detection and localization algorithms are used, then signal processing capability is improved, but computational complexity and memory requirements increase
Solution Approach 1:
The system performs preliminary signal processing by applying window functions and preprocessing techniques to the input signal before FFT analysis. This preliminary action reduces the computational burden on subsequent detection and localization algorithms while maintaining detection accuracy.
Solution Approach 2:
The system dynamically adjusts processing parameters such as FFT window size, overlap factor, and threshold levels based on signal characteristics and detection requirements. This dynamic adaptation optimizes computational complexity for different operating conditions while maintaining measurement precision.
3Device complexity
If conventional ESM systems with spectrum sweeping are used, then system complexity is reduced, but processing speed becomes too slow for agile emitters
Solution Approach 1:
The system implements continuous frequency scanning across the ultrawide bandwidth using parallel channelizers that simultaneously monitor multiple frequency bands. This continuous monitoring eliminates the slow sequential sweeping approach while maintaining manageable system complexity through modular channelizer design.
4Measurement precision
If training-based denoising methods with large dictionaries are used, then denoising performance is improved, but memory requirements and computation increase making it infeasible for low SWaP systems
Solution Approach 1:
The system extracts only the essential signal characteristics and features needed for denoising rather than storing complete training dictionaries. By extracting key temporal and spectral features, the system achieves effective denoising with minimal memory storage requirements.
Solution Approach 2:
The system uses lightweight, computationally efficient denoising algorithms that can be implemented with simple filters and adaptive thresholding rather than complex trained dictionaries. These simpler processing elements consume less memory and computation while providing adequate denoising performance for the application.
Data Source
AI summary
Implementations provide denoising a signal. A plurality of reservoir state values are produced based on the signal and the plurality of reservoir state values are collected into a historical record. A plurality of reservoir state value weights are calculated based at least in part on the historical record to produce a plurality of output values. The plurality of reservoir state value weights are computed over multiple clock cycles of a clock for the cognitive signal processor system. The plurality of output values are output. A more accurate representation of a next of set of output layer weights is thereby obtained.