Multi-Input Cognitive Signal Processor for Real-Time TDOA Estimation
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Solution Overview
Problem
Current systems for detecting and classifying source emitters over ultra-wide bandwidths require high-rate, power-hungry ADCs and complex algorithms, making them inefficient for real-time operation, especially in low SNR environments and for estimating TDOA and angles of arrival of multiple pulse waveforms.
Innovation Solution
A multi-input cognitive signal processor that uses a reservoir computer to predict and de-noise input signals from multiple antennas, estimating TDOA values and converting them into angles of arrival by cross-predicting and spatially de-noising signals using adaptive filter coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-rate ADCs and complex algorithms are used for detecting and classifying source emitters over ultra-wide bandwidths, then measurement precision and detection capability are improved, but power consumption and computational complexity increase significantly
Solution Approach 1:
The system segments the ultra-wide bandwidth into multiple sub-bandwidths and processes signals from multiple antennas separately. Each antenna's signal is processed independently through the cognitive signal processor, which performs denoising and TDOA estimation for each sub-bandwidth. This segmentation allows the system to achieve accurate TDOA estimation across the full ultra-wide bandwidth while keeping the computational load and power consumption of each processing unit manageable.
2Reliability
If complex algorithms are used for real-time signal processing over ultra-wide bandwidths, then detection capability is improved, but processing speed and real-time performance deteriorate
Solution Approach 1:
The cognitive signal processor performs preliminary denoising and signal enhancement actions before TDOA estimation. By预先 removing noise and enhancing signal characteristics in the time domain through adaptive filtering and prediction algorithms, the system prepares the signals for faster and more accurate cross-correlation-based TDOA estimation. This preliminary processing improves detection reliability while reducing the computational complexity of subsequent processing steps, enabling real-time performance.
3Measurement precision
If conventional denoising methods are used, then signal processing simplicity is maintained, but denoising effectiveness and TDOA estimation accuracy deteriorate in low SNR environments
Solution Approach 1:
The cognitive signal processor employs feedback mechanisms where the estimated TDOA values and signal characteristics from previous processing stages are fed back into the denoising algorithm. The adaptive filter coefficients are continuously adjusted based on the estimated signal parameters and noise conditions. This feedback loop allows the system to achieve high TDOA estimation accuracy in low SNR environments by dynamically adapting the denoising strength and characteristics, while the modular architecture keeps the overall system complexity manageable.
Data Source
AI summary
Described is a multi-input cognitive signal processor (CSP) for estimating time-difference-of-arrival (TDOA) of incoming signals. The multi-input CSP receives a mixture of input signals from an antenna a and an antenna b. The multi-input CSP predicts and temporally de-noises input signals a and b received from antennas a and b, respectively, using an input corresponding to each input signal, resulting in de-noised state vectors for input signals a and b. Using the de-noised state vectors for input signals a and b, cross-predicting and spatially de-noising the other of the de-noised state vectors for input signals a and b. TDOA values of signal pulses to each of antennas a and b are estimated and converted into estimated angles of arrival for each signal pulse.