Multi-Antenna Signal Processor Sub-Nyquist Ambiguity Resolution
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Solution Overview
Problem
RF signal processing equipment faces bottlenecks and loss of real-time performance due to high RF activity, and sub-Nyquist sampling introduces ambiguities and information loss, making it difficult to reliably reconstruct original signals and detect specific frequencies.
Innovation Solution
A signal processor system with multiple antennas sampling at distinct frequencies below the Nyquist rate, using a frequency feature detector and frequency resolvers to combine and synchronize data from multiple antennas, effectively resolving ambiguities and enhancing detection capabilities by comparing signals across different sampling frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sub-Nyquist sampling is used to reduce sampling rates below the Nyquist rate, then processing capability is improved and design constraints are reduced, but ambiguities arise in signal reconstruction and frequency detection capability deteriorates
Solution Approach 1:
The system segments the signal processing task by using multiple antennas (first and second antenna feeds) that sample the same RF signal at different sub-Nyquist sampling frequencies. Each antenna feed processes a portion of the frequency spectrum, and the results are combined to achieve complete frequency coverage without requiring any single antenna to sample at full Nyquist rate.
Solution Approach 2:
The system adds a spatial dimension by using multiple antennas receiving the same signal simultaneously at different locations. This spatial separation allows each antenna to be sampled at different sub-Nyquist rates, and the combined data from multiple spatial channels resolves the frequency ambiguities that would exist in a single-channel sub-Nyquist system.
2Loss of information
If sub-Nyquist sampling is used to reduce sampling rates, then information loss is reduced compared to conventional sampling limits, but blindness to certain frequencies occurs where integer multiples of half the sampling frequency give rise to no information
Solution Approach 1:
The frequency spectrum is segmented across multiple antenna feeds, each sampled at different sub-Nyquist rates. The first antenna feed samples at a first sampling frequency while the second antenna feed samples at a second sampling frequency, ensuring that frequency components that are blind to one sampler are captured by the other.
Solution Approach 2:
The system achieves universal frequency coverage by designing the multi-antenna sampling system such that the combination of different sub-Nyquist sampling frequencies covers the entire frequency range of interest. No single sampling frequency needs to cover all frequencies, but the set of frequencies collectively provides complete coverage.
3Measurement precision
If multiple antennas sample at different sub-Nyquist frequencies to resolve ambiguities, then detection accuracy is improved, but device complexity increases due to multiple samplers and synchronization requirements
Solution Approach 1:
The system merges the output data streams from multiple antenna feeds sampled at different frequencies through a combining mechanism. The frequency feature detector integrates information from multiple sample streams, resolving ambiguities by cross-referencing detections across different sampling frequencies and reducing false positives.
Solution Approach 2:
The system employs feedback mechanisms where the frequency feature detector analyzes detections from multiple antenna feeds and uses this information to resolve ambiguities. The detection results from one antenna feed provide feedback that helps disambiguate frequency measurements from other antenna feeds, improving overall detection accuracy.
Data Source
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AI summary
Signals from adjacent antennas are processed with respect to four dissimilar sampling frequencies, and the respective results are compared to resolve ambiguities.