Compressive Sensor Array Sampling With One ADC for Sparse Sources
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Solution Overview
Problem
Existing sensor array systems require redundant and expensive parallel acquisition hardware, leading to increased complexity, power consumption, and data volume, which becomes a bottleneck as the number of sensors increases, especially when only a few emitting sources are of interest.
Innovation Solution
A compressive sensor array system uses compressive sampling techniques to combine analog sensor signals from multiple sensors into a composite signal, modulating them with a random sequence to establish a sparse measurement basis, allowing for sub-Nyquist sampling and reducing the need for redundant hardware by projecting signals onto a sparse measurement basis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If parallel acquisition hardware is used for each sensor in the array, then each sensor can independently process and digitize its signal, but the system becomes expensive, power intensive, and complex due to redundant hardware components
Solution Approach 1:
The patent combines multiple sensor signals into a single composite signal that is then processed by a single ADC. Instead of having separate acquisition hardware for each sensor, the system merges the analog signals from N sensors through a combiner circuit, reducing the number of ADCs from N to 1, thereby eliminating redundant hardware while maintaining signal acquisition capability
Solution Approach 2:
The single ADC and associated processing hardware serve multiple sensors simultaneously. The acquisition system is designed to handle signals from N sensors through a universal processing path, making the hardware multi-functional rather than dedicated to a single sensor, thus reducing overall system complexity
2Measurement precision
If the number of sensors is increased to achieve higher spatial resolution, then the aperture utilization improves, but the amount of digital sensor data grows linearly requiring larger storage, higher transmission data rates and faster processing
Solution Approach 1:
The patent extracts only the essential information from the sensor array by projecting the N-dimensional sensor signal space onto a lower-dimensional subspace using random projection matrices. This extraction process captures the dominant signal characteristics while discarding redundant information, reducing data volume while preserving measurement precision for sparse signals
Solution Approach 2:
The system transforms the problem from the spatial domain to a randomized measurement domain. By applying random projection matrices, the patent maps N sensor inputs to M measurement outputs where M < N, effectively changing the dimensional representation of the data while preserving the essential spatial resolution information through compressed sensing theory
3Loss of information
If sensors sample at the Nyquist rate determined by channel bandwidth, then all signal information is captured, but the sampling rate is higher than necessary for sparse signals containing only a few emitting sources
Solution Approach 1:
The patent changes the sampling parameter from the traditional Nyquist rate (determined by signal bandwidth) to a sub-Nyquist rate determined by the sparsity of the signal. By recognizing that only K sources are present among N sensors, the system samples at a rate proportional to K rather than the full bandwidth, improving sampling efficiency while using random projection to prevent information loss
Solution Approach 2:
The system applies random projection matrices to the sensor signals before sampling, preparing the data in advance to enable efficient sub-Nyquist sampling. This preliminary transformation embeds the sparsity information into the measurement process, allowing the ADC to operate at lower rates while still capturing all essential signal information through the randomized measurement framework
Data Source
AI summary
A compressive sensor array (CSA) system and method uses compressive sampling techniques to acquire sensor data from an array of sensors without independently sampling each of the sensor signals. In general, the CSA system and method uses the compressive sampling techniques to combine the analog sensor signals from the array of sensors into a composite sensor signal and to sample the composite sensor signal at a sub-Nyquist sampling rate. At least one embodiment of the CSA system and method allows a single analog-to-digital converter (ADC) and single RF demodulation chain to be used for an arbitrary number of sensors, thereby providing scalability and eliminating redundant data acquisition hardware. By reducing the number of samples, the CSA system and method also facilitates the processing, storage and transmission of the sensor data.