Compressive Array Processing Reduces A/D Converter Count
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Solution Overview
Problem
Conventional signal processing systems with sensor arrays face high sampling complexity, which can be reduced using compressive sensing, but existing methods often require increased processing and more A/D converters, increasing costs and complexity.
Innovation Solution
The method involves forming linear combinations of signals from subsets of array elements, reducing the number of A/D converters needed by selecting specific subsets during the design phase, allowing for sparse reconstruction and target detection with minimal performance degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sampling methods are used in array processing, then measurement precision is maintained, but device complexity and sampling burden increase significantly
Solution Approach 1:
The patent extracts and processes only the essential signal components needed for reconstruction rather than sampling all array elements at full rate. By identifying and processing only the critical subsets of signals that contain the necessary information for accurate scene reconstruction, the system reduces sampling complexity while maintaining measurement precision through selective extraction of meaningful signal content
Solution Approach 2:
The patent performs preliminary processing by forming linear combinations of array element signals before the actual sampling and reconstruction stages. This preliminary action of pre-computing signal combinations allows the system to work with reduced-dimensional data that already contains the essential information needed for accurate reconstruction, thereby reducing the subsequent sampling burden while preserving measurement accuracy
2Device complexity
If fewer A/D converters are used, then device complexity and cost decrease, but the ability to capture full signal information deteriorates
Solution Approach 1:
The patent merges the signals from multiple array elements by forming linear combinations before sampling. By combining signals from different array elements into aggregated measurements, the system captures the essential spatial and temporal information in a compressed form that can be reconstructed accurately with fewer A/D converters, thereby preventing information loss despite reduced hardware complexity
Solution Approach 2:
The patent introduces linear combination operations as an intermediary processing step between the array elements and the A/D converters. This intermediary transformation converts the high-dimensional array signals into a lower-dimensional representation that preserves the critical information needed for reconstruction, allowing fewer A/D converters to capture sufficient signal information without direct loss of essential data
Data Source
AI summary
Signals received by an array of sensing elements are processed by first positioning the sensing elements in a uniform grid of L locations, wherein each location to include or not to include a sensing element is selected during a design phase. The sensing elements are selected and grouped into subsets, wherein each subset contains one or more sensing elements, and each sensing element is a member of one or more subsets. The signals in each subset are linearly combined to produce a combined signal, which is then sampled to form an output channel, which can detect objects.