Space-Time Error Shaping in Coarse Sensor Arrays
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Solution Overview
Problem
Existing array processing technologies face challenges in effectively addressing quantization errors in coarsely quantized arrays, particularly in spatial oversampling and error shaping, which limits the precision and efficiency of signal processing in applications like beamforming and communication systems.
Innovation Solution
A space-time error-shaping array system is developed, incorporating a multi-input multiple-output (MIMO) discrete-time filter structure that shapes quantization errors based on both temporal and spatial aspects of quantized waveforms, using a digital multichannel processor and dimensionality-reduction filters to improve precision and reduce noise, enabling efficient processing in coarsely quantized arrays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If coarsely quantized arrays are used, then device complexity and power consumption are reduced, but measurement precision deteriorates
Solution Approach 1:
The patent implements error shaping through feedback mechanisms where quantization errors from one sensor are propagated to adjacent sensors. This feedback loop allows the system to actively manage and redistribute quantization errors, shaping them to higher wavenumbers where they can be filtered out, thereby maintaining measurement precision despite using coarse quantizers.
Solution Approach 2:
The patent extends error shaping from temporal dimension to spatial dimension by propagating quantization errors across multiple sensor elements in the array. This spatial error shaping adds a new dimension to the error management strategy, allowing the system to exploit spatial redundancy and oversampling to achieve high precision output from coarse quantizers.
2Measurement precision
If spatial oversampling is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts and separates quantization errors from the desired signal by propagating them through dedicated error shaping paths. This allows the main signal processing path to focus on extracting useful information while the error propagation mechanism handles quantization artifacts, effectively dividing the processing tasks to manage complexity.
Solution Approach 2:
The patent introduces error shaping filters as intermediary components between the coarse quantizers and the final signal processing stages. These intermediary filters act as mediators that transform quantization errors into a form that can be easily separated and removed, simplifying the overall processing architecture while maintaining high precision.
3Measurement precision
If error shaping is applied, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the error shaping process into modular components associated with each sensor element. Each sensor has its own error shaping filter that processes local quantization errors, allowing the system to distribute complexity across multiple simple units rather than requiring one complex centralized processor.
Solution Approach 2:
The patent designs universal error shaping filter structures that can be applied to each sensor element in the array. These multi-functional filters serve multiple purposes: shaping quantization errors, exploiting spatial oversampling, and preparing signals for subsequent processing, thereby reducing overall system complexity through component reuse.
Data Source
AI summary
Methods and apparatus for shaping and filtering quantization errors conjointly in space and time to produce a higher-precision output in a spatially and temporally oversampled array. A space-time error-shaping array system has an array of sensors, each sensor producing a temporal signal comprising quantized waveforms. A multi-input multiple-output (MIMO) discrete-time filter structure with multiple inputs, each coupled to a sensor of the array of sensors, shapes quantization errors of the array of sensors on the basis of temporal aspects of the quantized waveforms conjointly with spatial aspects of the quantized waveforms.