Achieving Super-Resolution with Redundant Sensing

TL;DR

This paper introduces a novel interpretation of redundant sensing architecture that suppresses mismatch errors and enables super-resolution beyond the system's intrinsic limits, benefiting low-power sensor applications.

Contribution

It provides a new mathematical framework for redundant sensing that achieves super-resolution by leveraging code diffusion to mitigate mismatch errors.

Findings

Redundant sensing suppresses mismatch errors effectively.

Super-resolution surpasses intrinsic system resolution.

Applicable to low-power integrated sensors.

Abstract

Analog-to-digital (quantization) and digital-to-analog (de-quantization) conversion are fundamental operations of many information processing systems. In practice, the precision of these operations is always bounded, first by the random mismatch error (ME) occurred during system implementation, and subsequently by the intrinsic quantization error (QE) determined by the system architecture itself. In this manuscript, we present a new mathematical interpretation of the previously proposed redundant sensing (RS) architecture that not only suppresses ME but also allows achieving an effective resolution exceeding the system's intrinsic resolution, i.e. super-resolution (SR). SR is enabled by an endogenous property of redundant structures regarded as "code diffusion" where the references' value spreads into the neighbor sample space as a result of ME. The proposed concept opens the possibility for a wide range of applications in low-power fully-integrated sensors and devices where the cost-accuracy trade-off is inevitable.