Neural-inspired Measurement Observability

TL;DR

This paper explores neural-inspired measurement functions to enhance sensor observability, combining linear and nonlinear analysis tools, and applies these insights to optimize sensor placement on bioinspired wings.

Contribution

It introduces a novel framework for analyzing observability of neural-inspired composite measurement systems using Lie algebraic and empirical methods.

Findings

Analytical tools for observability of delayed linear systems.

Extension of Lie algebraic observability to nonlinear systems with delays.

Optimal sensor placement based on empirical observability metrics.

Abstract

The neural encoding by biological sensors of flying insects, which prefilters stimulus data before sending it to the central nervous system in the form of voltage spikes, enables sensing capabilities that are computationally low-cost while also being highly robust to noise. This process, which can be modeled as the composition of a linear moving average filter and a nonlinear decision function, inspired the work reported here to improve engineered sensing performance by maximizing the observability of particular neural-inspired composite measurement functions. We first present a tool to determine the observability of a linear system with measurement delay (the first element of the composition), then use a Lie algebraic observability approach to study nonlinear autonomous systems with output delay (the second element of the composition). The Lie algebraic tools are then extended to address overall observability of systems with composite outputs as in the neural encoder model we adopt. The analytical outcomes are supported using the empirical observability Gramian, and optimal sensor placement on a bioinspired wing model is performed using metrics based on the empirical Gramian.