Minimal Sensor Placement for Generic State and Unknown Input Observability

TL;DR

This paper investigates the minimal sensor placement problem for ensuring state and input observability in linear systems with unknown inputs, revealing its NP-hardness and proposing bounds and algorithms for approximate solutions.

Contribution

It provides refined conditions for sensor placement in structured systems, proves NP-hardness of the problem, and introduces polynomial-time bounds and a two-stage approximation algorithm.

Findings

Determined the NP-hardness of minimal sensor placement for unknown inputs.

Established polynomial-time bounds for the placement problem.

Developed a two-stage polynomial-time approximation algorithm.

Abstract

This paper addresses the problem of selecting the minimum number of dedicated sensors to achieve observability in the presence of unknown inputs, namely, the state and input observability, for linear time-invariant systems. We assume that the only available information is the zero-nonzero structure of system matrices, and approach this problem within a structured system model. We revisit the concept of state and input observability for structured systems, providing refined necessary and sufficient conditions for placing dedicated sensors via the Dulmage-Mendelsohn decomposition. Based on these conditions, we prove that determining the minimum number of dedicated sensors to achieve generic state and input observability is NP-hard, which contrasts sharply with the polynomial-time complexity of the corresponding problem with known inputs. We also demonstrate that this problem is hard to approximate within a factor of $(1-o(1)){\rm{log}}(n)$, where $n$ is the state dimension. Notwithstanding, we propose nontrivial upper and lower bounds that can be computed in polynomial time, which confine the optimal value of this problem to an interval with length being the number of inputs. We further present a special case for which the exact optimal value can be determined in polynomial time. Additionally, we propose a two-stage algorithm to solve this problem approximately. Each stage of the algorithm is either optimal or suboptimal and can be completed in polynomial time.