Fisher Information Approach for Masking Sensing Plan in Multifunction Radars

Core Concepts

Design a radar controller using Fisher information to mask the sensing plan from adversaries.

Abstract

The content discusses the application of Fisher Information Approach in designing a Markov Decision Process (MDP) based radar controller to conceal its sensing plan. It explores how perturbations in the MDP's transition kernel and operation cost can reduce Fisher Information, hindering adversary's estimation accuracy. Introduction to Markov Decision Processes (MDPs) for radar optimization. Analytical derivation of Fisher Information Matrix (FIM) determinant for MDP-based controllers. Application of FIM as a design constraint to mask sensing plans. Perturbation strategies in MDPs to reduce adversary's estimation accuracy. Comparison with traditional radar strategies and related works.

Stats

The radar controller purposefully minimizes the Fisher information of its emissions so that an adversary cannot identify the controller’s model parameters accurately. Numerical results show that minor perturbations can reduce the Fisher Information of the emissions, amplifying variability in policy and transition kernel estimation errors.

Quotes

"The primary objective is to decrease the determinant of the FIM concerning the adversary’s estimation of the sensing plan." "Lower curvature of likelihood function correlates with higher variance in parameter estimate for adversaries."

Key Insights Distilled From

Fisher Information Approach for Masking the Sensing Plan

by Shashwat Jai... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15966.pdf

Deeper Inquiries

How does this approach compare with traditional open-loop statistical inference methods

In traditional open-loop statistical inference methods, the Fisher information serves as a lower bound for the achievable covariance. However, in this approach, the Fisher information is utilized as a design constraint for a closed-loop radar controller to mask its sensing plan from potential adversaries. By purposely minimizing the Fisher information of emissions, the radar controller aims to hinder accurate identification of its model parameters by adversaries. This contrasts with traditional methods where Fisher information is used more passively as an indicator of estimation accuracy.

What are potential drawbacks or limitations of using Fisher information as a design constraint

One potential drawback of using Fisher information as a design constraint is that it may introduce complexity and computational overhead in optimizing the system. The non-convex nature of optimization problems involving Fisher information constraints can make them challenging to solve efficiently. Additionally, relying solely on minimizing Fisher information may lead to suboptimal performance in certain scenarios where other factors need to be considered for effective operation.

How might this concept be applied in other fields beyond radar systems

The concept of utilizing Fisher Information as a design constraint can be applied beyond radar systems in various fields such as finance, healthcare, and telecommunications. In finance, it could be used to optimize investment strategies while masking sensitive trading patterns from competitors or regulators. In healthcare, it might aid in designing patient monitoring systems that protect privacy while providing accurate data analysis. In telecommunications, this approach could enhance network security by concealing signaling protocols from malicious entities while maintaining efficient communication processes.