Trade-offs Between Weak-Noise Performance and Probability of Anomaly in Parameter Estimation from Noisy Chaotic Signals

TL;DR

This paper investigates the trade-offs in parameter estimation accuracy and anomaly probability in noisy chaotic signals, proposing bounds and alternative signals to improve estimation performance in chaotic communication systems.

Contribution

It introduces a lower bound on weak-noise estimation error and suggests using itinerary signals to outperform traditional chaotic outputs.

Findings

Derived a lower bound on estimation error at high SNR.

Showed itinerary signals can outperform main chaotic signals.

Analyzed the impact of anomaly probability constraints.

Abstract

We consider the problem of parameter estimation, based on noisy chaotic signals, from the viewpoint of twisted modulation for waveform communication. In particular, we study communication systems where the parameter to be estimated is conveyed as the initial condition of a chaotic dynamical system of a certain class and we examine its estimation performance in terms of the expectation of a given convex function of the estimation error at high SNR, under the demand that the probability of anomaly is kept small. We derive a lower bound on the weak-noise estimation error for this class of chaotic modulators, and argue that it can be outperformed by using the itinerary signal associated with the chaotic system instead of the main chaotic output signal.