# Semiparametric CRB and Slepian-Bangs formulas for Complex Elliptically Symmetric Distributions

## TL;DR

This paper extends semiparametric inference methods to Complex Elliptically Symmetric distributions, deriving bounds and formulas crucial for non-Gaussian data analysis in array processing, with practical simulation validations.

## Contribution

It introduces the first closed-form Semiparametric Cramér-Rao Bound and Slepian-Bangs formula for CES distributions, enhancing estimation theory in complex data models.

## Key findings

- Derived the constrained Semiparametric Cramér-Rao Bound for CES
- Formulated the Semiparametric Slepian-Bangs formula for Fisher Information
- Validated results through simulations in array processing scenarios

## Abstract

The main aim of this paper is to extend the semiparametric inference methodology, recently investigated for Real Elliptically Symmetric (RES) distributions, to Complex Elliptically Symmetric (CES) distributions. The generalization to the complex field is of fundamental importance in all practical applications that exploit the complex representation of the acquired data. Moreover, the CES distributions has been widely recognized as a valuable and general model to statistically describe the non-Gaussian behaviour of datasets originated from a wide variety of physical measurement processes. The paper is divided in two parts. In the first part, a closed form expression of the constrained Semiparametric Cram\'{e}r-Rao Bound (CSCRB) for the joint estimation of complex mean vector and complex scatter matrix of a set of CES-distributed random vectors is obtained by exploiting the so-called \textit{Wirtinger} or $\mathbb{C}\mathbb{R}$-\textit{calculus}. The second part deals with the derivation of the semiparametric version of the Slepian-Bangs formula in the context of the CES model. Specifically, the proposed Semiparametric Slepian-Bangs (SSB) formula provides us with a useful and ready-to-use expression of the Semiparametric Fisher Information Matrix (SFIM) for the estimation of a parameter vector parametrizing the complex mean and the complex scatter matrix of a CES-distributed vector in the presence of unknown, nuisance, density generator. Furthermore, we show how to exploit the derived SSB formula to obtain the semiparametric counterpart of the Stochastic CRB for Direction of Arrival (DOA) estimation under a random signal model assumption. Simulation results are also provided to clarify the theoretical findings and to demonstrate their usefulness in common array processing applications.