Kalman filter for dynamic imaging based on complex empirical covariances

被引：1

作者：

Arab, Nawel ^{[1
]}

Cano, Cyril ^{[2
]}

Vin, Isabelle ^{[1
]}

El Korso, Mohammed Nabil ^{[3
]}

Chaumette, Eric ^{[2
]}

Larzabal, Pascal ^{[1
]}

机构：

[1] Univ Paris Saclay, SATIE, ENS Paris Saclay, F-91190 Gif Sur Yvette, France

[2] Univ Toulouse, ISAE SUPAERO, F-31000 Toulouse, France

[3] Univ Paris Saclay, CentraleSupelec, L2S, F-91190 Gif Sur Yvette, France

来源：

2023 IEEE 9TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING, CAMSAP | 2023年

关键词：

Estimation; Kalman filter; Visibility matrix; Radio astronomy;

D O I：

10.1109/CAMSAP58249.2023.10403483

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Kalman filter (KF) is a priori unsuitable for the estimation from sample covariance matrices as they cannot be formulated analytically as a function of state parameters to be estimated. In this work, we propose a novel KF adapted to sample covariance matrices under the unconditional signal model. It is evaluated on simulated data representative of a dynamic radio astronomy framework, considering multiple uncorrelated sources and Gaussian noise. The results show that our method is capable of effectively tracking moving sources in complex scenes with greater accuracy than a KF regularized in a standard way, i.e., without proper formalization of the noise model.

引用

页码：461 / 465

页数：5