Results 251 to 260 of about 7,234,439 (291)

Modified Rao Test for Multichannel Adaptive Signal Detection

IEEE Transactions on Signal Processing, 2016
The problem of detecting a subspace signal is studied in colored Gaussian noise with an unknown covariance matrix. In the subspace model, the target signal belongs to a known subspace, but with unknown coordinates. We first present a new derivation of the Rao test based on the subspace model, and then propose a modified Rao test (MRT) by introducing a ...
Jun Liu 0004, Weijian Liu 0001, Bo Chen 0001, Hongwei Liu 0001, Hongbin Li 0001, Chengpeng Hao   +5 more
openaire   +2 more sources

Persymmetric Rao test for MIMO radar in Gaussian disturbance

Signal Processing, 2019
Abstract This paper considers the target detection problem in multiple-input multiple-output radar where the disturbance covariance matrix is unknown. Based on the Rao criterion, a persymmetric Rao test is designed by exploiting the persymmetric structure of the disturbance covariance matrix.
Jun Liu 0004, Jinwang Han, Weijian Liu 0001, Shu-Wen Xu 0001, Zi-Jing Zhang   +4 more
openaire   +2 more sources

Rao tests for distributed target detection in interference and noise

Signal Processing, 2015
This paper deals with the problem of detecting a distributed target in interference and noise. The target signal and interference are assumed to lie in two linearly independent subspaces, and their coordinates are unknown. The noise is Gaussian distributed, with an unknown covariance matrix.
Weijian Liu 0001, Jun Liu 0004, Lei Huang 0001, Dujian Zou, Yongliang Wang   +4 more
openaire   +1 more source

Rao Test With Improved Robustness for Range-Spread Target Detection

2020 28th European Signal Processing Conference (EUSIPCO), 2021
This paper deals with the problem of detecting range-spread targets in Gaussian noise with unknown covariance matrix. We model the received signal under the signal-plus-noise hypothesis as the sum of noise, useful target echoes and fictitious signals, which makes the signal-plus-noise hypothesis more plausible in the mismatched case.
Shengyin Sun, Jun Liu 0004, Weijian Liu 0001   +2 more
openaire   +1 more source

Bayesian Rao and Wald test for radar adaptive detection

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
This paper deals with the adaptive detection of a signal of interest in the presence of Gaussian noise with unknown covariance matrix (CM). To this end, we resort to a Bayesian approach based on a suitable model for the probability density function (PDF) of unknown CM.
Yu Zhou 0017, Lin-rang Zhang
openaire   +1 more source

Adjustments of Rao’s Score Test for Distributional and Local Parametric Misspecifications

Journal of Econometric Methods, 2017
Abstract Rao’s (1948) seminal paper introduced a fundamental principle of testing based on the score function and the score test has local optimal properties. When the assumed model is misspecified, it is well known that Rao’s score (RS) test loses its optimality. A model could be misspecified in a variety of ways.
Bera, Anil K., Bilias, Yannis, Yoon, Mann J., Taşpınar, Süleyman, Doğan, Osman   +4 more
openaire   +1 more source

A new two-stage rao test detector

IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 2010
In this paper, we propose a new two-stage Rao test detector, which is obtained by cascading a subspace detector (SD) and the Rao test. A set of secondary data is available and noise returns in primary and secondary data share the same statistical characterization.
Chengpeng Hao, Long Cai, Changlong Si, Qi Xu   +3 more
openaire   +1 more source

Burbea–Rao divergence based statistics for testing uniform association

Mathematics and Computers in Simulation, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
María-Dolores Jiménez-Gamero, V. Alba Fernández, María Dolores Estudillo-Martínez   +2 more
openaire   +1 more source

Coincidence of the Rao Test, Wald Test and GLRT for anomaly detection in hyperspectral imagery

Signal Processing, 2020
Abstract Anomaly detection methods are designed to detect targets (small anomalies) without a priori information on the target spectral signature. In this letter, we deal with the problem of anomaly detection for hyperspectral images based on the Gaussian model assuming that the background obeys a real-valued Gaussian multivariate distribution with ...
Yutong Feng, Jun Liu 0004, Weijian Liu 0001   +2 more
openaire   +1 more source

Fisher Information Matrix, Rao Test, and Wald Test for Complex-Valued Signals and Their Applications

Signal Processing, 2014
Weijian Liu, Yongliang Wang, Wenchong Xie   +2 more
exaly   +2 more sources