Jie Zhou

Sichuan University

AlgorithmMathematical optimizationMatrix (mathematics)Mean squared errorKalman filterFusionEstimation theoryCovarianceCovariance matrixEstimatorFusion centerApplied mathematicsMathematicsSensor fusionOptimal estimationComputer scienceControl theoryTransmission (telecommunications)Covariance intersectionStatistical hypothesis testing

54Publications

10H-index

722Citations

Publications 52

#1Yao Rong (Yunnan University)H-Index: 4

#2Mengjiao Tang (Yunnan University)H-Index: 4

Last. Jie Zhou (Sichuan University)H-Index: 10

view all 4 authors...

In this correspondence, we make a few corrections to the geodesic projection method of estimation fusion presented in [M. Tang, Y. Rong, J. Zhou, and X. R. Li, IEEE TSP, vol. 67, no. 2, pp. 279-292, 2019].

#1Xiangbing Chen (Sichuan University)

#2Jie Zhou (Sichuan University)H-Index: 10

Last. Sanfeng Hu (Sichuan University)

view all 3 authors...

Abstract As a natural intrinsic measure on the manifold of probability distributions, Rao distance has received a lot of attentions and been applied successfully to many fields. However, the closed form of Rao distance on the manifold of multivariate elliptical distributions (MEDs) is still absent. In this paper, a class of Manhattan distances (MHDs) with single parameter on MED manifold is constructed by deducing explicit expressions of the geodesic and Rao distance on a specified submanifold o...

#1Li Chen

#2Nathaniel JosephsH-Index: 2

Last. Eric D. KolaczykH-Index: 36

view all 5 authors...

In this paper, we propose a new spectral-based approach to hypothesis testing for populations of networks. The primary goal is to develop a test to determine whether two given samples of networks come from the same random model or distribution. Our test statistic is based on the trace of the third order for a centered and scaled adjacency matrix, which we prove converges to the standard normal distribution as the number of nodes tends to infinity. The asymptotic power guarantee of the test is al...

A novel heterogeneous behavior representation for linear stochastic switched system is proposed in the discrete-time domain. The switching modes of state evolution and measurement output are described by two random sequences with known probability information. The more general and flexible framework covers several classes of well-studied models as special cases, and can be served to manage different complex systems with random abrupt changes in structure and parameter, so that it has wider appli...

#1Junhao Guo (Sichuan University)

#2Jie Zhou (Sichuan University)H-Index: 10

Last. Sanfeng Hu (Sichuan University)H-Index: 1

view all 3 authors...

Abstract The property of statistical models not depending on the coordinate systems or model parametrization is one main interest of intrinsic inference in statistics. The intrinsic covariance matrix estimation is addressed for multivariate elliptical distributions in this paper. An optimal intrinsic covariance estimator is derived in the sense of minimizing the mean square Rao distance, and proved to own intrinsic unbiasedness. Specifically, the intrinsically unbiased estimators for elliptical ...

#1Mengjiao Tang (Xi'an Jiaotong University)H-Index: 4

#2Yao Rong (Sichuan University)H-Index: 4

Last. Jie Zhou (Sichuan University)H-Index: 10

view all 4 authors...

This paper studies the problem of detecting range-spread targets in (possibly non-Gaussian) clutter whose joint distribution belongs to a very general family of complex matrix-variate elliptically contoured distributions. Within the family, we explore invariance with respect to both the distributional type and relevant parameters. Several groups are used to describe these invariance mechanisms, and a relationship is revealed between the group invariance and the constant false alarm rate (CFAR) p...

#1Li Chen (Sichuan University)

#2Lizhen Lin (ND: University of Notre Dame)H-Index: 14

Last. Jie Zhou (Sichuan University)H-Index: 10

view all 3 authors...

Neuroimaging techniques have been routinely applied in various studies in neuroscience, which contribute to providing novel insights into brain functions. One of the most important and challenging questions related to data collected from such studies is hypothesis testing for the differences between two samples of networks of brain regions. This is due to the fact that networks constructed from neuroimaging studies, which can be weighted and large, are very complex. Focusing on this problem, a n...

#1Li Chen (MUC: Minzu University of China)

#2Jie Zhou (Sichuan University)H-Index: 10

Last. Lizhen Lin (ND: University of Notre Dame)H-Index: 14

view all 3 authors...

It has become an increasingly common practice for scientists in modern science and engineering to collect samples of multiple network data in which a network serves as a basic data object. The increasing prevalence of multiple network data calls for developments of models and theory that can deal with inference problems for populations of networks. In this work, we propose a general procedure for hypothesis testing of networks and in particular, for differentiating distributions of two samples o...

Adaptive Radar Detection in Gaussian Disturbance With Structured Covariance Matrix via Invariance Theory

#1Mengjiao Tang (Xi'an Jiaotong University)H-Index: 4

#2Yao Rong (Sichuan University)H-Index: 4

Last. Jie Zhou (Sichuan University)H-Index: 10

view all 5 authors...

This paper deals with adaptive radar detection of targets in the presence of Gaussian disturbance sharing a block-diagonal covariance structure. The problem is formulated according to a very general signal model, which contains the point-like, range-spread, and subspace target (or targets) as special instances. Hence, a unified study on the resulting adaptive detection problem is handled with the use of the invariance theory. The obtained results, including an appropriate transformation group, a...

Adaptive Diffusion for Distributed Optimization over Multi-Agent Network with Random Delays (Poster)

Optimizing an aggregate function over a multi-agent network based on diffusion strategies calls for node collaborations, where each sensor exchanges information with their predefined neighbors within its transmission/reception range, and then combines them linearly with fixed and non-adaptive scalar weights to obtain a consensus solution. However, the transmission/reception process may be corrupted by random delays due to imperfect network environment. This paper proposes a distributed method to...