Paper Info
Title
Noncoherent MIMO radar for location and velocity estimation: more antennas means better performance
Abstract
This paper presents an analysis of the joint estimation of target location and velocity using a multiple-input multiple-output (MIMO) radar employing noncoherent processing for a complex Gaussian extended target. A MIMO radar with M transmit and N receive antennas is considered. To provide insight, we focus on a simplified case first, assuming orthogonal waveforms, temporally and spatially white noise-plus-clutter, and independent reflection coefficients. Under these simplifying assumptions, the maximum-likelihood (ML) estimate is analyzed, and a theorem demonstrating the asymptotic consistency, large MN, of the ML estimate is provided. Numerical investigations, given later, indicate similar behavior for some reasonable cases violating the simplifying assumptions. In these initial investigations, we study unconstrained systems, in terms of complexity and energy, where each added transmit antenna employs a fixed energy so that the total transmitted energy is allowed to increase as we increase the number of transmit antennas. Following this, we also look at constrained systems, where the total system energy and complexity are fixed. To approximate systems of fixed complexity in an abstract way, we restrict the total number of antennas employed to be fixed. Here, we show numerical examples which indicate a preference for receive antennas, similar to MIMO communications, but where systems with multiple transmit antennas yield the smallest possible mean-square error (MSE). The joint Cramér-Rao bound (CRB) is calculated and the MSE of the ML estimate is analyzed. It is shown for some specific numerical examples that the signal-to-clutter-plus-noise ratio (SCNR) threshold, indicating the SCNRs above which the MSE of the ML estimate is reasonably close to the CRB, can be lowered by increasing MN. The noncoherent MIMO radar ambiguity function (AF) is developed in two different ways and illustrated by examples. It is shown for some specific examples that the size of the product MN controls the levels of the sidelobes of the AF.
Year
DOI
Venue
2010
10.1109/TSP.2010.2044613
IEEE Transactions on Signal Processing
Keywords
Field
DocType
noncoherent mimo radar ambiguity,fixed energy,mimo radar,total transmitted energy,large mn,velocity estimation,mimo communication,noncoherent mimo radar,total system energy,numerical example,fixed complexity,ml estimate,better performance,white noise,maximum likelihood,mean square error,maximum likelihood estimation,mimo,ambiguity function,reflection coefficient,cramer rao bound,helium
Radar,Ambiguity function,Cramér–Rao bound,Clutter,Control theory,Antenna array,Mean squared error,Algorithm,MIMO,Estimation theory,Statistics,Mathematics
Journal
Volume
Issue
ISSN
58
7
1053-587X
Citations 
PageRank 
References 
20
0.97
14
Authors
3
Name
Order
Citations
PageRank
Qian He123928.57
Rick S. Blum22322266.38
Alexander M. Haimovich361869.28