Ambiguity Resolution by Low-Rank Matrix
Completion for Distributed Subarray Antennas
Shipborne VHF Radar
Chen Genhua
∗
, Liu Baohong
∗
, Zeng Chunhua
∗
, Chen Baixiao
†
*
School of Information Engineering, Nanchang Institute of Technology, Nanchang, China, cghnit@126.com
†
National Lab of Radar Signal Processing, Xidian University, Xi’an, China, bxchen@xidian.edu.cn
Abstract: Distributed subarray antennas(DSA) have been
presented to satisfy the need for high -resolution and high-gain
radars, especially for shipborne radar platform due to limited
surface available. Coherent combining the distributed
apertures leads to the grating lobes in beampattern or
ambiguities in direction of arrival (DOA) estimation. By
exploiting the low-rank matrix completion theory and positive
definite programming, an ambiguity resolution algorithm is
proposed. It accomplishes high resolution DOA estimation,
coherent processing and aperture extension of the DSA.
Simulation results demonstrate the efficacy of the ambiguity
resolution method with high resolution performance.
Keywords: Ambiguity Resolution; Matrix Completion;
Distributed Subarray Antennas; Shipborne VHF Radar
I. INTRODUCTION
The ability to detect stealthy targets and targets beyond the
horizon is an attractive feature for VHF radar. However, there
are some disadvantages such as wide beamwidth and low
angular resolution by the limitations of VHF antenna aperture
size. This problem is especially an issue for shipborne radar
platform. One approach to high angular resolution is to use
distributed subarray antennas. An example is the
inter-ferometric radar characterized by two widely separated
subarrays for creating a large baseline with a limited amount of
hardware[1]. Lincoln Laboratory also have been carrying out
distributed coherent aperture research for next generation
radar(NGR)[2]. However, coherent combining distributed
apertures results in grating lobes in the beampattern or
ambiguity in the DOA estimation. The two-way beampattern
design based on array pattern multiplication can resolve the
grating lobes, causing an increase in the complexity of
subarray antennas design[3]. The two-step method applicable
only to special array geometry embedding dual-size spatial
invariance can also achieve ambiguity resolution [4]. The
Maximum Likelihood algorithm automatically incorporates
ambiguity resolution, thus being computationally intensive[5].
This paper proposes an ambiguity resolution method from
corrupted low-rank matrix completion theory and positive
definite programming.
II. SIGNAL MODEL
Assume that a DSA composed of
subarrays designated
as
,
, as shown in Fig.1. Each subarray is a
-element uniform linear array(ULA) with
as grid
distance, where
is the wavelength,
the
number of elements. The leftmost element of subarray
is
regarded as the reference, and the position of the leftmost
element of every subarray is
,
is the physical aperture,
.For descriptive convenience, the grid distance
is
omitted.
Fig.1 Configuration of Distributed Subarray Antennas
Let
narrowband plane-wave signals with DOAs
impinge on the DSA,
,where
is
defined to be the angle w.r.t. the broadside of the DSA.
The snapshot of array outputs can be modeled as
( ) ( ) ( ) ( )t t txAθ sn
(1)
where
is the steering vector, the array manifold
matrix
1
( ) [ ( ), , ( )]
K
A θ aa
, and
the element- position
vector,
is spatial frequency,
.
1
( ) [ ( ), , ( )]
T
K
t s t s t s
is the signal vector,
is a white
Gaussian noise with variance
.
denotes the transpose.
The data covariance matrix of array outputs of the DSA is
given by
2
[ ( ) ( )]
HH
D n N
E t t
R x x ASA I
(2)
where
is the signal covariance matrix,
the identity matrix.
and
stands for expectation operator and Hermitian
transpose, respectively.
Given
independent snapshots, the ML estimation of the
spatial covariance matrix
is the direct data covariance
(DDC) matrix
1
1
ˆ
( ) ( )
L
H
D
t
tt
L
R x x
(3)