Investigation of Adaptive Array Processing
The concept of a multi-function phased array radar, which would be capable of accomplishing
several disparate tasks, is not only interesting from a technological viewpoint but may
prove to have significant economic advantages
[Weber et al., 2005].
A multi-function radar
would, by necessity, use phased array technology
[Mailloux, 1994;
Hansen, 1998] owing to
the requirement of near-simultaneous aircraft surveillance and weather observations. Phased
arrays, such as the SPY-1A used at the NWRT, perform beamforming on both transmit
and receive, maximizing the antenna gain. Flexible beam steering would be accomplished
through a relatively complicated beam control system. In contrast to the SPY-1A,
preliminary designs for the MPAR system, as envisioned by MIT’s Lincoln Laboratory, would
consist of numerous overlapping subarrays, each of which would be capable of real-time
hardware-based beamforming. The set of time-series signals from all subarrays would then
be processed in software in order to produce focused beams within the spoiled transmit
beam of the radar. This software beamforming process is termed digital beamforming and
has several advantages, such as resolution enhancement and clutter mitigation.
Research in Progress
For this phase of the project, we propose to study the MPAR design using realistic numerical
simulations. Our focus will be on general validation of the design,
beam sharpening using adaptive techniques, and clutter mitigation algorithms.
MPAR Design Validation Using Radar Simulator
The digital beamforming design of
MPAR is innovative and would certainly be considered unconventional by the
general meteorological community. Therefore, it is important to validate the concept
using numerical simulations which are convincing to this community. Using the radar
simulator, it will be possible to closely emulate the MPAR design and generate raw
time-series data for each subarray. These data would be processed to estimate the
standard spectrum moments, which would be statistically compared to the source
meteorological fields from the ARPS data set. Leveraging on the aircraft tracking phase
of the project, multi-function capabilities will also be simulated and tested.
Resolution Enhancement Via Adaptive Digital Beamforming
Although more computationally expensive, data-dependent (adaptive) beamforming methods have several ad-
vantages. In particular, it is well-known that the Capon beamforming method
[Capon, 1969]
has superior angular resolution with moderate computational burden. This type
of adaptive beamforming has been demonstrated with vertically pointing phased array
radars
[Palmer et al., 1998]
and is applicable to the MPAR design given the availability of subarray time-series data.
It is proposed to investigate adaptive beamforming
from the perspective of resolution enhancement and to detail the possibility of reduced
aperture size while maintaining the angular resolution design criterion.
Clutter Mitigation
For conventional weather radars, ground clutter is mitigated by
the application of temporal digital filters. Phased array radar, in contrast, can use
spatial filtering to selectively null clutter targets
[Cheong et al., 2005a]. Normally,
these spatial filters are implemented by a set of pre-generated weights for each of
the array elements. Using these pre-generated weights, however, it is not possible
to attenuate signals from non-stationary clutter targets (aircraft, biological) or from
unknown ground clutter sources. Fortunately, the subarray design of MPAR would
allow the application of adaptive clutter cancellation
[Kamio et al., 2004]. For this
case, a clutter signal is defined as any signal from an undesired direction, independent
of whether it is stationary or non-stationary. This type of spatial filtering scheme will
be investigated in the context of the MPAR configuration.
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