1. TECHNICAL OBJECTIVES
The goals of this project are to develop and evaluate advanced signal processing
concepts for clutter mitigation in over-the-horizon HF radar. Both spatial and temporal
processing will be evaluated including: 1) methods for achieving adaptivity on both
transmit and receive arrays, 2) clutter-model-based temporal processing of the receive
beamformer outputs, and 3) adaptive 2-D receive array processing. The main objective
is to improve detection of surface targets and small airplanes which are masked by
clutter Doppler spreading induced by ionospheric propagation.
2. BACKGROUND AND TECHNICAL APPROACH
A critical function of over-the-horizon radar is to discriminate surface target returns
from sea clutter by exploiting their nominally different Doppler frequency spectra.
Because surface targets have much slower speeds than aircraft, much longer coherent
integration times (CIT’s) are required to resolve their Doppler shifts from that of the sea
backscatter. Under ideal scattering and ionospheric conditions, the sea surface return
consists of narrowband Bragg lines whose width in the estimated Doppler spectrum is
inversely proportional to the CIT. However, in practice, even modest ionospheric
variability over the CIT can frequency modulate Bragg lines returns over a band that is
much greater than the Doppler resolution cell width. Temporal methods to mitigate
clutter in this complex environment have included various frequency demodulation
methods; such as Bragg line sharpening [1], as well as so-called high resolution AR
spectral estimation techniques [2,3]. Unfortunately, previous temporal approaches have
yielded only marginal improvements in surface detection, chiefly because the sea clutter
and/or surface target is often too nonstationary to estimate the required demodulation
parameters or high-order AR model parameters. Spatial methods to mitigate clutter
have included space-time adaptive processing (STAP) at the 1-D receive line array [4],
elevation nulling with an auxiliary line array, and adaptive azimuthal nulling. The
difficulty faced by each of these approaches, however, is that the clutter returns are
spatially non-homogeneous in range, azimuth, and elevation. This makes it exceedingly
difficult to obtain signal-free training data using, for example, neighboring range bins.
In the proposed work, we plan to overcome the limitations of previous methods by
judicious modeling of the environment and clutter sources. Specifically, we propose to
pursue approaches for improving both spatial and temporal processing by exploiting
models which assume that much of the Doppler-spread clutter arrives via higher
elevation ray paths while less Doppler-spread targets are detected on lower elevation
angle paths at more nearly grazing incidence to the ionosphere. This model is
consistent with random phase-screen propagation modeling of the ionosphere [5].
Accordingly, the higher elevation angle rays traverse a moving phase screens with
random irregularities and the low elevation angle rays see essentially a constant phase
screen. Dividing the ionosphere into azimuthal segments, the received field may be
computed as the sum of far-field contributions from smaller, finite-length phase screens
over a set of azimuthal beam directions. The bulk phase screen motion is East-West
with approximately constant speed associated with the ionospheric electrojet. The
scale of irregularities in these phase screens varies from as little as 100's of meters to
10's of km. with smaller scale roughness causing higher Doppler spreading. To illustrate
raypaths responsible for Doppler spreading, Figure 1 shows propagation through three
phase screens corresponding to three different azimuths.
p-1
p0
p1
Tx/ Rx
Figure 1: Example raypaths through multiple ionospheric phase-screens
Without loss of generality, assume the direction of ionospheric motion is parallel to the
antenna array axis. Figure 1 illustrates four different propagation paths that can occur
at the same slant range from the transmitter to the clutter patch and back to the
receiver. The first ray path goes out in the transmitter mainlobe comes back in the
receiver mainlobe via the constant low-elevation phase screen and is thus referred to as
the direct ray path. This constant phase screen path does not spread the radar signal
thus facilitating target detection against background noise. The second ray path goes
out via a transmitter sidelobe on a low-elevation path and comes back into the receiver
main beam through a high elevation angle path. This second path is thus Doppler
spread in the return direction. The third ray path goes out via the transmitter mainlobe
on a high angle path but returns via a low angle path through a receiver sidelobe. It is
Doppler spread due to the phase-screen on the outbound path. The fourth ray goes out
and comes back on high elevation angle paths and thus suffers greater attenuation due
to two-way phase-screen scattering. The key point here is that receiver sidelobe control
can only mitigate Doppler spreading on the third path in this example. It cannot
suppress Doppler spread clutter on the second path corresponding to power scattered
on the inbound path arriving in the receiver mainlobe direction. It is for this reason we
believe previous receiver-only adaptive spatial processing [6] has met with limited
success.
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To mitigate Doppler-spread clutter which goes out on a transmitter sidelobe and
comes back in the receiver mainlobe, we propose developing methods for adapting the
transmitter array pattern so that power is minimized in directions which make the largest
contributions to clutter at Dopplers in the neighborhood of the nominal Bragg line
locations. The first step in solving this problem is to model the space-time steering
vector at the receive array elements for a return due to scattering from a downrange
clutter patch as a function of the transmitter array weights. This analysis has been
performed for microwave radars in the MIMO radar literature under line-of-sight
conditions [7]. Also in recent work, we have studied related bistatic clutter models for
airborne STAP and multipath active sonar channels [8]. The extension to multipath
models, e.g. as illustrated in Figure 1, will be performed in the proposed work. Given
space-time steering vectors, statistical models for the received Doppler-spread clutter
covariance matrix can be postulated as a function of the ionospheric ray paths and
phase screen statistics, down-range surface backscatter properties, and of course the
complex transmitter array weight vector which specifies the gain and phase for each
element. The proposed research will involve jointly optimizing the transmit and receive
array weight vectors to minimize clutter power in Doppler space. This joint optimization
will need to take into account inherent delays in communication between the receiver
and transmitter sites. It is expected that the ultimate performance metric will be related
to sub-clutter visibility (SCV), i.e. optimization of transmit array weights to maximize
SCV.
Although adaptive transmit array pattern control should improve HF sub-clutter
visibility, an OTH radar system required to test such algorithms is not currently
available. Thus evaluation of HF MIMO methods will mainly be performed via
simulation studies. To supplement simulations, however, we expect to be able to test
such algorithms using an experimental microwave MIMO radar developed at MIT
Lincoln Laboratory by Frank Robey and his group [7]. One experiment we have
discussed with Dr. Robey would consist of using the microwave MIMO radar to detect
targets on the opposite side of a freeway adjacent to Lincoln Labs. In this scenario,
propagation through vehicular traffic on the freeway would be analogous to propagation
through a moving ionospheric phase-screen. In such an experiment, the transmit array
pattern would ideally be adapted fast enough so that it's nulls would track large
scatterers (i.e. vehicles) as they contributed to Doppler spread clutter in the receiver
mainbeam.
While adaptive MIMO spatial processing on transmit and receive holds promise for
next generation advanced OTHR, improved temporal processing of beamformed data
has the advantage that it can be easily transitioned to existing PC-farm based OTHR
hardware. In recent work, we have developed time-varying autoregressive (TVAR)
processing which shows promise in reducing the Doppler spread of mainbeam clutter
[9]. Previous temporal approaches for clutter mitigation assume the effects of
ionospheric motion on the Doppler spectrum are slowly varying. In reality, however,
quite abrupt changes in the clutter return can be observed as shown in Figure 2 in
which the spectrum changes dramatically between time points 75 and 90.
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Figure 2: Time Varying OTHR Clutter Time Series
Conventional processing of these sharp transitions result in considerable spreading of
the Doppler clutter spectrum, thus reducing sub-clutter visibility both interior and exterior
of the Bragg line frequencies. In the recent work, we have employed fast time-varying
AR processing to unmask targets in the presence of highly varying clutter statistics. An
example of the improved detectability of weak targets in elevated clutter is shown in
Figure 3 where a synthetic target has been injected into real ROTHR clutter data.
Observe the significant improvement in target detectability evident in TVAR versus
conventional Doppler processing. While TVAR offers improved performance when
ionospheric motion induces fluctuations in the clutter return which fit a modified AR
model, it remains unclear as to what percentage of the time this holds true. Thus while
validation of TVAR over larger datasets is being performed, we are proposing to
examine other temporal models for spread-Doppler clutter.
In this work, we propose to consider an alternative temporal model for mainbeam
clutter which assumes a priori knowledge of the underlying sea clutter Bragg spectrum
which is then used to estimate a Doppler-spread clutter covariance matrix. The idea
here is to exploit the fact that the Bragg sea clutter spectrum is inherently different from
that of targets. In particular, the frequency separation of the Bragg lines is quite
accurately known to be multiples of 0.1 fc where fc is the operating frequency in Mhz.
Unlike previous previous Bragg line sharpening methods, the approach proposed here
is to estimate the Doppler-spread clutter covariance matrix as a Hadamard product of
the undistorted sea-clutter and ionospheric spreading sequence covariance matrices.
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Clutter return modeled as abruptly varying TVAR process
Simulated TVAR Doppler spectrum
Conv
10
TVAR
target
log mag in dB
0
-10
-20
-30
-40
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Doppler in Hz
Figure 3: TVAR vs. Conventional Processing on OTHR data
with Injected Target
The resulting model-based clutter covariance matrix estimate can then be used in any
of the traditional adaptive methods such as the AMF (adaptive matched filter) or MV
(minimum variance) methods for clutter suppression. The success of this approach
depends on correctly estimating a low-rank subspace which spans the Doppler
spreading sequence. We propose to estimate this low-rank subspace (but not the
realization of the spreading sequence) using clutter from neighboring range bins around
the range-bin under test. A simulation example of this structured clutter covariance
based clutter mitigation approach is shown in Figure 4. Note that the AMF using the
structured covariance matrix estimated from a single range-bin of simulated data clearly
unmasks the target (indicated by the circle).
Simulations: CIT - 25s WRF - 5.2 Hz SNR=30 dB
Conventional and AMF Doppler spectrum
Conv
AMF
60
50
Log mag in dB
40
30
20
10
0
-10
-2.5
-2
-1.5
-1
-0.5
0
0.5
Doppler in Hz
1
1.5
2
2.5
Figure 4: Simulated Doppler spectra for conventional vs. AMF using structured
covariance method.
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Finally, looking beyond both adaptive MIMO systems and advanced temporal
processing, we propose to study the potential of large two dimensional receive arrays
for OTH radar. The motivation for 2-D on receive is to facilitate suppression of high
elevation angle returns. Previous attempts at elevation nulling have used an auxiliary
line array perpendicular to the main receive array. This arrangement was unable to
provide sufficient additional clutter suppression to warrant the increased cost. However,
recent studies have suggested a more extensive 2-D array, such as a sparse spiral
arrangement of sensors behind the main line array, would provide a significantly greater
capability for elevation control. In our work, we propose to study beamforming options
for 2-D OTHR receive arrays. Key elements of the study will be optimum design of
beamformer weight vectors with minimum sidelobe and grating plateau levels.
Moreover, in light of the model of Figure 1, it will be important that such 2-D array
beamfomers have nulls that can be easily steered in directions where the Doppler
spread sidelobe clutter is highest. In the final year of the project, we also expect to
extend algorithms for adaptive MIMO processing with linear arrays to the configuration
of adaptive transmit line array and 2-D non-uniformly spaced adaptive receive array. It
is this configuration which will likely facilitate the signal-to-clutter-to-noise ratio gains
needed for reliable wide-area surface target surveillance over extended operating
periods.
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