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On the Use of Circular SAR to Improve the Performance of Knowledge-Aided STAP

Nihad Al-Faisali alfaisalin1@udayton.edu

Nicholas Hopkins hopkinsn4@udayton.edu

Mansour Aljohani aljohanim1@udayton.edu

Junjun Huan huanj1@udayton.edu

Muhannad Almutiry almutirym1@udayton.edu Krupakar Reddy Samala

samalak1@udayton.edu

Alex Burwell burwella1@udayton.edu

Daniel Wetzel wetzeld1@udayton.edu

Michael C. Wicks mwicks1@udayton.edu

University of Dayton

Dayton OH USA

Abstract-- In order to enhance the Hybrid STAP

algorithm presented by Adve [1], a novel method to

supplement the knowledge base through the use of circular

SAR imaging and registration with Land Use and Land

Cover * and other data is presented herein. The proposed

method will enhance the performance of the embedded

non-homogeneity detector [2] present in Hybrid STAP.

Keywords— STAP, Circular SAR, Narrowband

Waveforms, Knowledge Aided Processing, Tomography.

I. INTRODUCTION

A well-known issue with space-time adaptive processing

(STAP) parameter estimation, e.g. the sample covariance

matrix, is that the training data may contain targets and / or

non-homogeneous clutter regions within the reference cells

[2,3]. Signal suppression and / or poor clutter rejection results

when the training data is contaminated by targets, or is not

homogeneous with the interference in the cell under test. One

possibility to overcome this limitation is to use Knowledge-

Aided techniques to improve the performance of STAP radar

[4]. However, using knowledge from databases such as Land

Use / Land Cover (LULC) or Digital Terrain Elevation Data *

(DTED) may result in errors due to the imprecise alignment of

the radar range cells with a priori data. A method to improve

the performance of Knowledge-Aided STAP is proposed

herein. Using STAP radar data which has been collected from

an arc of a “circular” flight path, the STAP radar data is

preprocessed to first produce a circular SAR image of the

surface clutter. This SAR image is then correlated with LULC

and DTED databases to produce a highly accurate registration

of the aircraft position and radar range cells with a priori

databases. With this improved registration, the effectiveness

of Knowledge-Aided techniques may be enhanced.

II. NON-HOMOGENOUS CLUTTER ENVIRONMENTS: A PARTIAL

SOLUTION

STAP implementations rely upon training data to calculate appropriate clutter estimates leading to the desired filter response; however, this training data may be of a non-homogeneous nature. If this is the case, traditional STAP algorithms may be desensitized by the variation in clutter power and / or discretes. Specifically, the STAP filter frequency response does not converge appropriately since heterogeneous clutter data exhibits statistical dissimilarities

i.e., differing realizations requiring different filter responses leading to incorrect null positions and depths [5]. One approach to meeting the challenges posed by processing with non-homogeneous training data, is the exploitation of prior knowledge. Knowledge-Aided Space-Time Adaptive Processing (KASTAP) utilizes a priori information to better understand the cell under test (CUT). Specifically, it allows for the selection of the most appropriate reduced-dimension STAP algorithm and training data for the CUT.

III. TYPES OF KNOWLEDGE-AIDED DATA AND DATABASES

In order to gain prior knowledge of a given sensing

environment, a number of databases are available for

exploitation. LULC provides information on a number of

different terrain features within an environment. For example,

LULC data may classify an environment as rural croplands, a

lightly populated littoral zone, or wilderness. The difference in

these features is a key contributor to understanding clutter

non-homogeneities, the effects of internal clutter motion, etc.

DTED consists of elevation data that we further use to best

exploit LULC data in KASTAP. The geometry of the aircraft

hosting the sensor provides information on the look direction,

orientation, heading, speed, and location of the platform. This

can then be used in conjunction with the aforementioned

databases to reference the sensing platform more accurately to

the known terrain features (e.g. towers, bridges, railroads,

river beds) in the environment. Data collected from previous

sorties along the same flight path may also be used to improve

clutter characterization and identify known interfering

sources. However, a problem still exists when registering the

aircraft position with this a priori knowledge. With respect to

LULC, DTED and Defense Feature Analysis Data * (DFAD),

for example, terrain features change over time and available

data may be based on outdated surveys. Furthermore, terrain

type and elevation data may be of a resolution that is

insufficient to provide the best aircraft position registration

information. A more reliable method of accurately registering

aircraft position to available databases is required.

IV. EXPLOITATION OF CIRCULAR SAR

Synthetic Aperture Radar (SAR) imaging techniques are

controlled by many factors. Among them, are frequency

parameters such as carrier frequency, signal bandwidth, and

dwell time, e.g. Doppler resolution. The signal bandwidth will

determine the range resolution, while the Doppler bandwidth

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will determine the azimuthal resolution, which is ultimately

limited to / 2D , where D is the aperture size. The second

factor is the scan path geometry. Each scan path has its

disadvantages and most suffer from foreshortening, layover,

and shadowing effects [6]. In this research, we focus on

circular SAR. The circular geometry path provides more

information through wide scan angles. The geometric diversity

of circular SAR obtains different response functions of the

investigation domain under different azimuthal look-angles,

which leads to improved image quality.

V. NARROWBAND WAVEFORMS FOR STAP

A moderate to narrow bandwidth waveform may naturally

be chosen in STAP based radar for applications such as

Ground Moving Target Indication (GMTI), [7]. In this case, a

primary concern is Minimum Detectable Velocity (MDV).

With a dominant clutter ridge, the ability of STAP to cancel

stationary clutter while preserving the response of slow

moving targets is key. In this case, there is no need to utilize a

wideband waveform, which would improve range resolution

but could degrade MDV. Ignoring the effects of internal

clutter motion, MDV is ultimately limited by equation (1) for

side looking airborne radar, [8].

sin( / 2)MDV v a (1)

where v is the platform velocity, and a is the azimuthal

beam width. Note, there is no obvious dependence on signal

bandwidth in equation 1.

VI. WAVEFORMS IN CLASSICAL SAR

SAR has attracted considerable interest as the number of

applications in geoscience, remote sensing, and wide area

surveillance continues to increase. The ability to effectively

collect data in severe conditions such as rain, cloud cover and

/ or darkness is considered to be a main advantage of SAR as

compared to other imaging sensors. SAR is usually

implemented by mounting, on a moving platform such as an

aircraft, a single beam-forming aperture from which a target

scene is illuminated with pulses of radio waves at wavelengths

anywhere from a millimeter to a meter or more. The many

echo waveforms received successively at the different antenna

positions are coherently detected and then post-processed to

resolve features in an image of the targeted region. SAR can

generally acquire a high-resolution image at a great distance

because the sensor’s spatial resolution is independent of range.

Due to concomitantly increasing demands for all weather

high-resolution imagery, day or night, SAR is currently a

focus in the acquisition of such data.

Airborne and spacebased imaging radar systems in

surveillance and reconnaissance have to meet increasingly

severe demands. The next generation of top level SAR

systems will comprise, among others, high resolution and

long-range imaging capabilities, highly sensitive GMTI and a

multitude of sophisticated operational modes. The realization

of these and other forthcoming radar capabilities demands the

solution of many technological and methodological problems.

The desired spatial resolution requires the management of

transmission and reception bandwidths of 2 GHz or beyond.

The aperture has to be realized as an electronically steerable

and reconfigurable phased array of antennas, with a finely

quantized true time delay feed network and multiple receive

channels. Wideband multichannel data acquisition results in

very high data rates and volumes. With respect to sensor

resolution and dynamic range, hardware calibration

procedures and precise measurements of platform motion are

indispensable. Furthermore, a wideband waveform, and wide-

angle scenario requires a thorough modeling of the image

formation process with respect to motion compensation and

focusing. Studies of wideband SAR traditionally focus on

imaging algorithms, scattering characteristics of targets, and

sidelobe interference suppression. However, an important but

often overlooked topic is the wideband SAR signal model that

is critical both for a better understanding of the limitation of

available imaging algorithms that are often based on the go-

stop-go signal model, and for obtaining fine resolution and

high-quality images with new wideband SAR algorithms.

There are two limiting assumptions in the go-stop-go model.

One is that the platform remains stationary when the signal is

being transmitted or received (intra-pulse), and second, the

signal velocity is not problematic (fill time effects, etc.).

However if the relative slant-range velocity of a platform and

signal bandwidth are large, these assumptions might not be

valid. Thus, there is a need to further investigate the go-stop-

go model, and to develop a new approach if necessary. In this

research, we restrict ourselves to narrowband waveforms for

wide-angle circular SAR, thus alleviating said problems.

VII. TOMOGRAPHIC TECHNIQUES TO IMPROVE IMAGE

RESOLUTION

Circular SAR is a monostatic mode that may be expanded

to include tomographic image formation techniques. In this

research we apply monostatic tomographic radar imaging

algorithms to provide more spatial degrees of freedom while

using less bandwidth, as STAP radar waveforms are used.

Circular SAR resolution is determined by bandwidth and by

sensor platform geometry. The flight path of a circular SAR is

shown in Figure 1. This technique is similar to conventional

SAR. A sensor with polarization ˆ t

na collects N-samples along

a section of a 360-degree trajectory, as further described in the

references [9]–[11]. We sense a signal nES

r which is

dependent upon the target response, and measured at

N positions along a circular trajectory.

(2)

Where , nG r r is a Green’s function, and

2

0Qk is a

constant dependent upon radar parameters, especially

waveform and the radar range equation. By transforming

circular SAR processing via tomographic algorithms, we can

straightforwardly work in the narrowband signals domain.

From this starting point, we compute a surface image for

further exploitation by KASTAP.

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Figure 1: Circular SAR geometry.

VIII. CIRCULAR SAR FOR NON-HOMOGENEITY DETECTION

AND KNOWLEDGE-AIDED REGISTRATION

It is immediately evident that circular SAR may be used to

identify and classify cells with strong returns as non-

homogeneous. For example, if a wireless tower is in the scene,

it will produce a strong return, which can then be used to

augment the Knowledge Base (KB). This method would prove

especially valuable in the case of strong returns from objects

that are not previously characterized in the KB. However, a

more novel use of circular SAR in KASTAP is presented in

the subsequent discussion.

As described in previous sections and in [4], the use of

knowledge bases such as LULC, DTED, and DFAD can

greatly improve the performance of STAP. The proper STAP

algorithm can be selected through the use of a non-

homogeneity detector and the appropriate KB. For example,

identifying homogeneous regions of the clutter scene may lead

to application of the best Joint-Domain Localization (JDL)

algorithm [2] (proper degrees-of-freedom), whereas non-

homogenous regions may demand use of a least squares beam-

former in JDL. See details in [1]. The proposed method

presents the concept of using circular SAR to align the KBs

for better clutter data classification. As shown in the figure

below, an illustrative circular SAR image from [6] is used to

“layer” and align the KBs presented in [4]. This alignment

will improve the probability that the training data for the CUT

is correctly identified as either homogeneous or non-

homogeneous, moving or stationary, etc.

In order to properly align the KBs, it will be important for

the classification algorithm to identify dominant

characteristics in the SAR image. Examples of dominant traits

in the image can include permanent discrete reflectors

exhibiting a strong signal return, such as wireless towers, or

uniquely identifiable shapes, such as winding highways or

dominant buildings. If the alignment algorithm is targeting

strong, stationary reflectors, it will require multiple points to

align the image properly. Whereas the approach of identifying

shapes or boundaries will likely require the use of an edge

detection algorithm before classification. In all likelihood, the

classification and alignment algorithm will utilize a hybrid

approach. The estimates of performance gain using the

aforementioned data registration are presented below.

IX. POTENTIAL ISSUES

One potential issue with this concept is that the aircraft

must fly an arc of a circular flight path in order to produce the

high-quality image which is required for this technique. The

flight path may not be in agreement with other mission

requirements. Another issue is computational complexity. In

addition to producing an image, the registration of the image

with the KB is another daunting task. Still another issue arises

from uncertainty in flight path due to platform dynamics.

Figure 2: Illustrative example of a circular SAR image used to align knowledge bases.

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X. SAR AND STAP PERFORMANCE

In this section, we will provide two techniques to improve

STAP performance using circular SAR, and visa versa—both

of which are mentioned in [12]–[14]. These two techniques

include robust principal component analysis (RPCA)

illustrated in Figure 3, see [12], and high resolution and wide

swath (HRWS) imaging, presented in Figure 4, see [13, 14].

First, an RPCA based methods applied in the range-Doppler

domain is an important analysis tool. The concept in RPCA is

to separate stationary clutter from moving targets in

multichannel SAR data. We can easily distinguish between

strong clutter and moving targets. Figure 3 conceptually

shows how the RPCA is applied to a multichannel SAR, and

how it makes a distinction between clutter and moving targets.

Figure 3: RPCA algorithm to distinguish between strong

clutter, moving targets, and nonhomogeneities in SAR.

The second technique, HRWS, is an algorithm that

analyzes the scaling factor of the radar signal-to-noise-ratio

(SNR) and the azimuth-ambiguity-to-signal-ratio (AASR).

Along with these analyses, the error between the channels

must be characterized. Three types of error in each channel are

considered. They are: gain, phase, and position error. Figure 4

demonstrates, via simulation analysis, their impact on STAP,

and unambiguous azimuth reconstruction in SAR.

Figure 4: Simulation illustrating HRWS analysis of SNR

scaling factor in: (a) phase, (b) position, and (c) gain error.

Analysis of AASR v. channel errors in: (d) phase, (e)

position, and (f) gain are included. See detailed discussions

and original work in [14].

In Figure 5, the result from MCARM radar data analysis

demonstrates the use of circular SAR to improve the

performance of Knowledge-Aided STAP (see CSAR +

KASTAP in Figure 5). In this example, data from acquisition

471 is also analyzed via MSMI + NHD (modified sample

matrix inversion and non-homogeneity detection) as discussed

in the Hybrid STAP algorithm of [1]. The results are

compared to CSAR + KASTAP. Performance is enhanced via

the use of prior knowledge and circular SAR analysis of the

aforementioned narrowband MCARM data, with limited

RPCA and HRWS analysis. Multiple views of the scene under

investigation spanning a wide along-range profile (and angular

sector) collected over multiple coherent dwells are required to

best align LULC, DTED, and DFAD with circular SAR

imagery. The purpose of this alignment is to increase

knowledge and the quality of available training data for

covariance matrix estimation via the excision of outliers, thus

extending foundational research in the MAP STAP concept

presented in [15]. From this analysis of post-alignment

information, a significant improvement in detection

performance and false alarm control is achieved.

Figure 5: Measured radar data analysis. Comparison of

MSMI + NHD to CSAR + KASTAP. The target (MTS) is

at 17.4 miles and strong clutter at 22.6 miles.

XI. CONCLUSION

The concept of using circular SAR images to improve the

performance of the non-homogeneity detector in the Hybrid

STAP algorithm [1,4] has been presented, including

qualitative results. Through the use of this imagery, the utility

of these KBs, which are critical to KASTAP, are greatly

enhanced. A more reliable detection of non-homogeneous

clutter, including clutter edges and clutter discretes, results in

the selection of improved training data for covariance

estimation in the CUT, and could result in techniques aimed at

reducing the degrees of freedom—for example, intelligently

selecting the size of the Localized Processing Region (LPR) in

the JDL algorithm, [2]. Additional research is required to

statistically quantify the performance of the circular SAR

enhanced KASTAP algorithm presented in this paper.

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Dedicated to the Memory of

Larrell Walters

1956 – 2016.

* LULC, DTED, and DFAD are U.S. Geological Survey and

National Geospatial-Intelligence Agency data bases.