A SPA'I'IAIJSPECTRAL DOMAIN MICROWAVE COAL SEAM IMAGING SENSORPROGRESS IN SIGNAL PROCESSING 

ROBERT L. CHUFO 

Pittsburgh Research Center 

U.S. Bureau of Mines 

P.O. Box 18070 

Pittsburgh, PA 15236 U.S.A. 

Abstract-The paper, "An Electromagnetic Roof and Rib Thickness 

Sensor" pmwntcd at The 12th WVU International Mining 

Electmtechnology Conference in 1992, presented the results of roof 

d thiclcness meuurements in underground mines and rib coal 

thickness measurements in highwall mines. The microwave sensor 

described uses a unique spatial modulation scheme created by 

antenna motion, along with fiqucncy domain signal processing, to 

solve the problem of media, target, and antenna dispersion. This 

paper fuRher describes the advantages of the unique frequency 

domain signal processing technology chosen by the U.S. Bureau of 

Mines in extending previously reported onedimensional thickness 

mmurement technology to the generation of a full threedimensional 

underground image of the underground environment. 

The electromagnetic signature of a media, whether it is coal or 

other geological material, is its permeability and permittivity and the 

effect on the velocity and amplitude of an electromagnetic wave as it 

passes through. Highquality imaging cannot occur without the 

identification of, and correction for, the permittivity and 

permcability of each layer of a multilayer geological deposit. 

Clutter, or any signal that can interfere with target discrimination, 

usually comes from objects in the field of view for which there is no 

design control. These are nuisance targets with possibly very high 

contnrsts compared to the real target. The only proven tool to 

reduce clutter is to increase spatial, spectral, and polarization 

diversity. This paper will describe some techniques being used by 

the U.S. Bureau of Mines to increase the information content of both 

singlc-dirnensional and three-dimensional imaging systems through 

the identification of media distortion mechanisms made possible by 

the use of spatiaYspectra1 sensor technology. These techniques will 

pennit high quality imaging in an environment where the data is 

corrupted by dispersion and clutter. 

Parseval's relation states that frtquency is just a time derivative. 

This has resulted in two sets of Maxwell's quations, one set for the 

time domain (TD) and an quivalent set for the frequency domain 

0). There are no physical phenomena or variables in one domain 

that do not have corrtaponding physical phenomena or variables in 

the other domain. A TD sensor, like pulse radar, senses reflections 

vs. time. An FD sensor, like a synthetic pulse sensor, measures a 

number of frcquencits and integrates the reflection data to 

theoretically get the same reflections vs. time as dots the TD radar. 

However, in a practical sense, we must ask, Art these two hardware 

architectures qual? For subterranean imaging, FD sensors have an 

overwhelming advantage over TD sensors for the following reasons: 

1. The true imaging objective is the spatial distribution of 

permittivity and permeability, not reflection amplitude vs. time. In 

a subtemurcan media, electromagnetic time-space is not linear. 

Highquality imaging cannot occur without the identification of and 

correction for permittivity and permeability. 

2. Subterranean imaging is a complex problem rquiring large 

data files and complex signal processing. FD sensors produce more 

object plane data with more degrees of freedom than TD sensors. 

3. FD sensors produce data in a format compatible with theory. 

TD data must be transformed into the FD before dispersion 

corrections can be made. Windowing data in preparation for a 

transform will cost about a third of the data. 

4. Spectral bandwidth is quivalent to information. But for TD 

sensors, limited by analog to digital converter (ADC) sample rate, a 

smaller pulse-width means less information. As a result, FD sensors 

can produce more bandwidth than equivalent TD sensors. 

5. All antennas are dispersive. If a TD sensor and an FD sensor 

use the same antenna, the FD sensor will get more than twice the 

bandwidth out of the same antenna than does the TD sensor. 

GENERAL 

Subterranean Imaging 

The electromagnetic signature of a media is its permeability and 

permittivity and the effect on the velocity and amplitude of an 

electromagnetic wave as it passes through. The reason so many of 

the boundaries in a TD image look like ringing and are blurry, is 

because of the difference of permittivity (dispersion) at the boundary 

of materials having different dielectric constants. In TD processing 

the dispersion is almost impossible to sort out, but in FD processing 

the problem is a straightforward inverse scattering problem. 

Processing Com~lexity 

There are many problems with imaging and identifying the 

permittivity of underground targets. The antenna, media, and target 

are all dispersive; the media are usually inhomogeneous and lossy, 

and the target may have very little dielectric contrast comparcd to 

the bulk media in which it is buried. Sometimes the targets are 

either lying on the surface or are buried just below. Therefore, the 

surface reflections can never be completely separated from the target 

in the TD. 

We can postulate that multipath in TD, is the same as dispersion 

in FD. If two or more paths are too close to be separated by time 

(i.e., not enough bandwidth is available for separation) they can still 

be separated in the FD if the dispersion signature is known. 

In addition, the surface acts like a reflector and the antenna sees 

itself as a target. Like the old multimetcr problem when the mctcr 

impedance was part of the measurement, antennas interad with the 

unknown ground impedance. To make matters worse, the 

mursurement is distorted by antenna and media dispersion. 
Clutter, or my signal that can interfere with target discrimination, 

usually comer from objects in the field of view for which there is no 

design control. There are nuisance targets with possibly very high 

contmsta compared to the ma1 target. The only proven way to 

reduce clutter is to increase spatial, spectral, and polarization 

diversity (i.e., reduce the antenna beamwidth, use a range gate, and 

improve E field spatial purity). 

When the spectral bandwidth of a TD sensor increases, the 

infonnation content decreases because the hardware limits the 

maximum data rampling rate. If the TD hardware is optimum, then 

only one puke can be sampled because only one pulse is returned 

from the stationary target in the object plane. When TD people talk 

about avenging, they are talking about ovenwming the moving 

clutter within the transmitter and receiver. One cannot talk about 

KTB noise, (the "noise floorn where K = Boltzmann's constant, 

T = noise temperature in degrecs Kelvin, and B = noise bandwidth 

in hertz) which is random and can be averaged, bccause the object 

plane is so close to the antenna and the transmitter power level is so 

high that KTB noise should never show up in subterranean imaging. 

Increasing the information content of an image will not help 

unless all the distortion mechanisms are also identified. Equations 

written in the FD are prefermi to TD equations because FD 

equations more tasily characterize distortion caused by dispersion 

and clutter. 

S~cctral Bandwidth 

When the sensor is a ground penetrating radar (TD sensor), the 

typical means of increasing the bandwidth is to reduce the pulse 

width. This usually makes things worse because antenna dispersion, 

coupled with media and target dispersion, causes time and space 

distortion problems too complex to sort out in the TD. 

It is interesting to hypothesize that more frequency diversity (i.e., 

a shorter equivalent pulse) can be achieved by either (1) modifying 

the antenna design so that the phase center, in FD phase space, 

moves in the ditection of propagation to counteract the dispersion 

delay (the phase of SolSlo(f)) within the antenna, or (2) the phase of 

each frquency, for the stepped CW data, can be shifted at the 

output of the mxiver to counteract the dispersion delay. For all 

common dispersive and anisotropic targets, the two techniques art 

the same. However, due to the finite dynamic range of the meiver, 

the two techniques are not the same for antenna gain correction. 

Phase can be corrected in the software, but gain must be comtcd 

by attenuating the transmitter midband power output before the 

receiver refemcc power splitter. Attenuating transmitter power will 

not degrade system performance by adding noise to the system as 

received signal strength is in excess of the noise floor. In fact, then 

is merit in reducing the transmitter power below the present 0 dBm 

level to further reduce any clutter generated by the third harmonic of 

the transmitted signal. Trarrsmitter power in the FD system is not 

an issue as signals returned from the target are well above the noise 

floor. 

In addion to the external clutter, there are serious problems with 

internal clutter. Internal clutter is multipath within the sensor or 

paths between antcnna and outside sensor parasitics for which there 

is some design control but have not fully mitigated or understood. 

Internal clutter is difficult to measure and is usually overlooked 

because designers have evoked the linear transform principle to 

declare that my unknown but fixed internal multipath will affect the 

target and nuisance target equally. Research has shown that this 

assumption is incomt. The antenna parasitic can produce qwiharmonic couples with the media that cannot be solved with linar 

techniques. 

When the antenna cross-section coefficient is tested (see fig.1) 

one finds that the error terms have a missing harmonic. This 

passive cross-section is caused by the antenna parasitic and the 

asymmetry of the impinging field and its effects are quasi-harmonic 

and much more complex than a single power series. Fig. 2 shows 

how the wave might reflect from the parasitic on the fvst bounce but 

enter the coaxial cable on the scwnd bounce. The usual problem is 

that some of the backplane or reflector will act like a mhr to the 

half space but not excite the antenna on the first bounce. 

Fig. 1. Antenna Cross-Section (coefficient) with Parasitic Errors. 

Fig. 2. Monochromatic Phase-Space for a Passive Sca~~rcr. 

Antenna Dismrsion 

Fig. 3a shows spatial Bessel data (and spectral data a through e) 

of a single target before and after antenna normalization. Each polar 

plot is the spatial history for one frquency. The top row (600-1400 

MHz) is how the data would appear at the ADC if only the S, 

reflection was removed. The bottom row is the same data after 

compensation for the two-way antenna gain SolSlo. 

Fig. 3b art data that have had the reflection and gain removed 

after the ADC, and it can be seen that the 600 MHz data are noisy 

compared to 800 MHz (antenna resonance) data. This is bccause the 

bulk of the dynamic range was used to remove reflection and gain 

before the first nceiver amplifier. With an antenna normalization 

circuit (synthetic interferometer) there will be a uniform signal-tonoise ratio for all frequencies. 
Conrider a synthetic switch hypothesis. Most monostatic radar 

dcsignen use a tmnsmit/rcceive switch at the input to the receiver to 

gate out the antenna rtflection Sdf). This allows maximum 

dynamic range for targct response in the receiver. However, this 

condition occurs at only one frequency, and if the antenna is too 

close to the target the switch must be very fast. This hypothesis 

assumes that therc exists a passive antenna netwprk that will cancel 

all antenna rcflcctions into the receiver for all frequencies, thus 

eliminating the nd for the transmitireceive switch or the minimumdistanct-to-target rcquirtment. 

An antenna normalization circuit solves two problems, (1) it 

corrects the antenna dispersion plus any other distortions found in 

calibration, and (2) it allows a receiver architecture to be designed 

without a switch. 

CONCLUSION 

The imaging goal of measuring the permeability and permittivity 

of a target, whether it is coal or other geological material, is best 

mct with FD sensor technology. Highquality imaging should be 

done with the measurement technology that provides the greatest 

amount of information about the object plane. Frcquency domain 

measurement technology has the most degrees of freedom and has a 

data-gathering format compatible with irnage-processing theory. 

With high quality data, the boundaries of materials having different 

dielectric constants and the resulting dispersion that causes blurry 

images can be easily resolved. 

Nomenclature 

Area of a spatial modulation spiral area. 

Vectorial spectrum for E of incident field. 

Incident wave-amplitude in antenna feed 

tmnsmission line. 

Vectorial spectrum for E of scatttrtd or radiated 

field. 

Emergent wave-amplitude in antenna feed 

transmission line. 

Constant (2.718281828). 

Frequency. 

Imaginary number (j * = -1) . 

Wave number (ZnIA, fret space). 

Measured input reflection coefficient. 

Recursive reflection terms. 

Scattering matrix parameter. 

Antenna reflecting characteristic into coaxial cable. 

Antenna receiving characteristic from free space to 

coax. 

Antenna transmitting characteristic from coax to free 

space. 

Antenna reflecting characteristic from space to 

space. 

Vector spatial delay with diffraction. 

Time. 

Velocity. 

Distance of antenna to dielectric. 

Characteristic impdance. 

Wave length. 

Reflection coefficient. 

Angle. 

Spatial delay factor. 

Fig.3. Antenna Normalization in Gain and Phase: top row, with Sm reflection removed, bottom row, after two-way antenna gain 

compensation.