UHF RECEIVERS

An ultra-high frequency (UHF) receiver receives radio signals with input frequencies between 300 MHz and 3000 MHz. Ra­dio waves in this part of the spectrum usually follow line-of – sight paths and penetrate buildings well. The natural radio environment is significantly quieter at UHF than at lower fre­quencies, making receiver noise performance more important. UHF antennas are small enough to be attractive for vehicular and hand-held applications, but are not as directional or ex­pensive as microwave antennas. Many radio services use UHF, including land mobile, TV broadcasting, and point-to – point. The point-to-point users are rapidly disappearing, and the greatest current interest in receiver design centers on cel­lular and personal communications system (PCS) applications in bands from 800 MHz to 950 MHz and 1850 MHz to 1990 MHz.

UHF receiver design was once a specialized field incorpo­rating parts of the lumped-circuit techniques of radio fre­quency (RF) engineering and the guided-wave approach of mi­crowave engineering. Recent trends in circuit integration and packaging have extended RF techniques to the UHF region, and there are few qualitative distinctions between UHF re­ceivers and those for lower frequencies. UHF receivers differ from their lower-frequency counterparts primarily by having better noise performance and by being built from components that perform well at UHF.

UHF RECEIVER OPERATION The Role of a UHF Receiver in a Radio Communications System

Radio frequency communications systems exist to transfer in­formation from one source to a remote location. Figure 1 is a system block diagram of a simple radio communications sys­tem. A transmitter takes information from an external source, modulates it onto an RF carrier, and radiates it into a radio channel. The radiated signal grows weaker with dis­tance from the transmitter. The receiver must recover the transmitted signal, separate it from the noise and interfer­ence that are present in the radio channel, and recover the transmitted information at some level of fidelity. This fidelity is measured by a signal to noise ratio for analog information or by a bit error rate for digital information.

Receiver Characteristics

The following characteristics describe receiver performance:

• Sensitivity is a measure of the weakest signal that the receiver can detect. The ideal receiver should be capable of detecting very small signals. Internally generated noise and antenna performance are the primary factors limiting the sensitivity of UHF receivers.

• Selectivity describes the receiver’s ability to recover the desired signal while rejecting others from transmitters operating on nearby frequencies.

• Stability is the receiver’s ability to remain tuned to the desired frequency over time with variations in supply voltage, temperature, and vibration, among others.

• Dynamic range is a measure of the difference in power between the strongest signal and the weakest signal that the receiver can receive.

• Image rejection measures the receiver’s ability to reject images, incoming signals at unwanted frequencies that can interfere with a wanted signal.

• Spurious response protection measures the receiver’s free­dom from internally generated unwanted signals that in­terfere with the desired signal.

Vertical and Oblique Propagation

Before considering the behavior of a radio signal in a magne – toionic medium, we will state three theorems that relate oblique and vertical incidence propagation as depicted in Fig. 6. The first is the secant law, which relates the vertical-inci – dence frequency fv reflected at B to the oblique-incidence fre­quency fob reflected at the same true height. A typical deriva­tion of this relation is given in Ref. 5, and it is usually written as

JfL

2n m

■Bn& 2.80 x 1010Bn

fH —

(10)

and the angular gyrofrequency is given by le I

coH = — B0 ъ 1.76 X 10nB0

(11)

fob — fv sec ф0

(7)

Since electrons are much less massive than ions, the electron gyrofrequency affects the propagation of HF waves in the ionosphere more than the ion gyrofrequencies. For example, since B « 0.5 X 10-4 Wb/m2, the electron gyrofrequency is «1.40 MHz, which falls at the upper end of the medium wave band.

The Dispersion Relation. Using the recommended URSI (In­ternational Union at Radio Science) notation, the magne – toionic dispersion equation for a radio wave in a homoge­neous, partially absorbing ionized gas upon which a constant magnetic field is impressed is given by

The secant law, then, relates the two frequencies fv and fob reflected from the same true height (the distance BD in Fig. 6).

In order to determine sec ф and fob values from vertical – incidence soundings (which measure the virtual height h’), we need two more theorems. Breit and Tuve’s theorem states that the time taken to traverse the actual curved path TABCR in Fig. 6 at the group velocity vg equals the time nec­essary to travel over the straight-line path TER at the free-

E

Figure 6. Plane geometry describing vertical and oblique iono­spheric propagation.

n2 = 1 –

(1 – jZ) –

2(1 – X – jZ)

1/2

Y 4

IT

+ Yl2

±

4(1 – X – jZ)2

where

n = complex refractive index = (u _ jx) ш = angular frequency of the exploring wave (rad/s)

700

Sec ^(corrected)

3.0 2.0

16 20 24 28 32 36 40 44 48 52 56 60 70 80

Angle of departure (deg)

Figure 7. Logarithmic transmission curves for curved earth and ionosphere, parametric in distance between transmit­ter and receiver.

1.0

600

1=

is 500 ra

<d 400 sz

я

3 300 200 100 0

1

+ Yl2

=F I 7

2

(15)

wN = angular plasma frequency wH = angular gyrofrequency = B0|e|/m (rad/s) wL = longitudinal angular gyrofrequency = (B0|e|/m) cos в wT = transverse angular gyrofrequency = (B0|e|/m) sin в X = wN/w2 Y = wH/w Yl = wL/w YT = wT/w Z = v/w

в = angle between the wave-normal and the magnetic field inclination

The Polarization Relation. We begin by defining the polar­ization ratio R as

R = —Hy/Hx = Ex/Ey (13)

Then we can write the double-valued polarization equation as

In the upper F region of the ionosphere where the electron – ion collision frequency is very low, we may simplify the dis­persion and polarization equations by dropping the Z term (since v « 0). Equations (12) and (14) then become (for no absorption)

If we further simplify Eq. (12) by dropping the Y terms (no magnetic field), then we obtain n2 = 1 — X, which is equiva­lent to Eq. (1).

2 1 — X — jZ I 4 (1 — X — jZ)

2X (1 — X)

n2 = 1 —

n — 1

(14)

(16)

and

Y 4

YT

1/2-

X

2(1 — X) — YT2 ± [YT4 + 4YT2(1 — X)2]1/2

According to magnetoionic theory, a plane-polarized EM wave traveling in a medium like the terrestrial ionosphere will be split into two characteristic waves. The wave that most closely approximates the behavior of a signal propagating in this medium, without an imposed magnetic field, is called the ordinary wave, and the other is called the extraordinary wave. These terms are taken from the nomenclature for dou­ble refraction in optics, although the magnetoionic phenom­ena are more complicated than the optical ones. The ordinary wave is represented by the upper sign in the polarization Eq. (14), except when the wave-normal is exactly along the direc­tion of the magnetic field. Anomalous absorption occurs for the extraordinary wave when its frequency equals the elec­tron gyrofrequency (fH = |B| e/me « 0.8 to 1.6 MHz). These frequencies lie in the medium-frequency (MF) band; conse­quently the absorption of the extraordinary wave [A « (f — fH)2] is large and the polarization of the transmitted wave is important in the determination of the fraction of the incident power that goes into the extraordinary wave. This is espe­cially true near the dip equator, where the magnetic field is nearly horizontal and the field is usually vertical.

In addition to anomalous absorption effects near the elec­tron gyrofrequency, the wave may also experience significant lateral deviation. This is illustrated for vertical and oblique propagation in Sections 11.2.2 through 11.2.4 of Ref. 5.

If Eq. (16) is recast as a funtion of w and we define f(e) = j(sin2 e)/cos в and wc = (B0|e|/m) f(e), then it will be seen to describe an ellipse. The quantities f(e) and wc play an impor­tant part in the description of the polarization behavior of waves in magnetoionic theory. The magnitude of wc is inde­pendent of frequency, but varies with the angle between the wave normal and the magnetic field, в, whereas the sign of wc depends on the sign of the charge e and on the direction of the magnetic field. For longitudinal propagation wc = 0, and for transverse propagation wc ^ “.In the case where X = 1, the quantity wc primarily determines the polarization of the wave. A very complete discussion of R as a function of X and of the variation of the polarization ellipse is given in Ref. 21.

A more complete understanding of the behavior of EM waves in the terrestrial ionosphere may be obtained by em-

(23)

X

(24)

ploying two approximations. The quasilongitudinal (QL) ap­proximation applies when the wave is propagating nearly par­allel to the geomagnetic field, and the quasitransverse (QT) approximation applies when the wave propagates in a direc­tion nearly normal to the geomagnetic field. References 21 and 22 contain extended discussions of the QL and QT ap­proximations:

QT: YT4 » 4(1 – X)2Yl2

QL: YT4 << 4(1 – X)2Y2

LOGIC-CIRCUIT APPLICATIONS

Introduction

The growth of technology demands larger, faster, and more efficient integrated circuits (ICs) on a single chip. Engi­neers and scientists have answered this demand by con­tinually scaling down the size of transistors in fabrica­tion. As a result, the newer IC chips have more transis­tors, faster switching speed, and less power consumption due to the size and supply voltage reduction. As this reduc­tion in size continues, engineers are looking for alternative approaches. New and preliminary applications of resonant tunneling diodes in (RTDs) IC technology suggests that this device can help electrical engineers to design faster logic gates with fewer active devices (11,12). In addition to high speed, logic circuits using RTDs have fewer active elements and therefore are less complex in design and con­sume less power. Since the RTD is a latching structure, it can replace traditional latching structures; this would fur­ther reduce the total number of gates in the design.

Logic Circuits

Recent developments in semiconductor technology, such as molecular-beam epitaxy, have made it possible to integrate RTDs with conventional semiconductor devices. Here, the principle of operation of a logic circuit with RTDs and het­erojunction bipolar transistors (HBTs) is discussed (11). Figure 11 shows such a RTD + HBT bistable logic circuit (11). There are m inputs IN1v.., INm, and one clock tran-

LOGIC-CIRCUIT APPLICATIONS

Figure 11. Schematic of the bistable RTD + HBT logic gate (11).

sistor CLK. All transistors, connected in parallel, are driv­ing a single RTD load. Input transistors Q1 to Qm which are either on or off depending on the gate voltage, yield collector currents of Ih or zero respectively. The clock tran­sistor will have two current states, Iclkh and Iclkq respec­tively for the high and quiescent conditions respectively. A global reset state is when all collector inputs are zero. Figure 12 shows the operation of this circuit. It shows the RTD load curve and all possible collector currents. It shows two groups of possible input currents, one for the quies­cent clock transistor current (CLK Q), and the other for the high clock transistor current (CLK H). In each of the two groups, all possible total input-transistor collector cur­rents are shown. Considering there are m input transis­tors, this means there are m levels of total transistor col­lector current one for each. When the clock transistor cur-

LOGIC-CIRCUIT APPLICATIONS

rent is Iclkq, Fig. 12 shows that there are two stable states, a low and a high, for any possible input combination. When clock transistor current is Iclkh, Fig. 12 shows that there is exactly one, low stable state for any n or more high in­puts. In other words, there is exactly one stable operating point when the RTD current is nIh + Iclkh or more. The op­erating sequence is as follows: First, reset the logic gate by resetting all collector currents to zero; this will cause the output to go high. Second, remove the reset signal and set the clock to high. Then, if n or more inputs are high, then the logic will go to low; otherwise it will be high. Third, change the clock to its quiescent level. This will re­duce the clock current while the output logic remains the same.

Consider a three-input logic circuit. Now suppose n is 1. The output f(x1, x2, x3) is low (logic zero) if and only if one or more of the inputs x1, x2, x3 are high. This by definition is a three-input NOR gate. Now assume n is 3. Then the output f(x1, x2, x3) is low (logic zero) if and only if all three inputs x1, x2, x3 are high. This by definition is a three-input NAND gate. For n = 2, we obtain an inverted majority or inverted carry function.

This design scheme can be easily extended to consider weighted inputs. In such a case the output will be low if w1x1 + w2x2 + … + wmxm > n. The circuit of Fig. 11 can be used to implement the above weighted function when the collector current of each transistor is weighted by a factor. This can be accomplished by using different transistors

ILS CHARACTERISTICS

An instrument landing system (ILS) consists of ground-based transmitters and airborne equipment that provides lateral, along-track, and vertical guidance. The lateral signal is pro­vided by a localizer transmitter and the vertical signal is pro­vided by the glideslope transmitter. The airborne ILS receiver is capable of receiving and processing both the localizer and the glideslope signals. The along-track information is pro­vided by marker beacons (transmitters located along the de­scent path that provide a narrow vertical radio signal) or dis­tance measuring equipment (DME). The marker beacon receiver can be part of the ILS receiver or it can be a separate receiver (see Fig. 5).

The localizer beam is almost always aligned to guide the aircraft directly over the runway threshold. In certain situa-

ILS CHARACTERISTICS

tions, only a localizer signal is provided and no electronic glideslope signal is provided. In some localizer-only situa­tions, the localizer signal is not aligned to the runway but instead provides guidance to some location from which the pilot has other means to complete the landing.

In some cases, the ILS DME transponder delay is adjusted so that the sensed DME distance is zero at the runway threshold instead of at the DME antenna. Such DMEs are known as biased DMEs and the bias is indicated on the ap­proach chart. There are two general arrangements of biased DMEs (see Fig. 6). In one case the DME antenna is located at the glideslope antenna and adjusted to read zero at the corresponding runway threshold. In the other case, the DME antenna is located midway between the two runway thresh­olds at opposite ends of the runway. In this case the single DME will support approaches from either direction and will read zero at both runway thresholds.

The localizer signal is transmitted on assigned frequencies between 108.1 MHz to 111.95 MHz. As shown in Fig. 5, the localizer antenna is usually located past the far end of the runway very near the extended runway centerline. The signal pattern are two main lobes on each side of the center line. The left lobe is predominantly modulated at 90 Hz and the right lobe is predominately modulated at 150 Hz. Along the center line, the two modulated signals are equal (see Fig. 7).

The localizer signal also extends backwards and is called the back course. This signal can be used for guidance but the modulation convention is reversed. There is no glideslope sig­nal provided on the backcourse region. When flying the back – course, either the equipment must reverse the localizer indi­cations or the pilot must recognize and fly the reversed indications.

The glideslope signal is transmitted on assigned frequen­cies between 328.6 MHz and 335.4 MHz. The glideslope an­tenna is located on the side of the approach end of the run­way. The glideslope signal consists of two main lobes on each

Glideslope and DME antenna •

Antenna beam pattern

Back

course

Localizer

course

ILS CHARACTERISTICS

Localizer

antenna

Localizer beam centerline

Runway

Glideslope

ILS CHARACTERISTICS

An omnidirectional antenna is used to receive the signal to aid in tuning the receiver. The ADF receiver determined the bearing of the station using the directional sensitivity of loop antenna. The loop antenna may be physically rotated to de­termine the bearing to the signal or the bearing may be deter­mined using electronic sensing of the signal strength from more than one loop.

400

Hz

ILS CHARACTERISTICS

Inner

marker

Middle

marker

Outer

marker

side of the desired glideslope path. The glideslope descent angle is usually three degrees. To provide obstacle clearance or to reduce noise, steeper glideslope paths are used. The up­per glideslope lobe is predominantly modulated at 90 Hz and the lower lobe is predominantly modulated at 150 Hz (see Fig. 8).

Marker beacons signals are transmitted at 75 MHz and are modulated at 400 Hz, 1300 Hz, or 3000 Hz. The transmit­ters are located along the descent path of to the runway. Fig­ure 9 shows the general arrangement of the beacons. When the aircraft passes over the beacons, the marker beacon re­ceiver detects the signal and provides an indication to the pi­lot of the passage. The exact location of the marker beacons is given on the approach procedure chart. Inner marker bea­cons are installed at runways with Category II and Category III operations.

In typical operation, the pilot maneuvers the aircraft to cross the localizer signal centerline. At this time, the localizer receiver provides an indication that the localizer signal is be­ing received and provides a lateral deviation indication show­ing the aircraft displacement from the centerline. Using the lateral deviation indication, the pilot steers to the localizer centerline until the glideslope receiver indicates reception of the glideslope signal. At that time the pilot has both lateral and vertical indications to guide the aircraft on the desired glideslope path. The marker beacons or DME indications pro­vide along-track indications of the progress of the descent.

MAKE THE MODEL I

The human can overcome a time delay and track visual targets with zero latency. This is nicely demonstrated by the smooth-pursuit eye movement system. We found that if our model was to emulate the human, it had to predict target velocity and compensate for system dynamics. The model accomplished this using a prediction algorithm. To help validate the model, a sensitivity analysis and a pa­rameter estimation study were performed.

Figure 10 shows our model for the human eye move­ment systems. Like the human, this model can overcome the time delay and track a target without latency. To do this, the model must be able to predict future target veloc­ity and compensate for system dynamics. In this section, a least-mean-square technique for predicting target velocity is described. After incorporating this prediction algorithm into the model, the model was studied to learn more about the model, and hopefully about the human. In particular, we performed a sensitivity and analysis of the predictor and then investigated how parameter variations affected the MSE between the predicted output and the actual tar­get waveform.

The TSAC Model

This section primarily examines the smooth-pursuit eye movement system. The earliest model for the smooth – pursuit system is the sampled data model developed by Young and Stark (27). Because of later evidence presented by Robinson (39) and Brodkey and Stark (40), the pursuit branch is no longer viewed as a sampled data system, but rather as a continuous one.

There is one physically realizable model capable of over­coming the time delay in the smooth-pursuit branch, the TSAC model developed by McDonald (18, 19). This model with the saccadic and smooth-pursuit branches and their interactions is shown in Fig. 10. The computer simulation that implements this model was written in the C language on a Unix computer system.

Referring to Fig. 10, the input to the smooth-pursuit branch is retinal error, which is converted to velocity by the differentiator. The limiter prevents any velocities greater than 70°/s from going through this branch. [The numbers given in this section are only typical values, and the stan­dard deviations are large—for example, and LaRitz (41) showed smooth-pursuit velocities of 130°/s for a baseball player.] The leaky integrator K/(ts + 1) is suggested from (a) experimental results showing that humans can track ramps with zero steady-state error (7) and (b) open-loop experiments that showed a slope of — 20 dB per decade for the pursuit branch’s frequency response (30). The gain, K, for the pursuit branch must be greater than unity, since the closed-loop gain is almost unity. Currently used values for the gain are between 2 and 4 (30, 42). The e~sT term represents the time delay, or latency, between the start of the target movement and the beginning of pursuit move­ment by the subject. A time delay of 150 ms is currently ac­cepted (30,33). The saturation element prevents the output of any velocities greater than 60°/s, the maximum

velocity produced by most human smooth-pursuit systems.

The model must be able to overcome the 150 ms time delay and track with zero latency. Because the smooth- pursuit system is a closed-loop system, the model’s time delay appears in the numerator and the denominator of the closed-loop transfer function,

An adaptive predictor using adaptive filters was designed to overcome the time delay in the numerator. Compensa­tion for the model’s dynamics overcomes the time delay in the denominator.

We used several different techniques for predicting tar­get velocity. There are many more to choose from (see the “Adaptive filters’” and “Filtering theory” sections of this encyclopedia). Now we will make a detailed presentation ofone of these prediction techniques. The nonmathematical reader may skip this section (all the way to “VALIDATE THE MODEL”) without loss of continuity.

Figure 11. Implementation of the weight adjustment algorithm. [From D. R. Harvey and A. T. Bahill, Development and sensi­tivity analysis of adaptive predictor for human eye movement model, Transaction of the Society for Computer Simulation, De­cember 1985. © 1985 by Simulation Councils, Inc., San Diego, CA. Reprinted by permission. (20).]

Figure 10. McDonald’s TSAC model has three branches: smooth pursuit, saccadic, and the adaptive predictor.

To solve the Wiener-Hopf equation it is necessary to compute the correlation matrices. However, this would re­quire a lot of computer time; furthermore, these matrices cannot be computed in advance, because this would require a priori knowledge of the statistics of the input signal.

Because it is difficult to compute the true gradient, we use an estimate of the gradient, which is equal to -2E(j)X(j). Our algorithm is a form of the method of steep­est descent using estimated gradients instead of measured gradients. Using this estimated gradient, the adjustment algorithm can be written as

1) = W{j) – j – 2ksE{j)X{ j)

Figure 11 illustrates the implementation of this weight adjustment algorithm. If the input signals are uncorre­lated, then the expected value of the estimated gradient converges to the true gradient without any knowledge of the input signal’s statistics.

During the operation process of the LMS filter, illus­trated in Fig. 12, the tapped delay-line input signals are weighted, using the gains from the adaptation process and summed to form the output signal. The difference between the desired output signal and the actual output of the fil­ter is the error that is fed back to the weight adjustment algorithm.

The speed and accuracy of the filter while converging to the optimal solution depends on several factors. Because noise is introduced into the weight vector from the gra­dient estimates, it follows that if the filter is allowed to

Figure 12. The LMS adaptive filter. The boxes labeled “Weight adjustment” contain systems like Fig. 11. [From D. R. Harvey and A. T. Bahill, Development and sensitivity analysis of adaptive pre­dictor for human eye movement model, Transaction of the Society for Computer Simulation, December 1985. © 1985 by Simulation Councils, Inc., San Diego, CA. Reprinted by permission. (20).]

converge slowly, less noise will be introduced during each adaptation cycle and the convergence will be smoother. Re­gardless of the speed with which the filter converges, some noise will be introduced. This noise prevents the filter from converging to the minimum MSE. The ratio of the excess MSE to the minimum MSE gives a measure of the misad – justment of the filter compared to the optimum system. The misadjustment depends on the time constant of the filter’s weights, where the time constant is defined as the time it takes for the weights to fall within 2% of their converged value. A good approximate formula for the misadjustment, M, is

This algorithm shows that M is proportional to the number of weights, n, and inversely proportional to the time con­stant, tMSE. The time constant тMSE can be measured exper­imentally for each simulation. However, we would prefer to find an analytical way to estimate it. We can do that as fol­lows.

To ensure convergence the proportionality constant, ks, in the weight adjustment algorithm must be within the following bounds:

1

0 < ks <

where E[X2j] is the expected value of the square of the jth input. For slow and precise convergence, ks should be within the following more restrictive bounds:

і

0 < ks «

According to Widrow (43,44), for a filter using tapped delay – line input signals, the time constant is related to the pro­portionality constant by

Figure 13. The adaptive predictor. The boxes labeled “Adaptive filter” and “Slave filter” contain systems similar to those in Fig. 12. [From D. R. Harvey and A. T. Bahill, Development and sen­sitivity analysis of adaptive predictor for human eye movement model, Transaction of the Society for Computer Simulation, De­cember 1985. © 1985 by Simulation Councils, Inc., San Diego, CA. Reprinted by permission. (20).]

In summary, an adaptive filter is made up of a tapped delay line, variable weights, a summing junction, and the weight adjustment algorithm. The filter adjusts its own internal settings to converge to the optimal solution. Due to noise from the gradient estimate, the accuracy and speed of con­vergence depends on the number of weights and the pro­portionality constant, ks.