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IEEE TRANSACTIONS ON BROADCASTING, VOL. 51, NO. 1, MARCH 2005 51

A New Positioning System Using TelevisionSynchronization Signals

Matthew Rabinowitz, Member, IEEE, and James. J. Spilker, Jr., Member, IEEE

AbstractThe technique discussed herein may be used to posi-tion a range of wireless devices that require location informationwhen in inclement urban conditions, including PDAs, laptops,cellular phones, asset-tracking devices and radios for emergencyresponse personnel. We make use of synchronization signals thatare part of the standard for Television set forth by the AdvancedTelevision Systems Committee. Consequently, the technique de-scribed herein requires no changes to the television broadcaststations. The signal can accommodate robust indoor positioningwhere the Global Positioning System (GPS) fails, since the tele-vision synchronization signals typically have a power advantageover GPS of more than 40 dB. In addition, the effects of multipathare substantially mitigated since the signals have a bandwidth ofroughly 6 MHz, and substantially superior geometry for triangu-lating lateral position to that which GPS can typically provide ininclement environments. A wide range of VHF and UHF frequen-cies have been allocated to television stations; consequently, thereis redundancy built into the system to protect against deep fadeson particular channels. In addition, unlike GPS, the synch signalsare not affected by transmitter Doppler, ionospheric propagationdelays, or data that is modulated onto the signals. In overview, thetechnology exploits the considerable Digital TV infrastructure toachieve more reliable, accurate and rapid positioning than can beachieved with existing technologies.

Index TermsDigital Television, location, positioning, rosum.

I. INTRODUCTION

DIGITAL Television was first implemented in the United

States in 1998. As of February 28 2001, 1266 DTV con-

struction permits had been acted on by the Federal Communi-

cation Commission (FCC). According the FCCs objective, all

television transmission will soon be digital, and analog signals

will be eliminated. A total of over 1600 DTV stations are ex-

pected in the United States. The signal structure used for DTV

is specified by the Advanced Television Systems Committee

(ATSC).

Because of the physical characteristics of the signals that

are used in most positioning technologiessuch as the Global

Positioning System (GPS)no system designed to date canprovide truly accurate and reliable location information indoors

and in inclement urban areas. The best solutions to this problem

involve integrated systems. The positioning system based on

television synchronization signals is a suitable complement to

GPS since it tends to work in those challenging urban environ-

ments where GPS tends to fail. The power of the TV signal,

combined with the large number of available stations, makes it

Manuscript received March 13, 2003; revised August 16, 2004.The authors are with the Stanford University and Rosum Corporation, Red-

wood City, CA 94063 USA (e-mail: [email protected]).Digital Object Identifier 10.1109/TBC.2004.837876

Fig. 1. Structure of the ATSC DTV frame.

possible achieve low Horizontal Dilution of Precision (HDOP)

for locating wireless devices throughout most of the United

States, Europe, and Asia. The TV signals are at low frequencies

well-suited for urban propagation; they have bandwidths of

6 MHz or greater; and they do not suffer from the Ionosphericand Doppler effects which hinder the performance of GPS.

Herein, we focus on the use of the ATSC DTV [1] signal for

location in the Continental United States (CONUS). However,

it should be noted that in recent years synchronization signals

have also been included in NTSC Analog TV Broadcasts [2].

These are designed for channel modeling and multipath miti-

gation, and consequently can be used for accurate positioning.

In addition, it should be noted that other DTV standards around

the world (including DVB in Europe [4] and ISDB-T in Japan

[3]) include synchronization codes which can be used for accu-

rate positioning. Herein, we analyze the coverage and geometry

provided by Analog and Digitial TV stations over the CONUS.

We describe results of position testing performed both indoors

and outdoors.

II. SIGNAL DESCRIPTION FOR ATSC DTV

The ATSC signal [1] uses 8-ary Vestigial Sideband

Modulation (8 VSB). The symbol rate of the ATSC

signal is MHz which is derived from a

27.000 000 MHz clock. The structure of the ATSC frame is

illustrated in Fig. 1. The frame consists of a total of 626 seg-

ments, each with 832 symbols, or a total of 520 832 symbols.

Each segment within the frame has 4 symbols used for seg-

ment synchronization. There are segments within each frame

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52 IEEE TRANSACTIONS ON BROADCASTING, VOL. 51, NO. 1, MARCH 2005

Fig. 2. ATSC DTV field synchronization segment.

Fig. 3. ATSC DTV data segment.

termed field synchronization segments. The structure of the

field synchronization segment is shown in Fig. 2. Notice that

the two synchronization segments in a frame differ only to the

extent that the middle set of 63 symbols are inverted in the

second frame. The structure of the data segment is illustrated in

Fig. 3. Notice that the first four symbols are the

segment synchronization symbols; the other 828 symbols carry

data. Since the modulation scheme is 8-ary VSB, each symbol

carries 3 bits of coded data. A rate 2/3 coding scheme is used.

The 8 VSB signal is constructed by filtering. The in-phase

segment of the symbol pulse has a raised-cosine characteristic

[1], [5]. The pulse can be described as

(1)

where . This signal has a frequency characteristic

(2)

(2)

From which we see that the one-sided bandwidth of the signal

is MHz MHz. In order to create a VSB

signal from this in-phase pulse, the signal is filtered so that only

a small portion of the lower sideband remains. This filtering can

be described as

(3)

(4)

where is a filter designed to leave a vestigial remainder

of the lower sideband. A plot of the gain function foris shown in Fig. 4. The filter satisfies the characteristics:

and , .

The response can be represented as

(5)

where is the Hilbert transform of

. The VSB pulse may be represented as

(6)

and the baseband pulse signal may be represented as

(7)

Fig. 4. Transfer function of the filter H .

Fig. 5. Simplified architecture of a software receiver.

where is the in-phase component, is the quadrature

component, and

(8)

Before the data is transmitted, a carrier signals is inserted into

the ATSC signal. This carrier has dB less power than

the data signal and aids in coherent demodulation of the signal.

Consequently, the transmitted signal can be represented as

(9)

where is the 8-level data signal.

III. EXTRACTING TIMING FROM THE ATSC DTV SIGNAL

Many different approaches exist for downconverting the

TV signal, and extracting timing information. Some canonical

signal tracking techniques which make use of correlators, or

matched filters, are discussed in references [6], [7]. In the case

that position can be computed with a brief delay, a simple ap-

proach is to use a software receiver which samples a sequence

of the downconverted signal, and then processes the sample in

firmware on a DSP or microprocessor. Such an architecture is

illustrated in Fig. 5. The approach that we discuss here, whichmay be implemented in the processor of Fig. 5, considerably

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RABINOWITZ AND SPILKER: A NEW POSITIONING SYSTEM USING TELEVISION SYNCHRONIZATION SIGNALS 53

Fig. 6. Correlation segment displaying the peak produced by the field synchronization signal on channel 52, San Jose, California.

mitigates the effects of multipath. This is achieved by sampling

an entire autocorrelation function, rather than using only early

and late samples as in typical hardware implementations of

the standard DLL (Delay Locked Loop). The earliest corre-

lation peak that exceeds a particular threshold is selected as

corresponding to the most direct line of sight signal path.

Many approaches to identifying the signal components for a

multipath channel have been explored [9][15]. The details of

these approaches are beyond the scope of this paper.We will focus on the correlation processing in the general-

ized context of a noncoherent software receiver, where a nom-

inal offset frequency for the downconverted sampled signal is

assumed. If the signal is downconverted to baseband as in Fig. 5,

the nominal offset will be 0 Hz. The process described below is

generic, and produces the complete correlation function based

on a sampled signal ; where

is the sampling period and is the period of data sampled. Let

be the nominal offset frequency of the sampled incident

signal, and let be the largest possible un-modeled offset

frequency, due to Doppler shift and local oscillator frequency

drift. The process for identifying the correlation peak imple-ments the following pseudo-code:

Create a complex code signal ;

where is the function describing

the in phase baseband signal and is the function de-

scribing the quadrature baseband signal.

Compute where is the Fourier transform op-

erator, and is the conjugate operator.

For to step

Create a complex mixing signal

,

Combine the incident signal and the mixingsignal

Compute the correlation function

If then

,

Next

Upon exit from the process, will store the corre-

lation between the incident sampled signal and the

complex code signal . may be further refinedby searching over smaller steps of . The initial step size for

must be less then half the Nyquist rate . Although

a detailed description of the search algorithm is beyond the

scope of this paper, note that there are many techniques by

which this process can be implemented more efficiently than

the algorithm described above. For example, referring to the

ATSC DTV frame structure in Fig. 1, one can initially acquire

timing of the segment synch signal which repeats every 77

and can be found without searching over an entire field. This

timing information can then be used to efficiently search each

segment until the field synchronization segment of Fig. 2 is

found.In Fig. 6 we illustrate the magnitude of a portion of the corre-

lated segment, , in which the correlation peak for the

field synchronization segment is found. This correlation peak is

produced from correlating with one Field Synchronization Seg-

ment for a signal on channel 52, transmitted in San Jose, Cali-

fornia. The axis is in samples, with a sampling rate of 26 MHz,

and an arbitrary starting point. The sidelobes arise since the TV

signal is bandlimited to 6 MHz. The signal is relatively free of

multipath components; so we may assume that this transmission

channel consists of only one path to the receive antenna. Conse-

quently, the sample corresponding to the peak may be translated

into a timestamp or pseudorange, , representing the time of ar-rival of the field synch at the receive antenna.

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Fig. 7. Overview of the television positioning system.

IV. THE NAVIGATION SYSTEM

An overview of the navigation system architecture is illus-

trated in Fig. 7. The position calculation may be implemented

at the Enabled Device, or at the Server. Unlike the case of a

satellite-based positioning technique [8], the location of the

transmitters is unchanging, and need not be continually up-

dated. Consequently, the TV transmitter location data may be

stored at the device or at the server. By one of a variety of

tracking techniques, the device measures the pseudorange to

each of a subset of visible transmitters. Pseodoranges to three

spatially separated transmitters are sufficient to resolve the

users latitude, longitude, and clock bias, and to triangulate

the users position. Latitude and longitude may also be com-

bined with an altitude map to refine the position computation.

In order to compute an accurate location of the device, the

precise timing of the TV synchronization code transmissions

must be known. Monitor Units at known positions are used

to independently monitor the TV station clock offsets. These

clock offsets may be applied to the position computation at the

server, or they may be communicated to the device for posi-

tion computation. Alternatively, the TV transmitters themselves

may broadcast to the clock offset information for channels in thesurrounding area. In this case, no independent communication

channel would be required to the user device.

V. THE POSITIONING ALGORITHM

For the sake of clarity, we detail the positioning algorithm as-

suming the use of a single monitor unitor reference. We assume

in what follows that rough timing synchronization (on the order

of a several milliseconds) exists between the monitor and the

users enabled device. This level of timing synchronization is

not used directly in the computation of user position, since that

requires timing information accurate to the nanosecond level.

However, this level of timing synchronization does consider-ably simplify the position computation. Other versions of the

Fig. 8. Physical description of the positioning problem.

positioning algorithm enable positioning without the rough time

synchronization between user and monitor, but these are beyond

the scope of the current paper.

Fig. 8 provides a physical description of the positioning

problem which illustrates the terminology. The picture includes

a single TV transmitter, and a single monitor unit. Note that we

have taken the reference to be the location of the monitor unit.

However, when multiple monitor units are used to track a singletransmitter, a virtual reference may be created, which is the

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location of an imaginary monitor unit, the measurements from

which would correspond to the combined information from the

multiple monitor units.

Imagine the user device starts sampling the signal at some

true time . We use the term true time to distinguish from the

time perceived by the user device, which doesnt have a perfect

clock and consequently doesnt knowwhatthe exact or true timeis. According to the user device, it starts sampling at some time,

, which is offset from true time by the user clock bias at the

time

(10)

Fig. 8 represents a snapshot of the signals propagating from

the TV transmitter to the user device and the monitor station

at true time . The part of the signal which is represented

by a dotted line has yet to be transmitted. The pseudorange

measurement that is computed at the user device, , is

in essence the time from the start of sampling to the instant

when the burst of the synch code arrives at the user device,multiplied by the speed of light, . Notice that can only

be measured modulus the wavelength, , of the transmitters

code, which repeats every seconds. Let us assume that the

synchronization burst which the user measures is transmitted

from transmitter at time . Notice that the transmitter also has

an imperfect clock and consequently doesnt have knowledge

of exact time. According to the transmitter, the synch pulse

is transmitted at time , which is offset from true time by

the transmitter clock bias at time

(11)

Consequently, we may describe the pseudorange measurementat the user device for a transmitter, , as

(12)

Substituting (12) into (11) and (10) describing the imperfect

clocks of the User Device and transmitter, we obtain

(13)

We can eliminate the mod operation by including in the mea-

surement some unknown integer cycle ambiguity, , for the

user measurement of the transmitter . In addition, since the

transmitter clock is relatively stable [16] and the time at which

the synch burst is transmitted, , is relatively close to the timeat which sampling begins, , we may make the simplifying as-

sumption that . Then, the measurement at the

user device can be described as

(14)

Similarly, we may describe a phase measurement at the refer-

ence, or monitor station, as

(15)

where the subscript has been used to replace the subscript

to describe the same physical and timing phenomena at the ref-

erence that we have described at the user. The differential posi-tioning methodology may be implemented by differencing the

Fig. 9. Analog and digital TV towers in the CONUS.

Fig. 10. Roll-off curves for the CCIR propagation model for frequencies upto 1 GHz. The

x

axis represents distance in kilometers; they

axis representsElectric Field Strength in decibels. each line represents a different antennaheight above average terrain.

measurement made at the user and the reference for a particular

transmitter,

(16)

where . We may assume for simplicity that

according to the local clocks of the user and reference, whichare offset from true time by and respectively, the

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Fig. 11. Simulated HDOP throughout the CONUS.

user and reference think they are sampling the signal at the same

time, or . In order to render the measurement in

a form that can be used to estimate the necessary parameters,

we define a user-reference clock bias .

Using this definition, and expanding the measurement to first

order with respect to the user and transmitter clock offsets, we

obtain

(17)

If we now assume that the transmitters clock is relatively stable,

and that the difference between the user and the reference clock

biases is not too large, we can make the simplifying assumption

(18)

This enables us to represent the pseudorange measurement in

the tractable form

(19)

Assume that we take measurements from a set of transmit-

ters. Each of those transmitters will be associated with a partic-ular integer cycle ambiguity. Let be the vector of parameters

that we wish to resolve: namely the users latitude and longitude,

the clock offset between user and reference, and the integer am-

biguity for each of the channels

(20)

Notice that the measurement (19) is nonlinear in the pa-

rameters since the distance between the user and transmitter is

nonlinear in the user latitude and longitude:

(21)

Consequently, we must resolve these parameters using an

iterative technique which linearizes the measurement equa-

tion around our parameter estimates at each iteration. Based

on our current estimates of the parameters, we compute an

estimateaccording to (19)of the expected pseudorange

measurement: . In order to update the parameters, we then

create an estimation error vector based on the measurements

(22)

The set of estimation errors on transmitters can now gen-

erate a linearized matrix measurement equation

(23)

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Fig. 12. HDOP Testing conducted throughout the San Francisco Bay Area. The shading on the map illustrates the more densely populated areas.

where

......

. . . (24)

is the direction vector from the user device to the station ,

is a vector of disturbances or noise terms which we assume are

normally distributed with mean and covariance , and are

the set of parameter updates. Several canonical techniques for it-

eratively solving this matrix equation are well known in the liter-

ature [17][21]. The techniques range from treating the integers

as real numbers and using a recursive least-squares algorithm toperforming an integer search for the nonreal parameters. Note

that the wavelengths, , which correspond to the ATSC seg-

ment synch and field synch signals are largeroughly 23 km

and 7,360 km respectively. Hence, the cycle ambiguities can be

resolved with a high degree of integrity.

VI. CALIBRATING SYSTEM PERFORMANCE

Independent of the technique used to resolve the parameters

, it should be noted that the conditioning of the observation ma-

trix, , is crucial to the performance of the positioning system.

To illustrate this point, we consider only the real-valued param-

eters that require estimation

(25)

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and assume that all the integer-valued parameters are known

a-priori or determined by an integer search. Then, (23) with just

the real components isolated would become

where

.

..

.

..(26)

The maximimum-likelihood estimate for the real parameter up-

dates would then be

(27)

If the pseudoranging errors on each channel are uncorrelated

and each has variance , it is straightforward to show from

(27) that the expected variance on the estimated parameters is

given by

(28)

The matrix determines how the disturbances, , in

the pseudorange measurements are combined with the geometry

of the transmitters to cause errors in the parameters being esti-

mated. The diagonal elements of this matrix correspond to the

DOP (Dilution of Precision) factors for the latitude, longitude

and clock parameters.

(29)

So, we can compute the fundamental HDOP (Horizontal Dilu-

tion of Precision) of the navigation system according to

(30)

Assuming pseudoranging errors on each channel which are nor-

mally distributed with variance , then we expect horizontal

position errors with a standard deviation of .

In order to calibrate the performance of the navigation

system, we can simulate the expected HDOP throughout an

area. Fig. 9 illustrates the location of currently licensed analog

and digital TV stations in the CONUS. Each discernable x

represents a tower; typically many channels are transmitted

from a single tower. The heights, ERP (Effective Radiated

Power), and antenna patterns of each of the TV transmitters

may be combined with the CCIR propagation model [22] to

approximate the coverage of each station. Fig. 10 illustrates

the power roll-off curve from the CCIR model that was used

for our propagation study. Of course, the roll-of patterns for

TV signals are strongly dependent on terrain, so it should be

understood that this CCIR model is only used to provide a

rough estimation, based on certain assumptions about terrain

roughness.

On top of the propagation loss, the following set of power loss

assumptions are used to model the effect on the SNR in a small

wireless device in a severe indoor environment:

Added human noise above thermal: 15 dB Attenuation due to walls etc: 20 dB

Fig. 13. Positioning test bed in Redwood City. Note that the single monitorunit can span a larger area than that shown in the red tetrahedron. The coveragerange of a single monitor unit depends largely on terrain.

Polarization loss at the antenna: 3 dB

Matching loss at the front end: 10 dB

We assume that the signal can be coherently integrated in the

correlator for up to one second, and that the post-correlation

SNR must be at least 13 dB in order to be usable for a precise

position fix. With this set of assumptions, we obtain a rough

estimate of the coverage radius for each of the transmitters il-

lustrated in Fig. 9. Based on the set of available stations that

are above the usable threshold at each point in the CONUS, we

have generated the HDOP contour map of Fig. 11. Notice that

all areas within the dark inner contour have an HDOP of 1 or

less. Although there are certain rural and mountainous areas in

which coverage is lacking, there is typically good coverage in

the population centers of the United States.

In order to validate the implications of the coverage analysis,

an extensive test based on the measurement of real signals was

conducted throughout the greater San Francisco Bay Area. The

results of this testing are illustrated in Fig. 12. Each location

that was tested is marked with a peg on the map. The associated

HDOP at each peg is based on the analog and digital signals

from which usable pseudoranges could be acquired. Notice the

low HDOP values which are roughly consistent with the predic-

tions of Fig. 11even in built-up and hilly urban environments

such as down-town San Francisco at the Northern Tip of the

Peninsula. In order to perform a position fix, at least three towers

must be visible, providing three equations to resolve for latitude,longitude and clock. Note that only one location was found at

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Fig. 14. Scatterplot corresponding to the outdoor location of Fig. 15.

Fig. 15. Location in a park in Redwood City, with benign multipath,corresponding to the scatterplot of Fig. 14.

which the TV system could not generate a position fix in stand-

alone operation, since only two separate transmitters were vis-

ible. This location was south of San Gregorio Creek (bottom left

of Fig. 12), below of range of mountains which blocked the sig-

nals from inland TV transmitters.

It should be noted that the low HDOP indicates a robust

location system with many redundant signals. One method

for exploiting these redundant signals is a technique known

as RAIM (Receiver Autonomous Integrity Monitoring.) This

provides a technique by which a user device, independent of

the rest of the system, may verify the validity of each of

the signals used for positioning by checking for consistency

with the other redundant measurements. The lower the systemHDOP, the more effectively RAIM can eliminate channels that

are causing substantial errors due to multipath or other effects.

A more extensive discussion of the RAIM algorithm may be

found in the references [23][25].

VII. POSITIONING TESTS

Fig. 13 illustrates the positioning test bed that has been set up

in Redwood City, California. The location of the monitor unit is

illustrated with a red X, and the area over which position fixes

were performed is illustrated with a red tetrahedron. We will de-

scribe a set of results achieved in the area which, according to

the measurements of Fig. 12, is characterized by an HDOP ofroughly 1.43. Scaling of the position errors by the appropriate

Fig. 16. Scatterplot corresponding to the location of Fig. 17.

Fig. 17. Location inside a parking garage at Stanford University,corresponding to the scatterplot of Fig. 16.


HDOP at other locations will provide an indication of the posi-

tioning accuracy which can be expected at various locations.

The plots and pictures shown from Figs. 1421 give an

indication of the system performance in various environments.

Each of the scatterplots has and axes which are scaled

in meters. The circles with radius 150 meters and 50 metersrespectively indicate the 67% and 95% error thresholds for

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Fig. 19. A central location on the ground floor of an office building inRedwood City, corresponding to the scatterplot of Fig. 18.


Fig. 21. Testing laboratory in Redwood City, on the ground floor of a 3 storeybuilding, corresponding to the scatterplot of Fig. 20.

the FCCs phase II E911 requirements 1 for locating cellular

phones. The additional circles running through the mean of

the error distribution indicate the bias of the error for each

scatter plot. The results are summarized in Table I. In each

1These requirements are: An accuracy of 50 meters or better 67% ofthe time and an accuracy of 67% or better 95% of the time. While it

is not specified by the FCC whether this relates to indoor or outdoorperformance, the TV positioning system typically meets these requirementsin indoor as well as outdoor environments.

TABLE ISUMMARY OF RESULTS FOR DATA SHOWN IN FIGS. 1421

test scenario, the user device makes use of a small wire-loop

antenna, designed for easy integration into small, low-cost

wireless devices. The antenna is mounted on top of the blue

stick shown in all the location pictures.

VIII. CONCLUSIONS

The summary results indicate a positioning technology with

high accuracy and reliability, particularly in difficult indoor en-

vironments. It should be noted that performance is not alwaysbe as good as that indicated in Table I, since environments with

different TV station coverage, as well as different levels of atten-

uation and multipath reflection, will generate substantially dif-

ferent positioning performance. Nonetheless, the test results do

support the idea that the TV positioning technology can substan-

tially improve over the limitations of existing technologies such

as GPS. This is largely due to the higher power levels, wider

bandwidths, lower frequencies and superior geometries of the

TV positioning system. Of course, no positioning technology

will operate in all environments. Since GPS operates very well

in remote rural areas, and the Television positioning technology

operates very well in challenging urban environments, the syn-ergy between GPS and TV positioning technologies provides a

robust integrated location solution.

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Matthew Rabinowitz completed a B.A. in Physicsat Stanford, in 1996, graduating as top student inthe department, and receiving the Levin Award

for outstanding academics and research. He alsoreceived the Terman Award, the highest academichonor offered by the School of Engineering at Stan-

ford. In 1997, he completed an M.Sc. in ElectricalEngineering, and received a graduate fellowship tothe School of Electrical Engineering at Stanford.In 2000, Matthew completed his Ph.D. in ElectricalEngineering at Stanford. His Ph.D. involved the

design and construction of a navigation system that combined Low Earth Orbit

Satellites with the Global Positioning System to achieve centimeter-level posi-tioning. During his Ph.D., Matthew consulted with IntegriNautics Corporation,to investigate the commercial applications of the technology resulting from

his Ph.D. In 1999, he took a leave of absence from Stanford, to work fulltime at Panopticon, a company he had co-founded to address the challenge

of online, real-time, intelligent merchandizing. In 2000 the company wassold, as Panop.com, to Broadbase software. In September of 2000, Matthewco-founded Rosum Corporation, and currently serves as its Chief TechnologyOfficer. He has authored several papers and patents in the fields of optimization,communication systems, signal processing and navigation. In 2003, Matthew

joined the faculty of the Stanford School of Engineering as a ConsultingAssistant Professor.

James J. Spilker, Jr. completed a B.S. in ElectricalEngineering at Stanford University in 1955 followedby an M.S. in Electrical Engineering in 1956 anda Ph.D. in Electrical Engineering in 1958. Bothgraduate degrees were received from Stanford Uni-versity. Jim has pursued parallel careers in industryand academia. He is a member of the United StatesNational Academy of Engineering, a Life Fellow

of the IEEE, a Fellow of the Institute of Navigation(ION), a recipient of the ION Johannes KeplerAward, and a recipient of the Hall of Fame Award

from the GPS Joint Program Office and the US Air Force. Between 1973 and1999, Jim was founder, Chairman, and CEO of Stanford Telecommunications,Inc. Jim served on the advisory boards for several Internet access and ASICrelated companies, as well as the Board of Advisors for the Stanford Schoolof Engineering, the Board of Advisors for the USC School of Engineering,the U.S. Congressional Advisory Board for the International Space Station,and the U.S. Air Force GPS Independent Review Team. He was chairmanof the Technical Activities Board for the IEEE Communications SciencesInstitute, and a consulting professor at Stanford University. Jims expertiseincludes Spread Spectrum techniques, Orthogonal and Multicarrier CDMA,vector delay lock tracking, cable modems, coding schemes, broadband internetaccess, network management, fixed wireless in multipath channels, adaptiveequalization, general relativistic effects, signal propagation, and the GlobalPositioning System (GPS). He originated various forms of the Delay Lock

Loops used in almost all cellular CDMA and GPS receivers. He was also theco-architect of the original GPS system, and the next generation GPS L5 codes.

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