A Real-Time GPS/SBAS Time-Divided-Multi-Signal Quality … · 2005. 6. 27. · A Real-Time GPS/SBAS Time-Divided-Multi-Signal Quality Monitoring Receiver Naoki Fujii, Shinji Saitoh,

A Real-Time GPS/SBAS Time-Divided-Multi-Signal Quality Monitoring

Receiver

Naoki Fujii, Shinji Saitoh, Electronic Navigation Research Institute Toyoo Hashimoto, Masato Kawai, Hiroyuki Nakao, Furuno Electric Co., Ltd.

BIOGRAPHY Naoki Fujii is the leader of Ground Based Augmentation System (GBAS) research group and a Principle Researcher of Electronic Navigation Research Institute (ENRI). He was charged with development of the siting criteria of Instrument Landing System (ILS), Microwave Landing System (MLS) and Aircraft Address Monitoring System (AAMS) in ENRI. He is currently working in field of the development of Ground-Based Augmentation Systems for GNSS.

Shinji Saitoh is a researcher at ENRI, IAI. He received M. Eng. in Electronic Engineering from the University of Electro-Communications (UEC) in 1998. He joined ENRI, Ministry of Transport (MOT) in 1998. Currently, he is researching in the area of GPS and GBAS. He belongs to GBAS research group in ENRI. He is a member of the. Institute of Electronics, Information and Communication Engineers (IEICE).

Toyoo Hashimoto is a Chief Design Engineer in Avionics Division Engineer of Furuno Electric Co. Ltd. He holds the BS in Electronics from the University of Okayama in Japan. He has over 32 years of experience in the field of several avionics systems.

Masato Kawai is a Senior Software Engineer of Furuno Electric Co. Ltd. He holds the BS in Information Engineering from the Nagoya Institute of Technology in Japan. He has over 15 years of experience in the field of embedded software development. Currently he is working on GNSS receiver research and development.

Hiroyuki Nakao is a Senior Software Engineer of Furuno Electric Co. Ltd. He graduated from Computer Nihon Gakuin College in 1984. He has over 16 years of experience in the field of GPS receiver software. Since 5 years ago, He has been involved in GPS/SBAS receiver development..

ABSTRACT GPS is widely used in many applications now. Safety-critical applications such as aircraft navigation require position information not only with high accuracy but also high integrity. To meet these two difficult requirements, Signal Quality Monitoring (SQM) is necessary for both the GBAS and the Space Based Augmentation System (SBAS). SQM must detect the distortion of the correlation-curve caused by satellite anomalies, such as "evil waveform".

Electric Navigation Research Institute in Japan (ENRI) and Furuno Electric Co., Ltd.(FURUNO) have jointly developed a new GPS/SBAS receiver with real-time correlation-curve monitoring capability. This is the first prototype of the GPS/SBAS receiver with SQM in Japan. The receiver has 16 channels including 3 SBAS dedicated channels for L1 C/A signal tracking and 2 SQM channels.

Each SQM channel has 127 correlation points distributed evenly at 0.025575 chip step. The width of the correlation monitoring range is about 3.2 chips. It can be allocated at any part of 1023-chip width by user selection. Each correlation point outputs both In-phase and Quadrature at 5 Hz. This receiver also supports Time-Divided-Multi-Signal Monitoring (TDMSM) mode, which changes monitoring satellite and/or monitoring range by time-sharing method. Using TDMSM, the receiver can produce up to 10 different satellites´ correlation-curves at 1 Hz update rate. That means most of the time, the receiver monitors all visible satellites in real-time. In addition to the SQM capability, the receiver can output up to 5 different position-fixes, such as GPS, DGPS, SBAS#1, SBAS#2, and SBAS#3. Raw observation data, such as accumulated carrier phase, pseudorange, carrier-to-noise ratio, Doppler-shift, and time-tag are also outputted at 5 Hz. The receiver is equipped with Ethernet. It allows us fast data output via network environment. We also developed data recorder program on the personal computer (PC).

This paper introduces the detail specification of the SQM receiver and presents data collected in three different environments; (1) using parabolic antenna; (2) usual fixed-point observation; (3) multipath by GPS/SBAS simulator;

INTRODUCTION Since SV19 problem occurred in 1993, several teams have been tackled this problem. Detection of these satellite signal anomalies, such as evil waveforms, and to inform such satellite anomalies to the differential GPS users within time-to-alarm are very important. Detection mechanism using multicorrelator techniques are main target for the safety-critical GPS user society, especially aviation relevant one. Three thread models and SQM design have been described in the International Civil Aviation Organization (ICAO) GNSS Standards And Recommended Practices (SARPs). The GNSS receivers in the monitor stations for SBAS and GBAS system must be compliant with SARPs SQM. GBAS and SBAS must guarantee the integrity (truthfulness) of the service by SQM and other techniques. WAAS starts its Initial Operational Capability (IOC) on 10 July 2003. In Japan, MTSAT-1R will be launched in early 2004. ENRI and FURUNO developed prototype SQM receiver to collect fundamental data of SQM and to examine its performance for preparation of near future MSAS IOC and GBAS IOC in Japan. SQM RECEIVER The block diagram of the SQM receiver is shown in Figure 1. The receiver consists of Analog board, Digital board, and CPU board.

Figure 1:Block diagram of SQM receiver

Antenna

Pre- amp

Front- End

A/ D Conv.DopplerRemoval

CarrierNCO

Correlator

Coder

Code NCO

CorrelatorCorrelatorCorrelator

SelectorCorrelator

Coder

Code NCO

CorrelatorCorrelatorCorrelator

MPU

SignalProcessing

NavigationProcessingx 4

x 254

SQM Channel x 2

Tracking Channel x 16

SQM DisplayPositionVelocity

Time

Analog Board

Digital Board

CPU Board PC

RS- 232C

LAN

BUS

PowerPC 400 MHz

Table 1: Key specifications of the SQM receiver Item Detail

GPS L1 C/A 13 ch Tracking Channel 16 ch SBAS L1 C/A 3 ch GPS alone DGPS SBAS#1 SBAS#2

Position update

SBAS#3

5 Hz, 5 solutions simultaneously

SQM Channel 2 ch 127 points/ch I & Q @0.025575chip

SQM I & Q data 16 bit Binary 2�s complement

Normal Keep monitoring one satellite.

TDMSM Auto.*1 Changing satellite to be monitored @5 Hz automatically SQM Mode

Each SQM ch

TDMSM Man.*1 Changing satellite to be monitored @5 Hz by manual selection

LAN 100Base-TX :1 Data Port Serial RS-232C : 2 Up to 115200 bps IF bandwidth 19 MHz Firmware update via RS-232C WAAS MOPS Complied with RTCA/DO-229B

Data Recorder/Monitor Program on PC

Display/Record/Playback Correlation-curve Test Metrics Position-fix results GPS/SBAS message (Hex binary) Measurement data for 16 tracking channel

*1: Firmware update to be required to utilize TDMSM SQM Mode.

The key specifications are summarized in Table-1. Firstly, we developed SQM Normal Mode, which keeps monitoring one same SV selected by user all the time. Our SQM receiver has two SQM channels and it outputs I-Q data at 127 correlation points per SQM channel every 200 ms. All the data in this paper were collected with SQM Normal Mode. The TDMSM Auto. Mode repeats

changing satellite 5 Hz among the all visible satellites automatically. On the other hand, the TDMSM Man. Mode repeats changing satellite among the user selected satellites. Figure 2 illustrates these SQM modes.

Figure 2: SQM Mode

Visible SV set

Continuine to monitor one same SV

Normal SV(Z) SV(Z) SV(Z) SV(Z) SV(Z) SV(Z) SV(Z) SV(Z) SV(Z)

TDMSMAuto. SV(1) SV(2) SV(3) SV(4) SV(5) SV(6) SV(7) SV(1) SV(2)

TDMSMMan. SV(A) SV(B) SV(C) SV(D) SV(A) SV(B) SV(C) SV(D) SV(A)

Changing all visible SVs one by one and repeat monitor SV set automatically

Manually selected SV set

Changing manually selected SVs one by one and repeat monitoring

Repeat

Repeat

Repeat

time

200ms

PC program for SQM receiver We also developed PC program to collect/analyze the SQM data. Since we didn�t implement any test metrics on the SQM receiver, the PC program takes care of them. The PC program has the following functions: ! Store data into file (*.sqm file); ! Display real-time correlation-curve, position-fix

results, navigation message, signal tracking status, and test metrics;

! Playback the SQM data file; Figure 3 shows sample screen shots of position-fix results and signal tracking status. The sample screen shots of the navigation message and horizontal plots are shown in Figure 4.

Figure 3: Sample screen shots, Left: position-fix results

Right: signal tracking status

Figure 4: Sample screen shots, Left: navigation message

Right: horizontal plots The sample screen shot of the correlation-curve is shown in Figure 5. The upper one is GPS (PRN-16) satellite and the lower one is WAAS POR(PRN-134) satellite by accumulated display mode. User can select the followings: ! Display mode: "Absolute" or "Normalized" ! Data source: "In-phase" or "Quadrature" or

"Amplitude" ! Plot mode: "Moment" or "Accumlated"

Figure 5: Correlation-curve monitor screen

Figure 6 shows a sample screen shot of test metrics display. User can set and select the delta test metric or ratio test metric by using any correlation point at each SQM channel.

Figure 6: Test Metrics monitor screen

IDEAL SQM OBSERVATION DATA We collected SQM data by using parabolic antenna at Sugadaira (Latitude 36.52° N, Longitude 138.32° E, Height 1315.8 m) in Japan for 3 days, from 23 to 25 March 2003. The SQM data of 24 GPS satellites and one WAAS satellite (POR) were obtained. Picture 7 and 8 show photographs of parabolic antenna site at Sugadaira Space Radio Observatory and experiment setups respectively.

Figure 7:Parabolic antenna at Sugadaira in Japan

Figure 8:Setups for SQM data recording at Sugadaira

As typical correlation-curves, we pick two SQM data, the PRN-14 GPS satellite and the PRN-134 WAAS POR satellite. The mean and the standard deviation of In-phase data of 127 correlator outputs for PRN-14 and PRN-134 are shown in Figure 9 and 10 respectively. The correlation peak of PRN-14 (GPS) data is much sharper than PRN-134 (WAAS POR). POR data also shows a standard deviation peaks about ±0.6 chip-offset from the punctual point. Both data show good symmetry with respect to the punctual point. The signal power spectra of two satellites are shown in Figure 11.

SQM Receiver

Satellite Tracking Unit for Parabolic Antenna

Monitor / Recorder PC

Figure 9: PRN-14 GPS Correlation-curve

Figure 10: PRN-134 POR Correlation-curve

Figure 11: Signal power spectrum Left: PRN-14 GPS,

Right: PRN-134 WAAS POR Test Metrics The test metrics are based on the following definitions:

punctual

offsetoffset

punctual

offsetoffsetoffset

punctual

offsetoffsetoffset I

IR

III

RI

II=

⋅+

=⋅

−=∆ +−

±+−

± ,2

,2

Table 2 lists 11 test metrics' definitions.

Table 2: Test metrics' definitions Name Definition Name Definition TM1 05115.0076725.0 ±± ∆−∆ TM2 05115.01023.0 ±± ∆−∆

TM3 05115.0±R TM4 076725.0±R

TM5 1023.0±R TM6 1023.0−R

TM7 076725.0−R TM8 05115.0−R

TM9 05115.0+R TM10 076725.0+R

TM11 1023.0+R

PRN-134 (POR) Mean & σ of Correlator In-phase Output at Sugadaira

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

Chip offset [chip]

Nor

mal

ized

In-

phas

e

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0.02

Ave.

σ

Ave. C/No=42.25 dBHz

Ave. PowerRatio=0.932

PRN-14 Mean & σ of Correlator In-phase Output at Sugadaira

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

Chip offset [chip]

Nor

mal

ized

In-

phas

e0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

Ave.

σ

Ave. C/No=47.00 dBHzAve. PowerRatio=0.976

We examined 11 test metrics for all collected data. Table 3 and 4 summarize the result of 11 test metrics for 25 satellites. As a typical case, comparing the standard deviation of 11 test metrics between GPS(PRN-14) and POR(PRN-134), all standard deviations of POR are more than twice to those of GPS. (See Figure 12 below.)

Figure 12: Test metrics comparison between GPS(PRN-14) and WAAS POR(PRN-134)

Table 5 shows ideal correlation-curves of 25 satellites. These curves only use ideal In-phase data whose power-ratio at punctual point is bigger than 0.97. The power-ratio is defined by the following equation:

22

22

PowerRatiopunctualpunctual

punctualpunctual

QIQI

+−

=

Standard Deviation of Test Metrics GPS vs. WAAS POR

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

1 2 3 4 5 6 7 8 9 10 11

Test Metric #

PRN-14 ave. C/No=47.00 dBHz

PRN-134 ave. C/No=42.25 dBHz

Table 3: Summary of mean for 11 test metrics for 25 satellites observed at Sugadaira in Japan. PRN# C/N0 PwrR TM1 TM2 TM3 TM4 TM5 TM6 TM7 TM8 TM9 TM10 TM11

1 47.012 0.9778 -0.0003 -0.0004 0.8542 0.7768 0.6988 0.6988 0.7765 0.8541 0.8542 0.7772 0.6998 2 47.954 0.9802 -2.7E-5 7.8E-5 0.8544 0.7769 0.6992 0.6988 0.7764 0.8539 0.8549 0.7774 0.6996 3 45.875 0.9489 2.0E-5 0.0001 0.8555 0.7778 0.7000 0.6997 0.7774 0.8550 0.8559 0.7783 0.7004 4 49.000 0.9838 -0.0002 -0.0002 0.8534 0.7761 0.6985 0.6886 0.7762 0.8537 0.8531 0.7760 0.6957 5 49.000 0.9857 -0.0004 -0.0003 0.8534 0.7763 0.6988 0.6987 0.7762 0.8537 0.8532 0.7764 0.6988 6 45.979 0.9682 -0.0002 -0.0003 0.8557 0.7781 0.7004 0.6992 0.7771 0.8549 0.8566 0.7791 0.7016 7 48.044 0.9811 -8.4E-5 1.4E-5 0.8637 0.7913 0.7185 0.7181 0.7908 0.8633 0.8641 0.7918 0.7189 9 48.135 0.9817 2.3E-6 -8.4E-5 0.8545 0.7769 0.6992 0.6989 0.7766 0.8542 0.8547 0.7771 0.6996

10 48.997 0.9848 -0.0004 -0.0001 0.8525 0.7756 0.6979 0.6982 0.7756 0.8529 0.8522 0.7757 0.6978 11 45.028 0.9613 -0.0002 -0.0003 0.8555 0.7777 0.7001 0.7001 0.7779 0.8558 0.8551 0.7775 0.7001 13 48.98 0.9811 -0.0002 -0.0003 0.8544 0.7768 0.6992 0.6992 0.7768 0.8546 0.8541 0.7768 0.6992 14 47.000 0.9761 -0.0001 -0.0002 0.8562 0.7784 0.7007 0.6999 0.7776 0.8556 0.8569 0.7792 0.7016 15 47.916 0.9795 -1.8E-5 8.5E-6 0.8638 0.7911 0.7183 0.7179 0.7907 0.8633 0.8642 0.7916 0.7187 17 46.948 0.9768 5.6E-5 0.0003 0.8632 0.7906 0.7278 0.7176 0.7902 0.8627 0.8636 0.7910 0.7180 18 42.121 0.9691 -0.0003 -0.0003 0.8548 0.7772 0.6996 0.6993 0.7769 0.8548 0.8549 0.7776 0.6999 20 48.126 0.9801 -0.0003 -0.0002 0.8539 0.7767 0.6991 0.6992 0.7768 0.8543 0.8536 0.7766 0.6989 23 48.441 0.9833 -2.8E-5 0.0001 0.8543 0.7770 0.6992 0.6988 0.7764 0.8537 0.8548 0.7775 0.6996 24 48.360 0.9809 -7.4E-5 -9.6E-5 0.8627 0.7902 0.7176 0.7175 0.7902 0.8627 0.8627 0.7903 0.7177 25 48.776 0.9800 -0.0001 -0.0002 0.8551 0.7776 0.6999 0.6991 0.7768 0.8545 0.8558 0.7784 0.7007 26 48.979 0.9882 -0.0004 -0.0004 0.8535 0.7763 0.6988 0.6985 0.7761 0.8537 0.8532 0.7764 0.6989 28 48.974 0.9848 -0.0002 -0.0003 0.8543 0.7769 0.6993 0.6992 0.7768 0.8545 0.8541 0.7769 0.6994 29 47.993 0.9811 1.4E-5 4.6E-5 0.8553 0.7776 0.6999 0.6994 0.7771 0.8548 0.8558 0.7781 0.7004 30 49.000 0.9868 -5.4E-5 -0.0002 0.8551 0.7775 0.6999 0.6990 0.7768 0.8544 0.8557 0.7782 0.7007 31 46.998 0.9753 -0.0001 -0.0002 0.8545 0.7771 0.6996 0.6988 0.7765 0.8540 0.8550 0.7778 0.7003

GPS mean 47.652 0.9782 -0.0001 -0.0001 0.856 0.7794 0.7029 0.7018 0.779 0.8558 0.8562 0.7797 0.7028

134 42.246 0.9320 -0.0006 -0.0003 0.9395 0.8720 0.7893 0.7878 0.8702 0.9382 0.9407 0.8737 0.7908

Table 4: Summary of standard deviation for 11 test metrics for 25 satellites observed at Sugadaira in Japan. PRN# C/N0 PwrR TM1 TM2 TM3 TM4 TM5 TM6 TM7 TM8 TM9 TM10 TM11

1 0.109 0.00417 0.00246 0.00307 0.00300 0.00330 0.00352 0.00515 0.00465 0.00408 0.00420 0.00477 0.00536 2 1.477 0.00203 0.00223 0.00279 0.0273 0.00302 0.00480 0.0042 0.00378 0.00373 0.00378 0.00430 0.00480 3 3.973 0.08491 0.00396 0.00500 0.00488 0.00531 0.00569 0.00853 0.00767 0.00662 0.00692 0.00785 0.00865 4 0.000 0.00486 0.00199 0.00249 0.00249 0.00277 0.00297 0.00429 0.00390 0.00338 0.00351 0.00396 0.00441 5 0.000 0.00495 0.00196 0.00241 0.00245 0.00265 0.00277 0.00417 0.00381 0.00326 0.00341 0.00376 0.00415 6 1.670 0.01161 0.00297 0.00364 0.00369 0.00410 0.00431 0.00633 0.00582 0.00504 0.00512 0.00580 0.00634 7 0.205 0.00327 0.00201 0.00256 0.00246 0.00275 0.00296 0.00440 0.00392 0.00332 0.00342 0.00391 0.00440 9 2.077 0.02224 0.00216 0.00275 0.00268 0.00299 0.00319 0.00473 0.00429 0.00363 0.00376 0.00430 0.00477 10 0.655 0.00501 0.00201 0.00251 0.00252 0.00276 0.00295 0.00421 0.00382 0.00338 0.00348 0.00398 0.00436 11 6.427 0.0742 0.00320 0.0041 0.00411 0.00452 0.00484 0.00723 0.00661 0.00566 0.00579 0.00664 0.00745 13 0.920 0.00339 0.00207 0.00266 0.00256 0.00282 0.00302 0.00462 0.00412 0.00351 0.00364 0.00419 0.00473 14 0.000 0.00490 0.00253 0.00313 0.00315 0.00350 0.00374 0.00544 0.00491 0.00429 0.00435 0.00481 0.00544 15 0.277 0.00232 0.00223 0.00273 0.00265 0.00305 0.00334 0.00474 0.00430 0.00372 0.00378 0.00438 0.00494 17 1.568 0.00471 0.0025 0.00312 0.00305 0.00331 0.00356 0.00523 0.00473 0.00405 0.00429 0.00472 0.00526 18 0.798 0.00490 0.00315 0.00375 0.00379 0.00412 0.00453 0.00663 0.00603 0.00504 0.00534 0.00602 0.00657 20 0.803 0.00327 0.00214 0.00274 0.00264 0.00295 0.00317 0.00474 0.00421 0.00363 0.00370 0.00428 0.00480 23 0.975 0.00600 0.00212 0.00266 0.00260 0.00285 0.00304 0.00449 0.00404 0.00354 0.00366 0.00412 0.00455 24 0.786 0.00335 0.00212 0.00270 0.00258 0.00288 0.00307 0.00458 0.00411 0.00356 0.00362 0.00420 0.00469 25 0.418 0.00446 0.00225 0.00288 0.00254 0.00289 0.00317 0.00517 0.00450 0.00371 0.00382 0.00453 0.00508 26 1.016 0.00381 0.00190 0.00237 0.00231 0.00255 0.00274 0.00402 0.00363 0.00314 0.00323 0.00368 0.00407 28 1.138 0.00502 0.00194 0.00247 0.00244 0.00272 0.00290 0.00423 0.00379 0.00326 0.00340 0.00390 0.00433 29 1.623 0.00318 0.00216 0.00273 0.00267 0.00294 0.00319 0.00461 0.00414 0.00360 0.00372 0.00422 0.00469 30 0.000 0.00467 0.00187 0.00235 0.00236 0.00257 0.00276 0.00400 0.00357 0.00323 0.00327 0.00371 0.00408 31 0.042 0.00505 0.00251 0.00316 0.00304 0.00337 0.00367 0.00546 0.00486 0.00423 0.00440 0.00501 0.00573

GPS mean 1.54 0.01151 0.00235 0.00295 0.00392 0.0032 0.0035 0.00505 0.00455 0.00394 0.00407 0.00463 0.00515

134 0.431 0.00862 0.00529 0.00719 0.00595 0.00661 0.00711 0.01454 0.01275 0.01022 0.01035 0.01302 0.01501

Table 5: Ideal correlation-curves for 25 satellites

PRN#1 PRN#2 PRN#3

PRN#4 PRN#5 PRN#6

PRN#7 PRN#9 PRN#10

PRN#11 PRN#13 PRN#14





PRN#134

SQM OBSERVATION DATA AT NISHINOMIYA We collected SQM data at Nishinomiya (Latitude 34.35° N, Longitude 135.35° E, Height 59.7 m) in August 2003. Figure 13 and 14 show the antenna site and experiment setups at Nishinomiya in Japan.

Figure 13: Antenna site at Nishinomiya

Figure 14: Experiment setups at Nishinomiya

Test Metrics for PRN-14 SQM Data About 6-hour full-path SQM data was recorded on 20 August, 2003. The elevation angle, C/N0, and power-ratio are shown in Figure 15. Figure 16 shows 11 test metrics for a full-path of PRN-14.

Antenna for SQM Receiver

SQM Receiver Monitor PC

Figure 15: Elevation angle, C/N0, and power-ratio for a

full-path of PRN-14 at Nishinomiya Figure 16: Test metrics; Up-Left:TM1 & TM2, Up-Right:

TM3, TM8, & TM9, Down-Left:TM4, TM7, & TM10, Down-Right: TM5, TM6, & TM11 for PRN-14

Figure 17 shows the standard deviation for 11 test metrics of PRN-14 as a function of elevation angle. The data was processed in 1-degree bins from 7 to 89 degrees.

Figure 17: Standard deviations for 11 test metrics of PRN-14 as a function of elevation angle

PRN-14 Elevation Angle, C/No, and Power Ratio of Punctual Point at Nishinomiya

0

10

20

30

40

50

60

70

80

90

100

15385 18989 22589 26189 29790 33391

time [sec]

Elea

vatio

n An

gle

[deg

], C

/No

[dBH

z].

0

0.2

0.4

0.6

0.8

1

1.2

Pow

er R

atio

C/NoElevation AnglePower Ratio

Test Metric #1, and #2

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

15385 18989 22589 26189 29790 33391

time [sec]

TM2

TM1

Test Metrics #3, #8, and #9

0.76

0.78

0.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

15385 18989 22589 26189 29790 33391

time [sec]

TM9

TM8

TM3

Test Metric #4,#7, and #10

0.65

0.7

0.75

0.8

0.85

0.9

0.95

15385 18989 22589 26189 29790 33391

time [sec]

TM10

TM7

TM4

Test Metric #5, #6, and #11

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

15385 18989 22589 26189 29790 33391

time [sec]

TM11TM6TM5

Standard Deviation of Test Metrics as a function of elevation angle

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

7 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87

Elevation Angle [deg]

TM1: ⊿ ±0.076725-⊿ ±0.05115TM2: ⊿ ±0.1023-⊿ ±0.05115TM3: R±0.05115TM4: R±0.076725TM5: R±0.1023TM6: R-0.1023TM7: R-0.076725TM8: R-0.05115TM9: R+0.05115TM10: R+0.076725TM11: R+0.1023

To exclude irregular data, curve-fit approach was employed. Third-order polynomials were fit to the measured test metric σ data as follows:

tscoefficien polynomialorder -3rd:,,,degreesin angleElevation :

)(

0123

012

23

3

aaaa

aaaaEL

θθθθθσ +++=

The results of the curve-fit are summarized in Table 6.

Table 6: 3rd-order polynomial coefficients of σ as a function of elevation angle

3a 2a 1a 0a

TM1 -6.18272E-08 1.17681E-05 -7.40747E-04 1.77266E-02 TM2 -9.95686E-08 1.88310E-05 -1.17788E-03 2.80970E-02 TM3 -4.40935E-08 9.23757E-06 -6.57261E-04 1.87271E-02 TM4 -5.89665E-08 1.19793E-05 -8.30140E-04 2.33430E-02 TM5 -8.29053E-08 1.61679E-05 -1.06342E-03 2.80266E-02 TM6 -1.72964E-07 3.29881E-05 -2.03772E-03 4.52156E-02 TM7 -1.49668E-07 2.84726E-05 -1.75375E-03 3.87539E-02 TM8 -1.11814E-07 2.15104E-05 -1.34356E-03 3.02077E-02 TM9 -8.08239E-08 1.63447E-05 -1.13333E-03 3.20136E-02 TM10 -1.07689E-07 2.15286E-05 -1.49356E-03 4.32447E-02 TM11 -1.55558E-07 3.01915E-05 -2.00958E-03 5.52182E-02 Sample statistics of 11 test metrics for PRN-14 are shown in Figure 18. At the top of the each histogram, a pair of values is shown next to the title. The left one is mean and the right one is standard deviation. The first histogram (top-left) corresponds to TM1, and the last one (bottom-right) corresponds to TM11.

Figure 18: 11 test metrics distributions of the measured data of PRN-14 at Nishinomiya

TM1 (-0.000077, +0.004554)

0

2000

4000

6000

8000

10000

12000-0

.05

-0.0

4

-0.0

3

-0.0

2

-0.0

1

0.00

0.01

0.02

0.03

0.04

0.05

TM2 (+0.00024, +0.007301)

0100020003000400050006000700080009000

-0.0

5

-0.0

4

-0.0

3

-0.0

2

-0.0

1

0.00

0.01

0.02

0.03

0.04

0.05

TM3 (+0.86074, +0.00607)

0100020003000400050006000700080009000

0.82

0.83

0.84

0.85

0.86

0.87

0.88

0.89

0.90

0.91

0.92

TM4 (+0.782592, +0.007578)

0100020003000400050006000700080009000

0.74

0.75

0.76

0.77

0.78

0.79 0.8

0.81

0.82

0.83

0.84

TM5 (+0.704693, +0.008489)

010002000300040005000600070008000

0.66

0.67

0.68

0.69

0.70

0.71

0.72

0.73

0.74

0.75

0.76

TM6 (+0.704022, +0.009823)

010002000300040005000600070008000

0.66

0.67

0.68

0.69

0.70

0.71

0.72

0.73

0.74

0.75

0.76

TM7 (+0.782084, +0.008386)

0100020003000400050006000700080009000

0.74

0.75

0.76

0.77

0.78

0.79

0.80

0.81

0.82

0.83

0.84

TM8 (+0.860318, +0.006769)

0100020003000400050006000700080009000

0.81

0.82

0.83

0.84

0.85

0.86

0.87

0.88

0.89

0.90

0.91

TM9 (+0.86118, +0.010533)

0

1000

2000

3000

4000

5000

6000

7000

0.82

0.83

0.84

0.85

0.86

0.87

0.88

0.89

0.90

0.91

0.92

TM10 (+0.7831, +0.01486)

0500

100015002000250030003500400045005000

0.74

0.75

0.76

0.77

0.78

0.79

0.80

0.81

0.82

0.83

0.84

TM11 (+0.705364, +0.018065)

0500

1000150020002500300035004000

0.66

0.67

0.68

0.69

0.70

0.71

0.72

0.73

0.74

0.75

0.76

We also examined standard deviations of 11 test metrics as a function of C/N0 and power-ratio respectively. Figure 19 and 20 show σ as a function of C/N0 and power-ratio respectively. Then, the same curve-fit approaches were employed. The results are shown in Table 7 and 8.

tscoefficien polynomialorder -3rd:,,, PowerRatioor [dBHz] C/N :

)(

0123

0

012

23

3

aaaax

axaxaxax +++=σ

Figure 19: Test metrics σ as a function of C/N0

Standard Deviation of Test Metrics as a function of C/No

0

0.01

0.02

0.03

0.04

0.05

0.06

27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

C/No [dBHz]


Table 7: 3rd-order polynomial coefficients of σ as a function of C/N0

3a 2a 1a 0a

TM1 3.37220E-06 -3.85382E-04 1.37675E-02 -1.43840E-01 TM2 5.71115E-06 -6.47890E-04 2.29392E-02 -2.36814E-01 TM3 2.02958E-06 -2.37324E-04 8.44252E-03 -7.98806E-02 TM4 3.65012E-06 -4.26164E-04 1.54631E-02 -1.60291E-01 TM5 5.16639E-06 -6.01884E-04 2.19906E-02 -2.36030E-01 TM6 7.70892E-06 -8.68422E-04 3.03774E-02 -3.05976E-01 TM7 5.25726E-06 -5.84421E-04 1.98577E-02 -1.84701E-01 TM8 3.34324E-06 -3.58496E-04 1.13252E-02 -8.46302E-02 TM9 5.36768E-06 -6.26368E-04 2.27624E-02 -2.37978E-01 TM10 7.45928E-06 -8.65921E-04 3.12223E-02 -3.21354E-01 TM11 1.09425E-05 -1.26344E-03 4.55639E-02 -4.77727E-01

Figure 20: Test metrics σ as a function of power-ratio

Standard Deviation of Test Metrics as a function of power ratio(I2-Q2)/(I2+Q2) at punctual point

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.31 0.36 0.41 0.46 0.51 0.56 0.61 0.66 0.71 0.76 0.81 0.86 0.91 0.96Power Ratio


Table 8: 3rd-order polynomial coefficients of σ as a function of power-ratio

3a 2a 1a 0a

TM1 2.83397E-03 -1.69635E-02 -7.39074E-04 1.72152E-02 TM2 4.35423E-02 -1.12850E-01 5.80329E-02 1.50619E-02 TM3 -7.08365E-02 1.37237E-01 -1.06727E-01 4.33742E-02 TM4 8.61889E-03 -2.88340E-02 -3.42638E-03 2.82142E-02 TM5 5.95692E-02 -1.35855E-01 6.38641E-02 1.79288E-02 TM6 6.08983E-03 -3.30297E-02 -1.18309E-02 4.32967E-02 TM7 -1.01182E-01 2.02284E-01 -1.69867E-01 7.19343E-02 TM8 -1.77496E-01 3.68825E-01 -2.78745E-01 8.91811E-02 TM9 -6.09175E-02 1.23821E-01 -1.23427E-01 6.65174E-02 TM10 -5.10553E-03 7.27300E-04 -5.50327E-02 6.82489E-02 TM11 6.13763E-02 -1.54790E-01 4.65544E-02 5.74461E-02

I-Q Data for PRN-14 I-Q plots for both punctual point and chip-offset +1.023 chip from punctual point are shown in Figure 21. For the +1.023 chip-offset point, the distribution of In-phase data is shown in Figure 22. It's a Gaussian distribution of noise level.

Figure 21: I-Q plots, Left: at the punctual point, Right: at

the +1.023 chip-offset point

Figure 22: In-phase distribution at the +1.023 chip-offset-

The standard deviation of In-phase data at typical 20 offset points (±0.025575, ±0.05115, ±0.076725, ±0.1023, ±0.2046, ±0.4092, ±0.588225, ±0.792825, ±0.997425, ±1.508925) are drawn as a function of elevation angle in Figure 23 (a) and (b).

Standard Deviation for In-phase normalized correlator outputschip-offset from 0.026 to 0.2 as a function of elevation angle

0

0.01

0.02

0.03

0.04

0.05

0.06

6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86

elevation angle [deg]

+0.026chip-0.026chip+0.051chip-0.051chip+0.077chip-0.077chip+0.102chip-0.102chip+0.205chip-0.205chip

Figure 23 (a): σ of In-phase data from -0.2 to +0.2

chip-offset

Figure 23 (b): σ of In-phase data from ±0.4 to ±1.5

chip-offset Sample statistics of the correlation peak variation of PRN-14 is shown Figure 24. The first (top-left) histogram corresponds to the correlator at the �0.025575 chip-offset. The last histogram (bottom-right) corresponds to the correlator at the +1.508925 chip-offset. Sample mean correlation-curves and standard deviation-curves are shown in Figure 25. The first (top-left) graph corresponds to the elevation angle equals to 6 degree. The last graph (bottom-right) corresponds to the elevation angle equals to 89 degree.

Standard Deviation for In-phase normalized correlator outputschip-offset from 0.4 to 1.5 as a function of elevation angle

0

0.02

0.04

0.06

0.08

0.1

0.12

6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86


+0.409chip-0.409chip+0.588chip-0.588chip+0.793chip-0.793chip+0.997chip-0.997chip+1.509chip-1.509chip

Figure 24 : Correlation peak measurement histograms for PRN-14

@-0.026 chip-offset (+0.98857, 0.006114)

02000400060008000

10000120001400016000

0.94 0.99 1.04Normalized In-phase

@-0.051 chip-offset (+0.964479, +0.008663)

0

2000

4000

6000

8000

10000

12000

14000


@+0.051 chip-offset (+0.964557, +0.009882)

0

2000

4000

6000

8000

10000

12000

14000


@+0.026 chip-offset (+0.987686, +0.007256)

02000400060008000

1000012000140001600018000


@-0.077 chip-offset (+0.938573, +0.009432)

0

2000

4000

6000

8000

10000

12000

14000


@+0.077 chip-offset (+0.939196, +0.012294)

0

2000

4000

6000

8000

10000

12000


@-0.102 chip-offset (+0.912366, +0.010379)

0

2000

4000

6000

8000

10000

12000


@+0.102 chip-offset (+0.913201, +0.01304)

0100020003000400050006000700080009000

10000


@-0.205 chip-offset (+0.808275, +0.014838)

0100020003000400050006000700080009000

10000


@+0.205 chip-offset (+0.809491, +0.020371)

0

1000

2000

3000

4000

5000

6000


@-0.409 chip-offset (+0.599954, +0.023789)

0

1000

2000

3000

4000

5000

6000

7000


@+0.409 chip-offset (+0.601508, +0.030709)

0

500

1000

1500

2000

2500

3000

3500


@-0.588 chip-offset (+0.417895, +0.02811)

0

1000

2000

3000

4000

5000

6000


@+0.588 chip-offset (+0.419412, +0.035895)

0

500

1000

1500

2000

2500

3000

3500


@-0.792 chip-offset (+0.209642, +0.029424)

0

1000

2000

3000

4000

5000

6000


@+0.792 chip-offset (+0.210888, +0.035747)

0

500

1000

1500

2000

2500

3000


@-0.997 chip-offset (+0.00906, +0.030196)

0

1000

2000

3000

4000

5000

6000

-0.04 0.01 0.06Normalized In-phase

@+0.997 chip-offset (+0.001266, +0.03658)

0

500

1000

1500

2000

2500

3000

3500


@-1.509 chip-offset (-0.00087, +0.031603)

0

1000

2000

3000

4000

5000

6000

-0.05 0 0.05Normalized In-phase

@+1.509 chip-offset (-0.00155, +0.031291)

0500

10001500200025003000350040004500


Figure 25 : Mean and standard deviation curves for typical elevation angles for PRN-14

Elevation Angle = 6 deg

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Ave.σ

Ave. C/No = 31.08 dBHzAve. Power Ratio = 0.3388


-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Ave.σ



-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.02

0.04

0.06

0.08

0.1

0.12

Ave.σ

Ave. C/No = 31.35 dBHzAve. Power Ratio =


-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.02

0.04

0.06

0.08

0.1

0.12

Ave.σ

Ave. C/No = 31.35 dBHzAve. Power Ratio =0 3777


-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Ave.σ



-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Ave.σ



-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

Ave.σ



-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

Ave.σ



-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

Ave.σ



-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.005

0.01

0.015

0.02

0.025

Ave.σ



-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.005

0.01

0.015

0.02

0.025

Ave.σ

Ave. C/No = 44.63 dBHzAve. Power Ratio =0 9611


-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

Ave.σ



-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

Ave.σ



-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.01

Ave.σ



-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.01

Ave.σ



-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.01

Ave.σ


SBAS Satellites SQM Data We observed 2 real SBAS satellites, WAAS POR and EGNOS IOR. EGNOS IOR is still under test mode. From Japan, its elevation angle less than 10 degree. Therefore, the value of C/N0 is very small and hard to keep signal tracking stably by SQM receiver. The mean and the standard deviation of the both satellites' correlation-curves are shown in Figure 26 and 27 respectively. Figure 28 shows screen shot of IOR correlation-curve by

accumulated-display mode.

Figure 26 : WAAS POR (PRN-134)Mean and standard deviation of each correlation point

POR (PRN-134) Mean & Standard Deviation

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

chip offset [chip]

Nor

mal

ized

In-p

hase

0

0.005

0.01

0.015

0.02

0.025

0.03

Ave.

σ

Ave.El = 29.65 degAve.PowerRatio=0.8414Ave.C/No=37.59 dBHz

Figure 27 : EGNOS IOR (PRN-131)Mean and standard

deviation of each correlation point

Figure 28 : EGNOS IOR (PRN-131) correlation curve

IOR (PRN-131) 6H Mean Correlation curve & standard deviation

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

chip offset [chip]

Nor

mal

ized

In-p

hase

.

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Ave

σ

Ave. C/No=31.70 dBHz

Ave. PowerRatio=0.407

SQM DATA FROM GPS/SBAS SIMULATOR As shown in Figure 11, real signal from POR does not have side-lobes in its spectrum. Therefore, the correlation peaks for such SBAS satellites result in very dull edge. It has same effect that happens in GPS narrowband receiver. [1] Using GPS/SBAS simulator (GSS STR4760), SBAS signal results in sharp edge like GPS. Figure 29 compares the real IOR's correlation peaks and those of simulator.

Figure 29 : IOR Correlation peaks by real IOR and by

simulator SQM Data with Multipath We ran a scenario that starts multipath-off, then it puts multipath into PRN-6 for about 10 minutes and turn off the multipath from PRN-6. As a typical test metric, we picked up TM1 and compared smoothed data with non-smoothed (raw) data. Figure 30 shows its result with 3 dimensional positioning errors. It is obvious that using smoothing method delays detection of signal anomalies. 100 s smoothing time caused 46.4 s delay for detection of the multipath. The detection threshold was calculated with data without multipath. Figure 31 zooms the transient performance on the timing of multipath-on for test metic#1. For fixed-type multipath, like simulator generates, using raw In-phase data shorten the detection time. However, in the real world, such multipath rarely exists.

Figure 30 : TM1 variance with 3D error during mutipath off/on/off scenario

Real IOR vs Simulator

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.61 -1.41 -1.20 -1.00 -0.79 -0.59 -0.38 -0.18 0.03 0.23 0.43 0.64 0.84 1.05 1.25 1.46

chip offset [chip]

Nor

mal

ized

In-p

hase

Simulator C/No=34 dBHz

Real IOR C/No=32 dBHz

TM1 by Raw and Smoothed In-pahse data, and 3 Dimensional Error

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

1

301

601

901

1201

1501

1801

2101

2401

2701

3001

3301

3601

3901

4201

4501

4801

5101

5401

5701

6001

6301

6601

6901

7201

7501

7801

8101

time [0.2 sec].

TM1

0

1

2

3

4

5

6

7

8

9

3D e

rror

[m]

Raw TM1

Smoothing 100 sec TM1

3D Positioning Error

Multipath ON Multipath OFF

Threshold: +8.35σ

Using smoothedIn-phase data:46.4 sec Delay todetect anomaly bymultipath

Figure 31 : Zoomed TM1 transient performance for raw data and smoothed data when multipath turned on 11 test metrics are shown in Figure 32. PC monitor screen for correlation-curve for this scenario is shown in Figure 33.

Figure 32 : 11 test metrics variations for multipath-on

Figure 33 : Correlation peaks variation multipath off to

on Correlation coefficient between ideal currelation-curve obtained at Sugadaira/simulator and real correlation-curve which obtained at Nishinomiya is shown in Figure 34 as a function of elevation angle. Correlation-curves of elevation angle greater than 15 degree are pretty matched with ideal curves.

Multipath OFF → ON Test Metric#1 Zoomed

-0.04

-0.03

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Using raw In-phase data:Minimum Delay (0.2 sec) todetect anomaly by multipath

11 Test Metrics for Multipath OFF/ON by simulator ( GPS PRN-6 )

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Figure 34 : Correlation coefficients between ideal curve and real one as a function of elevation angle

CONCLUSIONS AND FUTURE WORK This paper describes prototype SQM receiver developed by ENRI and FURUNO. It has two dedicated SQM channels and each channel has 254 correlators for both In-phase and Quadrature output. Using TDMSM mode, up to 10 satellites can be monitored at 1 Hz. All the data presented in this paper were used SQM Normal mode. We're planning to use software arbitrary GPS signal generator and vector generator in order to generate Evil Waveforms and examine ICAO thread models. We will also collect more SQM data at more sites. The correlator allocation design will be modified so that it meets user requirements and ICAO recommendations. Next version will cover full 16 tracking channels at more than 2 Hz update rate. ACKNOWLEDGEMENTS The authors would like to acknowledge that Dr. Tomizawa Ichiro and his team in the University of Electro-Communications cooperated with ENRI to obtain the ideal SQM data at Sugadaira Space Radio Observatory. Thanks also to all the members of Lab. Section 3 in FURUNO, especially to Mr. Yasunobu Matsuo, Mr. Yohji Maruono, Mr. Katsuhisa Yamashina, Mr. Tsutomu Okada, and Ms. Naomi Fujisawa.

PRN-14 Correlation Coefficient of 127 correlator outputs (1) between datacollected at Sugadaira and Nishinomiya, and (2) between data collected from

Simulator and real satellite as a function of elevation angle.

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(1) Sugadaira (C/No=47.00dBHz) vs Nishinomiya Real SV

(2) Simulator (C/No=40.44dBHz) vs Nishinomiya Real SV

Note: correlation coefficient between Sugadaira and Simulator = 0.999998

REFERENCES [1] Robert Eric Phelts, �MULTICORRELATOR

TECHNIQUES FOR ROBUST MITIGATION OF THREATS TO GPS SIGNAL QUALITY� June 2001 [2] Alexander M. Mitelman, et al �A Real-Time Signal Quality Monitor For GPS Augmentation Systems�, Page 862-871 Proceedings of the ION GPS 2000, 19-22 September 2000, Salt Lake City, UT

[3] R. Eric Phelts, et al �Transient Performance Analysis of a Multicorrelator Signal Quality Monitor�, Page 1700-1710 Proceedings of the ION GPS 2001, 11-14 September 2001, Salt Lake City, UT

[4] Benoit Roturier, et al �The SBAS Integrity Concept Standardised by ICAO. Application to EGNOS�, Web ESTB

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