An Interference Monitoring System For GNSS Reference Stations

Jan Wendel, Christian KurzhalsAstrium GmbH

Taufkirchen, Germany

Miroslav HoudekIguassu Software Systems a.s.

Prague, Czech Republic

Jaron SamsonESA ESTEC / TEC-ETN

Noordwijk, The Netherlands

Abstract— Interferences present a serious threat to GNSS reference stations like EGNOS RIMS and Galileo GSS, because undetected degradations in reference station measurements potentially impact the quality of the information provided via the navigation / service messages, which in turn can affect the whole user community. In order to mitigate this threat, it is necessary to monitor the interference environment at these reference stations, which generates situational awareness of possible measurementquality deteriorations. This paper gives an overview on an interference monitoring system developed by Astrium GmbH and Iguassu Software Systems a.s. for ESA.

Keywords-Interference monitoring, GNSS reference station, RIMS, GSS, EGNOS, Galileo

I. INTRODUCTION

Intentional and unintentional interference presents a serious threat for users and operators of satellite navigation systems. Intentional interference or jamming refers to the deliberate emission of radio frequency (RF) signals with the aim to prevent acquisition and tracking of Global Navigation Satellite System (GNSS) signals. Jammers can be categorized according to the type of signal that is emitted, e.g. noise like broadband, continuous wave (CW), or matched modulation. For a given jammer power, the latter achieves the largest denial range. Unintentional interference can have different origins: First of all, the Radio Navigation Satellite Service (RNSS) bands are shared with other services like Aeronautical Radio Navigation Service (ARNS) or earth exploration satellites. Second, out-of-band emission from equipment operating in neighboring frequency bands, harmonics from equipment operating at lower frequencies, and malfunctioning equipment can act as unintentional sources of interference, too.

A variety of off-the-shelf interference monitoring systems are offered by different companies, from low-cost devices only capable of detecting additional RF power within a frequency band, up to systems that provide spectral information, interference characteristics, modulation recognition etc. In Germany, the Bundesnetzagentur is operating a RF monitoring network, and the JLOC system [1] developed for the US Air

Force Research Laboratory is another example for a RF threat detection network.

However, regarding Galileo Sensor Stations (GSS) and EGNOS Ranging and Integrity Monitoring Stations (RIMS), situational awareness of the interference environment and especially the ability to assess the impact of interferences on the performance of the receivers operated at these reference stations is of paramount importance, because undetected degradations in reference station measurements potentially impact the quality of the information provided via the navigation / service messages, which in turn can affect the whole user community. Astrium GmbH and Iguassu Software Systems a.s. have developed an Interference Monitoring System (IMS) for the European Space Agency (ESA), which is dedicated to protect Galileo GSS and EGNOS RIMS sensor stations by providing the aforementioned information to theGalileo or EGNOS operator. Hereby, the IMS is working independently from the systems it is designed to protect, which emphasizes that an adaption of the IMS to other interference monitoring missions is easily achievable. It is understood that the alternative to an interference monitoring system for theprotection of sensor stations would be a monitoring of the sensor station measurements. However, this does not provide unambiguous results like the IMS, because interference is not the only cause for C/N0 degradations: multipath, attenuations due to foliage, and other mechanisms can have a similar effect.

In this paper, the IMS is described. The architecture of the system is discussed, and the approach for detection and characterization of interferences is outlined. Representative results of the IMS demonstration phase are given, too.

The work reported in this paper has been supported under a contract of the European Space Agency in the frame of the European GNSS Evolutions Programme. The views presented in this document represent solely the opinion of the authors and should be considered as R&D results not necessarily impacting the present EGNOS and Galileo system design.

II. IMS ARCHITECTURE

The IMS consists of a Processing Facility (PF) and several Local Elements (LE). The LEs act as sensor nodes which autonomously monitor the local interference environment and analyze interference events. The PF is the “central node” of the system, it collects and stores the information provided by the LEs and allows to instantly view the interference environment at every LE. Furthermore, the PF allows configuring the LEs from remote according to the user’s needs, so a local operator at the LE is not required. Fig. 1 shows a block diagram of the prototype IMS system, which currently consists of three LEs,

Figure 1. Block diagram of currently deployed IMS architecture

one at the EGNOS RIMS in Warsaw, one at the GCC in Oberpfaffenhofen, and one at ESA ESTEC in Noordwijk. The PF is located at ESA ESTEC, too. The LEs are connected to the PF via internet, at the Warsaw site additionally a VSAT connection is available. First of all, the redundancy established with the possibility to connect a LE to PF either via VSAT or via landline demonstrates that at the PF a high availability of relevant LE information can be achieved, second it proves the IMS does not need to rely on existing infrastructure. The switching to VSAT is performed automatically in case the landline is lost, which was proven in lab tests. However, at Warsaw the switching to VSAT was performed manually for demonstration purposes only, as the landline was continuously available. The IMS prototype was operated for a six-month demonstration period to prove the functionality of the systemwhich was completed by the end of July 2012. Since then, the IMS continues regular operations.

A. IMS Local Element ArchitectureEach LE consists of an outdoor unit including an antenna

and appropriate signal conditioning equipment, and an indoor rack which is shown in Fig. 2. The indoor rack contains a RF frontend which is connected to the outdoor unit and provides the RF signal to a signal analyzer. A NTP server allows for precise absolute time tagging of the signal analyzer measurements, and a remote power switch allows to power up and down every component in the rack, also remotely from PF.A rack-mount server is used to analyze the spectrum and the IQdata measurements provided by the signal analyzer. Finally, a rack-mount console (LCD, keyboard and touchpad) is included to allow for operation and maintenance of the system by a local operator, too.

Figure 2. IMS LE indoor rack

B. LE Measurement Chain While the signal analyser employed in this system is

obviously a COTS product, the outdoor unit and the RF front-end are dedicated developments for the IMS. The RF front-end contains several low noise amplifiers (LNA) and step attenuators, which allow to precisely control the overall gain of the RF front-end to achieve the best possible sensitivity when performing measurements with the signal analyser. This variability in gain is required for two reasons: First of all, the interference environment changes with time. Second, the RF front-end contains a switchable filter bank which provides a wideband path covering 900 MHz – 1800 MHz, and dedicated paths for the GNSS bands L1, L2, L5 and E6. The filters used in these paths provide a stop band attenuation of more than 55 dB. In case the wideband path is selected, less RF front-end gain has to be used in order to avoid that high power GSM and UMTS signals saturate the signal analyser, while considerable more gain can be used when the RF front-end is switched to one of the GNSS band filters, which significantly increases the overall measurement sensitivity.

Figure 3. IMS indoor equipment block diagram

After the L1 filter, a splitter is used to provide a signal to the timing receiver, which acts as a NTP server. A block diagram of the indoor equipment is shown in Fig. 3.

C. LE Calibration An important aspect of the IMS is the measurement chain

calibration: The attenuation of the step attenuators and the gain of the LNAs are not known perfectly and are expected to vary, the antenna cable introduces an only approximately known frequency dependent attenuation which is different from LE to LE, and the signal analyser's quartz oscillator is prone to aging, which – if not compensated – leads to errors in the observed interference frequencies. Therefore, besides the possibility to trigger the signal analyser's internal calibration routines, additional means for calibrating the measurement chain have been implemented: The outdoor unit contains a switch, which allows to chose whether the antenna or a 50 Ohm termination is connected to the antenna cable. In order to calibrate for attenuator and LNA imperfections as well as the frequency dependent attenuation of the antenna cable, the 50 Ohm termination is selected and spectrum measurements are performed with the signal analyser. The comparison of these measurements with the expected results allows to determine the required calibration parameters.

Figure 4. IMS LE GPS CA code acquisition results

The frequency error of the signal analyser's quartz oscillator is calibrated as follows: With the outdoor unit switched to the antenna and the L1 path selected in the RF front-end, IQ data is acquired. The IMS software downloads GPS ephemeris data at regular intervals, and based on this information, a CA code acquisition is performed for the GPS satellites which are expected to be visible. For illustration, Fig. 4 shows a real-live example for the outcome of this acquisition process, clearly revealing the Doppler frequency of several GPS satellites.Then, in the next step the Doppler frequency provided by this acquisition process is compared with the expected Doppler calculated from the Ephemeris data and the LE site position.This allows to calculate the signal analyser's frequency error, which is compensated by adjusting the measurement acquisition process during regular monitoring operations accordingly.

III. INTERFERENCE DETECTION AND CHARACTERISATION

Each IMS LE monitors the frequency range from 900 MHz to 1800 MHz, but could be used to monitor much higher frequency bands, too, given that the bandpass filter in the outdoor unit is adapted accordingly. A variety of interference detection and characterization approaches can be found in literature. In [2],[3], multi-correlator GPS receivers are used which allow to sample the correlation function between replica and received signal at several points. Interferences cause distortions to this correlation function, therefore a comparison between expected and observed correlation function reveals the presence of interferences. The assessment of the characteristics of the distortions allows for a limited interference characterization, too. In [4], the variations of a GPS receiver's automatic gain control state is assessed, which is shown to be more sensitive and much faster for interference detection purposes than a C/N0 monitoring. However, both approaches are restricted to GNSS bands only. More general approaches are discussed in [5],[6], which can use either GNSS receiver baseband samples or signal analyzer IQ data as input. In [5], the Kurtosis of this data is calculated. Without an interference, the received signal can be modeled as Gaussian noise, which has a known Kurtosis. If the observed Kurtosis deviates significantly from the Kurtosis of a Gaussian distribution, the presence of an interference is detected. However, this approach fails for wide-band noise like interferers and does not provide any information on interference characteristics. The interference detection approach discussed in [6] is an example of a transformation technique, which play a very important role in interference detection. The short-term Fourier transform (STFT), the spectral correlation density and the Wigner-Ville transformation belong to this category, and all of them have the ability to provide time and frequency domain characteristics of the input signal, and are therefore suited for detection as well as for characterization purposes. The most straight forward of these techniques is the STFT. Other techniques might show some very desirable characteristics which outperform the STFT capabilities, but are often restricted to very specific detection problems, are computationally extremely demanding, or suffer in practical situations from severe drawbacks. For example, the Wigner-Ville transformation offers a better time-frequency resolution than the STFT and does not require a window function to be used. On the other hand, if more than one signal is contained within the IQ data bandwidth, the so-called cross-term interference gives rise to ghost signals, which are very difficult to distinguish from real signals. However, as the STFT trades in time-domain resolution against frequency domain resolution and vice versa in a rather unflexible way, the IMS uses a different approach for interference detection and characterization, which is outlined in the following.

A. Spectrum MeasurmementsThe first step to detect interferences is to acquire spectra

measurements covering the complete frequency range of interest. Hereby, maximum and average detectors are used, as these have complementary characteristics regarding their detection capabilities: Pulsed interferences with a low duty cycle might not significantly increase the average power within the interference bandwidth, therefore a detection of these interferences using the average detector is not possible, while

these interferences might be easily visible using the maximum detector. On the other hand, the maximum detector suffers from an increased noise floor, burying interferences of lower power, which might be visible using the average detector, of course depending on their power level and duty cycle. An example of these complementary detector characteristics isshown in Fig. 5. First of all, it can be observed that the noise floor of maximum detector is larger than the noise floor of the average detector. The maximum detector is denoted with POS, the average detector with RMS. Second, at 1136 MHz, the maximum detector reveals the presence of an interference, which is not visible from the average detector. From the discussion above it is obvious that this interference must be pulsed and has a low duty cycle.

Different detection algorithms are used to search for interferences in the spectra provided by the average andmaximum detectors, for example assessing variations of the

Figure 5. Spectra captured with maximum (POS) and average (RMS) detectors, real life data

spectra power levels within a certain bandwidth, or checking for the excess of an appropriate threshold. The result of this process is a list of frequency bands, where interferers are suspected.

B. IQ data acquisitionFor each of these frequency bands where interferers are

suspected, IQ data are acquired and analyzed to confirm the presence of interferences and to identify the interference characteristics. Hereby, the Welch periodogram is used to determine the number of spectrally separated interferences within the acquired IQ data bandwidth, and to identify center frequency and bandwidth of each of these interferences. As an example, Fig. 6 shows for real life date acquired in E5b the Welch periodogram, as well as the automatically identified interferers, which are marked by a horizontal line showing the estimated bandwidth of each interferer. After identification of the interferers from the Welch periodogram, a bandpass filtering of the IQ data is performed in order to obtain IQ data with all interferences wiped out, except for the one of which the time domain characteristics shall be identified. In order to identify the time domain characteristics of the remaining

Figure 6. Interferers identified from IQ data using the Welch periodogram, horizontal lines indicate the estimated RFI bandwidths

Figure 7. Instantaneous power of bandpassed IQ data, revealing the interference to consist of a double pulse

interference, the instantaneous power is calculated, which reveals whether this interference is continuous or pulsed, and for the latter case also allows to identify the time intervals during which a pulse is present. Fig. 7 shows an example of the instantaneous power of one of the interferences visible in Fig. 6, which reveals that the interference is a double pulse, and allows assessing pulse power and pulse duration.

Depending on power level and pulse duration, the pulses are assigned to different pulse groups, each of which is considered to be a separate interferer. For each of these pulse groups, the duty cycle is determined, and, in case of regular pulse intervals, also the pulse repetition time. Furthermore, special routines are used to detect double pulses. Finally, the instantaneous frequency is analyzed, which allows to identify different types of chirps.

C. Identification of the RFI sourceThe identified interference characteristics are compared

with a database in order to associate the observed interference

with a specific RFI source. In the example shown in Fig. 7, a double pulse with a pulse spacing of 36 µs was found, which indicates an airborne DME equipment in Y-Mode as RFI source. Of course, the identification of the RFI source from the observed characteristics is not always possible.

D. Prediction of RFI impact on reference station receiverThe identified interference characteristics are also used to

predict for each GNSS band the impact of the identified interferences on the performance of the reference station GNSS receiver. For this task, the closed-form analytical expressions given in [7] have been augmented to be able to handle pulsed interferences, as well as receivers performing pulse blanking as a means of interference mitigation. Based on receiver parameters like front-end bandwidth, early-late spacing, loop filter bandwidth, pre-detection integration time, pulse blanking threshold, and GNSS signal code rate, the degradation in C/N0, the code jitter, the carrier jitter, and the impact of the interference on the availability of the observables and the possibility for re-acquisition is calculated. The impact prediction results obtained this way allow for a fast detection of potential problems at the Galileo (GSS) and EGNOS (RIMS) sensor stations where the IMS LEs are installed. Such potential problems are communicated to the IMS PF, which, depending on the severity of the interference event, can sent out alarm e-mails. Besides from impact prediction results, these alarms can also be triggered when interference threshold masks are

Figure 8. Spectra statistics included in IMS LE daily reports

Figure 9. Visualization of interference impact on reference station receiver performance in IMS LE daily report.

violated. These interference threshold masks specify the maximum level of interference, where a receiver can still achieve its minimum required performances, see [8],[9].

All information collected by a LE is used to generate a detailed report on the local interference environment with a summary at a daily, monthly, and yearly basis. These reports include status information on the LE itself, statistical information like the 95%, 99% and 100% percentiles of the spectra power levels, see Fig. 8 for an example, and detailed information on the time and frequency domain characteristics of critical interference events. The predicted impact of the observed interferences on the reference station receiverperformance is contained in these reports, too, see Fig. 9. At the PF, reports are generated which provide a general overview of this information for all LEs.

IV. INTERFERENCE EVENTS DETECTED DURING THE IMSDEMONSTRATION PHASE

A major purpose of the IMS demonstration phase was to prove that the IMS system is able to detect and characterize interference events, and to predict their impact on the performance of a reference station receiver – all automatically without operator intervention required.

During the demonstration phase, where the three LEs at Warsaw, Noordwijk and Oberpfaffenhofen were operated for six month connected to a PF at Noordwijk, a variety of interference events was observed, for which the IMS predicted a significant impact on the reference station receiver. For comparison, actual reference station receiver data from the GIOVE network was available, and ESSP providedinformation on the EGNOS RIMS receiver performance in Warsaw for the periods in time during which interference events were observed at that site.

For illustration, two interference events detected at the Warsaw site shall be discussed briefly. On 17.03.2012, a pulsed interference was detected with approximately 1 MHz bandwidth, the center frequency was 1234.5 MHz, i.e. within the L2 band. The instantaneous power of this RFI is shown in Fig. 10.

Figure 10. Instantaneous power of Warsaw L2 interference on 17.03.2012

ESSP provided RIMS A and RIMS B receiver data for this interference event. The RIMS A receiver performs the measurements to GPS/GLONASS/GEO satellites, which are used by the main processing platform for message generation. It uses a semi-codeless L2 tracking and a double delta correlator. The RIMS B receiver is connected to a parallel processing platform and used to verify the data produced by the main processing platform. It uses adaptive semi-codeless L1/L2 narrow correlator. The impact prediction algorithms of the IMS are applicable for the RIMS B receiver. The RIMS B receiver showed a degradation in C/N0 of 18 dB, while the IMS predicted during the presence of the interference degradations between 16.4 dB and 19.2 dB, which is an excellent agreement.

Besides the RIMS receiver data, also data from the Giove network receiver was available. The C/N0 calculated by this receiver is shown in Fig. 11, clearly showing the impact of the interference event. However, as the Giove receiver obviously lost lock, a comparison with the predicted C/N0 could not be performed.

Figure 11. C/N0 estimated by the Giove network receiver

As a second example, an interference event detected at the Warsaw site on 13.04.2012 in E5b shall be addressed. The power spectral density calculated from IQ data is shown in Fig. 12, which reveals the presence of three interferers, which were all pulsed with varying pulse durations and duty cycles. Again, the predicted degradation in C/N0 was up to a few dB in agreement with the actual impact experienced by the Giove network receiver. Fortunately, the receiver did not loose track during this interference event, therefore the actual code and carrier jitter could be compared to the predictions provided by the IMS. Fig.13 shows a clearly visible increase in code jitter of this receiver, which is in very good agreement with the IMS predictions which are shown, too. However, Warsaw actually was the most quiet site regarding interference during the IMS demonstration period. Significant interference events where detected at the Noordwijk site in E5b, which are omitted herefor brevity. The most severe interference events were observed at Oberpfaffenhofen in E6. Unfortunately, for E6, no reference

Figure 12. PSD calculated from IQ data for the Warsaw RFI on 13.04.2012. The RFI bandwidth estimated by the IMS is marked with horizontal lines.

Figure 13. Giove receiver code jitter and the corresponding IMS prediction during the Warsaw E5b RFI event on 13.04.2012.

station receiver data was available at Oberpfaffenhofen for comparison.

In summary, the IMS demonstration phase was an impressive success. It was actually possible to operate the system, the expected functionalities were all available, and the capabilities of the system regarding interference detection, characterization, and impact prediction could be demonstrated. The automatic identification of interference sources also performed as expected, especially for DME the assignment to a specific source, i.e. the DME station from which the signal was originating, could be accomplished frequently. The operation of LEs by PF from remote was also used extensively and proved its maturity. One of the most important observations during the demonstration phase of the IMS was that the interference environment at a reference station site is in general not stable at all. At reference station sites where nointerferences might be observed in a GNSS band for days,

Figure 14. Overview on maximum in-band spectrum power levels observed at Oberpfaffenhofen during the IMS demonstration phase.

suddenly strong interferences might occur, which significantly impact the reference station receiver. This is illustrated in Fig. 14, which shows the maximum in-band spectrum power levels observed at Oberpfaffenhofen during the IMS demonstration phase. It was found that the differences in the maximum power levels per day can exceed 30 dB. In principle, similar observations were made at the other sites, too.

V. CONCLUSIONS

Interferences present a serious threat to GNSS reference station receivers. Monitoring the interference environment and assessing the current impact on the reference station receiver performances are mandatory steps to mitigate this threat. The interference monitoring system prototype described in this paper is specifically designed for this purpose, offering an extensive interference detection, characterisation, and impact prediction without any operator intervention required. Automatic notifications in case of critical events and the provision of reports containing all relevant information for each LE complete the capabilities of this system.

The IMS proved its capabilities in a six month demonstration phase, where several interference events were detected at the LE sites in Warsaw, Noordwijk and Oberpfaffenhofen. Where reference station receiver data was available, the IMS predictions regarding the impact of the observed interferences could be validated sucessfully. Furthermore, it turned out that the interference environment can vary significantly from day to day. Therefore, the current practice to perform a one day site survey to assess the suitability of potential Galileo and EGNOS sensor station sites is not sufficient. Much longer monitoring periods are required in order to gain situational awareness on the interference environment at a potential sensor station site, for example regarding the frequency of occurrence of sporadic but massive interference events. The IMS system is perfectly suited for this task, as the implemented means for interference statistics generation and RFI event characterisations provide in-

depth information on the interference environment at a level of detail, which is not available with current site survey means.Especially regarding such surveys for potential future reference station sites, a mobile version of an IMS LE would be an interesting option.

ACKNOWLEDGMENT

The authors whish to thank O. Nouvel, R. Swinden, A. Hedqvist, and G. Galluzzo for their support.

REFERENCES

[1] A. Brown, and R. Edwards, "Civil Applications of the GPS Jamming Detection and Location (JLOC) System" GPS Jamming & Interference –A Clear and Present Danger, National Physical Laboratory, Teddington. 23rd February 2010.

[2] C. Macabiau, O. Julien, and E. Chatre, “Use of Multicorrelator Techniques for Interference Detection”, ION NTM 2001, 22-24 January 2001, Long Beach, CA, pp. 353 – 363.

[3] F. Bastide, E. Chatre, C. Macabiau, “GPS Interference Detection and Identification Using Multi-correlator Receivers” ION GPS 2001, 11 -14 September 2001, Salt Lake City, UT, USA, pp. 872 – 881.

[4] H. Borowski, O. Isoz, F.M. Eklöf, S. Lo, and D. Akos, "Detecting false signals with automatic gain control", GPS World, pp. 38 – 43, April 2012.

[5] R.D. De Roo, S. Misra, and C.S. Ruf, “Sensitivity of the Kurtosis Statistic as a Detector of Pulsed Sinusoidal RFI”. IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No.7, July 2007.

[6] A. Tani, and R. Fantacci, "Performance Evaluation of a Precorrelation Interference Detection Algorithm for the GNSS based on Nonparametrical Spectral Estimation". IEEE Systems Journal, Vol.2, No.1, 2008.

[7] J.W. Betz, and K.R. Kolodziejski, "Extended Theory of Early-Late Code Tracking for a Bandlimited GPS Receiver" Navigation: Journal of the Institute of Navigation, Vol. 47, No. 3, Fall 2000.

[8] RTCA/DO-235A "Assessment of Radio Frequency Interference Relevant to the GNSS", October 15, 2002.

[9] RTCA/DO-292 Assessment of Radio Frequency Interference Relevantto the GNSS L5 / E5A Frequency Band, July 29, 2004.