Real-time GIOVE Signal Performance using STFT

Real-time GIOVE Signal Performance using STFT based excision method in the presence of Interference Ayse Sicramaz Ayaz (1), Roland Bauernfeind (1) , Thomas Pany (2) , Jae Gyu Jang (3) and Bernd Eissfeller (1) (1) Institute of Space Technology and Space Applications, University FAF Munich, Germany (2)Ifen GmbH, Germany (3)LG Electronics, South Korea

BIOGRAPHY Ayse Sicramaz Ayaz is a research associate in the Institute of Space Technology and Space Applications at the University of Federal Armed Forces Munich. She received the B.Sc degree in Electronics Engineering from DokuzEylül University, Turkey and M.Sc degree in Information Technology from University of Erlangen, Germany. Major interests are signal processing implementations, synchronization and interference mitigation algorithms in GNSS systems.

Dr. Thomas Pany works for IFEN GmbH as a senior research engineer in the GNSS receiver department. In particular, he is concerned with algorithm development and C/C++/assembler coding. He was for six years assistant professor (C1) at the university FAF Munich and for four years research associate at the Space Research Institute of the Austrian Academy of Science. His research interests include GNSS receivers, GNSS/INS integration, signal processing and GNSS science.

Roland Bauernfeind joined the Institute of Space Technology and Space Applications at the University of Federal Armed Forces Munich in 2008 after a one year employment at the German Space Operations Center where he was working as a Satellite Operations Engineer. He received a diploma in aerospace engineering from University of Stuttgart. His main research topic is mitigation of intentional interference in GNSS Receivers with focus at Intelligent Transportation Systems.

Prof. Bernd Eissfeller is Full Professor and ViceDirector of the Institute of Space Technology and Space Applications at the University FAF Munich. He is responsible for teaching and research in the field of navigation and signal processing. Till the end of 1993 he worked in industry as a project manager on the development of GPS/INS naviation systems. From 1994 2000 he was head of the GNSS Laboratory and since 2000 full professor of Navigation at University FAF. He is author of more than 215 scientific and technical papers.

Dr. Jaegyu Jang is a chief research engineer in Mobile Communication Lab., LG Electronics. He received B.S and M.S degree from Seoul National University in 1999 and 2001, respectively. And he got a Ph.D. degree of Aerospace Engineering at Seoul National University in 2006. Before he joins LG Electronics; he worked at Samsung Electronics as Senior Research Engineer and at the Institute of Geodesy and Navigation, University of FAF in Munich. He participated in real-time DGPS team from 1999 to 2000 and UAV team for user display system and simulator in 2000. His research interests were attitude determination algorithm and receiver development during Ph.D. In Munich, he involved in research about future navigation system design and interference mitigation technique as software receiver team. Now he works as GPS engineer in mobile industry.

ABSTRACT Various interference mitigation methods are required within the Global Navigation Satellite System (GNSS) receiver, especially for Safety of Life applications where integrity and accuracy of the position is significant. For this purpose, International Civil Aviation Organization [6] provides requirements stating that the estimated received signal sensitivity should be above the minimum required sensitivity depending on the desired signal characteristics and no loss of lock on carrier phase tracking occurs in the receiver. In this study, the capability of Short Time Fourier Transform (STFT) based mitigation algorithm that is used against both pulsed and narrowband interference is discussed and its real-time tracking performance with several precorrelation techniques is

compared. With real pulsed interference, 0.35 dB and with continuous wave interference of -110 dBW around 4 dB gain has been achieved.

2. SYSTEM MODEL The received interfered signal rm of the i-th satellite after

1. INTRODUCTION Direct-sequence spread spectrum (DS-SS) signals are fairly prone to have the interference or jammers effect as is the case for GNSS signals. A reliable transmission of desired signal is no more possible if the processing gain of the system is less than the interference signal power. There are two main approaches in order to increase the processing gain of the system; namely cross correlation protection and different interference mitigation approaches applied in the receiver. With new GNSS signals greater cross correlation protection with reduced interference level is provided, however since the noise level is much higher than the signal power level within around 20 dB, various interference mitigation techniques are required in the receiver. Against various types of interference signals, (i.e. continuous wave, chirp, pulsed or impulsive signals) a number of transform domain mitigation methods, blanking and antenna arrays using beamforming techniques are applied to the receivers. In these methods, the critical points are the complexity cost and the determination of the threshold. Among aforementioned methods, STFT based mitigation methods have widespread literature coverage in the DSCDMA field [8, 12, 13] due to its attractive localization properties. In this study, STFT method is combined with a blind threshold excision method, namely Forward Consecutive Mean Excision (FCME). FCME approach [1, 3, 4] is mainly used in the field of spectrum sensing in future cognitive radio transmissions to investigate if the frequency band is occupied or not. This method can also be applied to the Interference Cancellation (IC) module and can provide several benefits to the real-time GNSS Software Defined Radio (SDR), nominately no a priori information about the noise level is needed, the threshold value is set iteratively and preserving a desirable resolution gain it gives computational benefits in some degree. The FCME method itself can also be used as a concentrated signal detection [3] rather than interference suppession method. With the term concentrated signal, it is meant that narrow signal in any domain, i.e. short duration pulse signals in time-domain and narrow continuous wave signals in frequency-domain. Extensive knowledge about chirp jammer signal analysis and detection can be provided from [7, 10]. The current paper presents applicability of STFT-FCME method to the GNSS SDR in the presence of pulsed and narrowband interference and is organized as follows. In Section 2, interference signals recording setup is described. In Section 3, detection and excision methods are explained. Performance results of the methods are presented in Section 4 and finally in Section 5 conclusions are drawn.

rm

Front-end can be modelled as =

Ai c (tm f c ,i

− τ i )d (tm

− τ i ) cos(2π ( f IF +

f c ,i

f D ,i )tm − φi )

+ Aint,i cos(2π f int tm + φint,i ) + nIF ,m + I IF ,m (1) where

m is the sample index, d is the navigation data

message, { Ai ,τ i ,φi } stands for the amplitude, code delay in chips and carrier phase of the i-th satellite signal and

{ Aint,i ,φint,i} stands for the amplitude and carrier phase of

the interference signal. rate in chips/s,

c is the PRN code, fc,i is the code

f IF is the IF frequency,

f is the int

interference frequency, f D ,i is the Doppler frequency, tm is the time of sample in [s],

nIF , m is the real valued white

Gaussian noise and I IF ,m is the single component sinusoidal. The tracking sensitivity is computed from the carrier to noise ratio derivations which exceed the established threshold above the noise floor (based on precorrelation noise density in ipexSR) that can be expressed as C = N 0, pre

(2)

C N0 +

Cl BFE

BFE /2

∫

Gl ( f )df

− BFE /2

where BFE is the Front-end bandwidth and Cl Gl is the interference power spectral density. Hereby, the Front-end bandwidth directly affects the interference power. In GNSS, one of the main interference sources in the E5 band is the Distance Measurement Equipment (DME) that is utilized for aviation purposes. The DME signal may give degradation to GNSS signal power to around several dBs even if blanking is applied [14]. In order to analyse the effect of real DME signal, a field measurement campaign in Munich Airport was performed using a NovAtel GPS-704-x Passive Antenna, 16 dB Low Noise Amplifier (LNA), the ipexSR GNSS software receiver [5] and the triple-frequency USB Front-end. In Figure 1, the DME signal recording setup is represented [2]. 6 4 2 0 -2 -4 -6 -8 6.85

6.9

6.95

7

7.05

7.1 4

x10

Fig. 1. Real DME signal recording set up

The CWI signal is recorded in a laboratory environment with a setup depicted in Figure 2. The superposition of CWI interference and Giove E5a signal is generated in a Matlab environment in baseband. The CWI signal is generated at 1176 MHz with -110 dBW power level. The phase φint,i is assumed to be zero and Aint,i is set to unity. E5a BPSK(10) is generated with a sampling rate of 40.96 MHz. The received signal power is calibrated at -158 dBW. The interfered baseband signal is generated, stored and replayed from the signal recorder in the analogue domain. Afterwards it is mixed with the local oscillator for up-converting it to the RF frequency of 1176.45 MHz and then the signal power is attenuated using a 31 dB attenuator and finally the signal is fed into the ipexSR Front-end.

Power Spectral Density -35 -40

[11], Daubechies wavelet is not a perfect match to DME signal characteristic; however it is the optimal wavelet among various functions (Symlet, Coiflet). With wavelet transform method, interfering pulses are detected with high precision on the other hand it causes a substantial computational load at the receiver. In conventional Notch filtering, the filter blocks all frequencies within the stop band while passing all outside of this band. In this module, multiple Notch filtering with pole-zero placement on the z-domain is used. The IF sample spectral peaks are estimated using FFT and considering the estimated interference power, Notch filter transfer function is formed and then the filter is applied. 3.1. FORWARD CONSECUTIVE MEAN EXCISION

FCME is the interference suppression method that is related to outlier detection. In outlier detection, the interference samples are away from the mean of the sample set and the offset to the mean is calculated by Mahalanobis squared distance.

Power/frequency (dB/Hz)

-45 -50 -55

γ = (r − )T

-60 -65 -70 -75 -80 2

4

6

8 10 12 Frequency (MHz)

14

16

18

Fig. 2. Generated CWI signal recording set up 3. DETECTION AND EXCISION METHODS In general, the detection problem is to decide whether the GNSS signal is embedded in the background noise or if only noise is present via Hypothesis testing. The noise is assumed to be zero mean white Gaussian process and based on the threshold set, the signal detection is decided. In this study, the applicability of STFT-FCME method is searched for interference mitigation in real-time GNSS receivers. For comparison purposes, against pulsed interference; STFT-FCME, blanking, Wavelet transform based blanking and Time-FCME methods are applied in the IC module. On the other side, Notch filtering and STFT-FCME are applied for CWI suppression. In pulse blanking, samples of interference signal are set to zero. The threshold is applied considering the average power of IF signal without DME.

In Time-FCME, FCME is applied to the magnitude of the whole segment of IF samples. The segment length is 65536 samples. In principle, in wavelet transform, the signal is decomposed into its building components by using wavelets. Wavelets are acquired through dilated and shifted version of the mother wavelet. In wavelet transform based blanking method, IF samples are transformed into wavelet-domain and processed considering a certain wavelet mother function and approximate pulse duration. Then excision is applied to the processed output and the inverse transform is applied. In the wavelet module, a best match to the DME pattern, namely Daubechies Wavelet of order 4 is used. Based on

−1

(r − )

(3)

where r is the sample vector, C is the covariance matrix and is the mean of sample vector of r . The sample vector can be classified as an outlier if Mahalanobis squared distance is larger than a predetermined threshold value [3]. In principle, the FCME algorithm is based on Constant False Alarm Rate (CFAR) with constant probability of false alarm. It operates forward and calculates the mean from an initial ascended sorted set of samples. In the sorting process, heapsort is used mainly due to the efficient best and worst case running times. FCME can operate in any transform domain and it is applied in time-domain to short duration interference like impulses addressed in [1, 3] and CW interfered signal in frequency-domain. The initial threshold value is calculated w.r.t. the apriori information based on the pure desired signal characteristics. Due to the iterative calculation of the threshold, the performance of FCME is superior to Notch filters. The FCME algorithm flow diagram can be written as follows [3]: 1. With a predefined Pfa , calculate the FCME threshold considering Gaussian distributed noise.

TFCME = where

4 − ln( Pfa ) π

(4)

Pfa is the desired false alarm probability which

indicates how many samples are above the final threshold in the noise only case. 2. Rearrange the samples r in the ascending order upon their energies for the first iteration (m=0) as

y (0) = ( y0 , y1 ,..., y N )

(5)

3. Choose the M (set size) smallest samples to form the interference-free set and calculate the initial threshold M

Td = TFCME ∑ yi

(6)

i =1

The size of M is typically about 10 % of the size of the data set. 4. Update y ( m) iteratively for the samples that are below the threshold Td

y (m) =

1 M

M

∑y

i

The principle of STFT-FCME is illustrated in Figure 3. In the algorithm, besides FFT operation the most complex parts are the sorting and searching the outlier sample in the sample set. The sorting is accomplished using Heapsort function. In Figure 4, the time-domain signal is represented and in the below figure, spectogram of the DME interfered signal is illustrated. Red bands indicate the high energy frequency contents of the signal emphasizing the DME signal, since STFT concentrates the interference in a small area in the time-frequency domain.

i =1

(7) 5. Update the threshold Td until the maximum predefined number of iterations, and then go to Step 7. 6. The ultimate threshold value will be

8 6 4

Td = TFCME ym

(8) Amplitude

2

m=m+1 go to Step 3 7. Reset the interfered indexes exceeding

Td

0 -2 -4

3.2. SINGLE THRESHOLDED STFT- FCME METHOD

2 P(t , w) = Rt ( w) = 1

+∞

2

e 2π ∫

− jwτ

-8

0

r (τ )h(τ − t )dτ

0.2

7

x 10

0.4

0.6

0.8 1 1.2 Number of samples

1.4

1.6

(9)

SPECTROGRAM, R = 2048, overlapping

1.8

3

1.6

2.5

1.4

2

1.2

1.5

1

1

0.8 0.5 0.6 0 0.4 -0.5

0.2

-1 0

1

2

3

4 -3

x 10

Fig. 4. Magnitude and Spectogram of DME plus Giove signal of PRN1

6 4 2

r ( n)

0 -2

r%k (e j 2π k ( f s / N ) )

⊗

r% ' (e j 2π k ( f s / N ) )

h(n)

-4 -6 -8

6.85

6.9

6.95

7

7.05

7.1 4

x10

r% k' ≥ Td Td

Fig. 3. STFT-FCME flow diagram

2 5

x 10

time

h(τ ) is the window function.

1.8

2

0

−∞

where

-6

frequency

STFT is a time-frequency representation of the signal energy; it splits the non-stationary signal into small segments that are then assumed as stationary. This is achieved by multiplying the signal with a window function and then the FFT operation is applied to the windowed signal. The excision is realized in frequencydomain by applying FCME. Then the signal is transformed into the time-domain by applying IFFT to enable feeding the acquisition process. The time-frequency distribution P(t , w) , namely spectogram of the signal is achieved by an ensemble of spectra for different spectrum in different time sections. The energy density spectrum at time τ can be written as

TFCME

In the next figure, the PSD of the CW interfered Giove E5a is represented and the CW interferer is identified with spikes. In the spectogram, every spike is represented with the red band indicating the CW interferer.

Loop (DLL/FLL/PLL) bandwidths 0.5/10/20 Hz

Coherent integration time 20 ms

Sampling rate

Front-end bandwidth

40.96 MHz

20 MHz

Power Spectral Density

Table 1. Multicorrelator Tracking process settings -35

Figure 6 shows the tracking sensitivity of DME interfered real Giove E5a signal. Against pulsed interference, it is shown that all algorithms present similar interference suppression gain w.r.t. without mitigation. The achieved gain is between around 0.2 and 0.35 dB. However, the gain provided via STFT method is slightly higher w.r.t. other methods.

-40

Power/frequency (dB/Hz)

-45 -50 -55 -60 -65 -70

42

-75 -80

41.8

7

x 10

4

6

8 10 12 Frequency (MHz)

14

16

18 41.6

SPECTROGRAM, R = 2048, overlapping

2 2.5 1.8 2

frequency

1.6 1.4

1.5

1.2

1

C N 0 [ d B -H z ]

2

41.4

41.2

41 S T F T -F C M E

40.8

T im e -F C M E P u ls e b la n k in g W a ve le t t ra n s fo rm b a s e d b la n k in g

40.6

N o m it ig a t io n

1

0.5 40.4

0.8

4

6

8

10

0

12 T im e [ s ]

14

16

18

0.6 -0.5

0.4

-1

0.2 0

0

1

2

3 time

Fig. 6. Tracking sensitivity comparison of mitigation methods for detecting DME interfered Giove E5a signal

4 58

-3

x 10

S TFT-FCM E No m itigation Notc h

56

Fig. 5. PSD and Spectogram of CWI (-110 dBW) plus Giove signal of PRN1

interference plus desired signal plus noise is suppressed. This also increases the computational load in the receiver. Due to this reason, a low Pfa has been selected.

52 CN0[dB -Hz ]

4. PERFORMANCE RESULTS The performance of mitigation algorithms are analyzed using estimated received signal power levels in tracking process based on multicorrelator technique and settings are tabulated in Table 1. The structure to process interfered Giove signals is conceptually shown in Figure 3. In STFT, overlapping segments (50% overlap) with FFT size of N=64 and N=2048 are used (DME and CWI respectively) to reduce the windowing loss to around 1 dB and Heapsort is used to rearrange the FFT samples in an ascending order. The initial clean set sizes are L=8 and L=256 samples. The initial threshold was calculated with the FCME algorithm with the target probability of false alarm of Pfa = 1E − 05 . The higher the Pfa is, the more

54

50 48 46 44 42 40 8

10

12

14

16 Tim e[s ]

18

20

22

24

Fig. 7. Tracking sensitivity comparison of mitigation methods for detecting CW interfered Giove E5a signal In Figure 7, the interference mitigation capability of STFT-FCME is compared with conventional Notch filter. The performed measurements indicated that the gain achieved with STFT-FCME w.r.t. Notch filtering is roughly 4 dB. This gain could be due to iterative calculation of threshold. The real-time aspect of interference mitigation modules is indicated in Table 2. On a PC with Intel Core 2 Quad

CPU 2.66 GHz processor with four cores, the normalized computational time for a Notch filter processing of one data packet corresponds to 1.0078 s. From normalized time results, as expected Wavelet transform based blanking is the most computationally complex method in terms of processing time. With STFT-FCME, less complexity is achieved; however it is already high for a real-time process. Due to high gain achieved via STFTFCME, in the module implementation optimizations could be applied to diminish the time spent. Algorithm Pulse blanking Wavelet trans. based blanking Time-FCME STFT-FCME Notch filtering

Normalized time 2.18 32.72 4.911 15.511 1

Table 2. Tracking process settings

5. CONCLUSION It has been discussed the applicability of STFT-FCME method for detection and excision of pulsed and CW interference signals in the real-time GNSS SDR. The gain achieved from STFT-FCME detector compared to conventional Notch filtering is roughly 4 dB. The improvement on the gain could be due to the iterative calculation of threshold in the FCME, hence useless suppression of desired signal has been prevented. However, the main drawback of STFT method is that it gives a considerable amount of computational load in a real-time process and it still needs to be optimized. Notch filtering and pulse blanking are the most computationally attractive methods nevertheless they require knowledge of the noise level. Finally, according to the results obtained, against pulse interferenced Giove signal, STFT-FCME outperformed Wavelet transform based method and against CW interfered signal conventional Notch filtering is outperformed. Hence, STFT method could be used as a common suppression module against both interference types provided that an optimization on the implementation be performed. ACKNOWLEDGMENTS

The authors wish to acknowledge gratefully the financial support by EC where the presented article was performed in the EC funded ART-X Project. We also would like to acknowledge ESA, since Wavelet transform and Notch filtering parts were prepared under IISILP project. Thanks also to Thomas Kraus for the assistance of the CWI signal recording setup.

REFERENCES [1] P. Henttu, S. Aromaa, “Consecutive Mean Excision Algorithm”, 2002, IEEE Int.Symp. Spread Spectrum Techniques and Applications, Chech Republic, 2002 [2] A. Sicramaz Ayaz et al, “Performance Evaluation of Single Antenna Interference Suppression Techniques on Galileo Signals using Real-time GNSS Software Receiver”, ION GNSS 2010, Portland, OR, USA [3] J. Vartiainen, J. Lehtomäki, H. Saarnisaari, M. Juntti,”Analysis of the Consecutive Mean Excision Algorithms”, Journal of Electrical and Computer Engineering, Vol 2010, Article ID459623 [4] J. Vartiainen, J. Lehtomäki, H. Saarnisaari, M. Juntti, “Limits of Detection for the Consecutive Mean Excision Algorithms”, in Proceedings of the International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM ’10), Cannes, France, June 2010 [5] C. Stöber et al, “ipexSR: A Real-Time MultiFrequency Software GNSS Receiver”, IEEE ELMAR, Zadar, Croatia, 2010 [6] “Interference susceptibilities of Systems Operating in the 960-1215 Mhz Band Application to the Compatibility Analysis of the Future Communication System”, ICAO WRC’07 A.I. 1.6, Malmö, 2005 [7] R. Bauernfeind, A. Sicramaz Ayaz, B. Eissfeller, “GNSS Interference Monitoring Network Based on Detection in Automative ITS Station Receivers”, ION GNSS 2011, Portland, OR, USA [8] D. Borio, L. Camoriano, S. Savasta, L. Lo Presti, “Time-Frequency Excision for GNSS Applications”, IEEE Systems Journal, Vol. 2, No. 1, 2008 [9] T. Pany, B. Eisfeller “Code and Phase Tracking of generic PRN Signals with Sub-Nyquist Sample Rates”, NAVIGATION, Journal of the Institute of Navigation, Vol. 51, No.2, pp. 143-159 [10] T. Kraus, R. Bauernfeind, B. Eissfeller, „Survey of In-Vehicle Jammers-Analysis and Modelling of the RF Signals and IF Samples“, ION GNSS 2011, Portland, OR, USA [11] Anyaegbu E., Cooper J., Boussakta S., “Wavelet based Interference Detection and Suppression Algorithm for Next Generation GNSS Signals”, The European Navigation Conference (GNSS). 2005, Munich [12] Ouyang X., Amin G., “Short-Time Fourier Transform Receiver for Nonstationary Interference Excision in Direct Sequence Spread Spectrum Communications”, IEEE Transactions on Signal Processing, Vol. 49, No.4, 2001 [13] Borio D., Lo Presti L., “A Reconfigurable GNSS Acquisition Scheme for Time-Frequency Applications”, EURASIP Journal on Advances of Signal Processing, Vol. 2008, Article ID: 356267 [14] Bastide F., “Analysis of the feasibility and interests of Galileo E5a/E5b and GPS L5 signals for use with civil aviation”, PhD Thesis, 2004