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Interference Suppression for High Precision Navigation Using Vector-Based GNSS Software Receivers Tao Lin, Mohammad Abdizadeh, Ali Broumandan, Da Wang, Kyle O’Keefe, and Gérard Lachapelle Position Location And Navigation Group Department of Geomatics Engineering Schulich School of Engineering University of Calgary

BIOGRAPHY Tao Lin is a Ph.D. candidate in the PLAN Group of the Department of Geomatics Engineering. He received his BSc from the same department in May 2008. His research interests include the fields of GNSS software receiver design, satellite-based navigation, inertial navigation and ground-based wireless location. Mohammad Abdizadeh received his M.Sc. degree in microelectronics engineering from Sharif University of Technology (SUT), Iran. Since 2010, he is pursuing his Ph.D. degree in the PLAN Group. His current research focuses on the interference problems in GPS systems, interference mitigation schemes based on adaptive algorithms, and GPS signal acquisition and tracking in interference channels. Dr. Ali Broumandan holds a MSc degree from the Department of Electrical and Computer Engineering, University of Tehran. He obtained a PhD in Geomatics Engineering at the University of Calgary in 2009 where he is a Senior Research Associate/Post-Doctoral Fellow in the PLAN Group. His current research focuses on GNSS signal processing, array processing, detection, and estimation theory. Da Wang is pursuing his PhD degree in the PLAN group after completing a MSc degree in September 2010 in the same group. He obtained his B.Eng. degree in the Department of Communication Engineering, Jilin University, China in 2007. His research focuses on sensor integration and carrier phase ambiguity resolution for adhoc vehicular network positioning and navigation. Dr. Kyle O’Keefe is an Associate Professor of Geomatics Engineering at the University of Calgary. He has worked in positioning and navigation research since 1996. His major research interests are GNSS system simulation and assessment, space applications of GNSS, carrier phase positioning, and local and indoor positioning with ground based ranging systems.

ION GNSS 2011, Session B1, Portland, OR, 20-23 September 2011

Dr. Gérard Lachapelle holds a Canada Research Chair in Wireless Location in the Department of Geomatics Engineering where he has been a professor since 1988. He was involved in the GNSS industry from 1980 to 1988. He has been involved in a multitude of GNSS R&D projects since 1980, ranging from RTK positioning to indoor location and GNSS signal processing enhancements.

ABSTRACT Vector-based tracking has gained much attention due to its superior code phase and carrier Doppler tracking performance compared to scalar tracking architecture under Radio Frequency (RF) interference. However, the robustness of vector-based tracking still cannot satisfy the requirements for many applications under strong RF interference conditions. Moreover, because the pseudorange and pseudorange-rate based navigation solution feedback in vector-based tracking only controls the code phase and carrier Doppler tracking, individual carrier phase tracking is still required. Therefore, it is desirable to improve the carrier phase tracking performance in vector-based receivers under strong interference. In this paper, two multi-pole complex adaptive Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) notch filtering techniques have been implemented in a GNSS vector-based software receiver to mitigate Continuous Wave Interference (CWI) and swept interference. The main focus of this paper is the implementation and the performance comparison of these two notch filters in a GNSS vector-based software receiver by processing simulated GNSS signals with CWI and swept interference generated from a hardware simulator.

INTRODUCTION The problem of RF interference mitigation in GNSSbased navigation systems has recently been receiving greater attention, as the potential use of jamming and other unintentional interference sources have become

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more recognized. The expected interference propagation adversely affects GNSS receiver performance for most applications. Interference sources can be categorized into two types: intentional sources, e.g. jammers, and unintentional sources such as TV/FM/Radar emitters and Distance Measuring Equipment (DME)/ Tactical Air Navigation (TACAN) transceivers. Strong interference at the input of the GNSS receivers may affect performance, continuity and integrity of receiver operation in different applications such as vehicle and pedestrian navigation, aviation and high precision surveying. Interference signals can be mainly categorized into four classes based on the statistical, spectral, and temporal characteristics as 1- Narrow-band Gaussian interference, 2- Continuous Wave Interference (CWI), 3- Pulsed interference, and 4Swept interference (e.g. Borio 2008). Some other classifications have been done based on the utilized bandwidth and the frequency components of the interferer signals. Among them, CWI and swept are the two most common types of interference signals in the GNSS band for many applications. Hence, this paper focuses on CWI and swept interference detection and mitigation techniques.

band. In comparison to notch filtering, this technique is more complex and the resolution of the required DFT algorithm is directly proportional to the desired frequency estimation precision. Some works focuses on using Kalman and Weiner based filtering (Borio 2008). Other current approaches include interference mitigation employing transform domain filtering, e.g. the Short Time Fourier Transform (STFT) domain, the Wavelet Transform (WT) domain, and subspace processing, as well as methods based on adaptive array antennas. However, these approaches require large dimensionality of space time weights and are computationally complex due to the space-time processing issues (Songtao & Sun 2010). Compared to other interference mitigation techniques, notch filtering is the most low-cost, computationally efficient and appropriate for in-band and out of band CW interference. Hence, it is suitable to be implemented in a GNSS software receiver. In this paper, two multi-pole complex adaptive Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) notch filtering techniques are developed and implemented in a GNSS vector-based software receiver to mitigate CWI and swept interference.

In the literature, numerous solutions have been proposed to alleviate the RF interference challenges. These solutions can be categorized into two groups: the first group mitigates interference via advanced interference mitigation algorithms, while the second group utilizes some advanced GNSS receiver architectures. Vectorbased tracking and its extension ultra-tight integration have attracted attention because of their sensitivity improvement in weak signal tracking and higher immunity to interference compared to conventional scalar-based tracking. Benson (2007) and Groves et al (2007) reported the benefits of a vector delay lock loop (VDLL) and a non-coherent ultra-tight integration in a high jamming environment. However, the anti-jamming improvement by utilizing vector-based and ultra-tight architectures still cannot satisfy the requirements for many applications under strong RF interference. Vectorbased tracking and ultra-tight integration requires ephemeris information, which cannot be obtained unless signals are successfully acquired, tracked, and decoded. In practice, RF interference likely occurs before vector based receivers are able to transform from scalar-based tracking to vector-based tracking. Moreover, because the carrier phase tracking loops in the vector-based receivers are closed in each channel individually, interference mitigation techniques are required for carrier phase tracking in a vector-based receiver.

This paper starts with a review of a cascaded vector-based tracking architecture for high precision applications. The theoretical concepts and the implementation descriptions of the two proposed multi-pole adaptive notch filters are then presented. Finally, the field test results and the performance evaluation of the proposed receiver are presented.

Several interference mitigation techniques including the time domain and frequency domain filtering have been proposed and investigated to combat the problems caused by narrowband interference on GPS. The most employed interference mitigation scheme is based on notch filters (Borio 2008). Capozza et al (2000) employed Discrete Fourier Transform (DFT) algorithm to detect and remove any abnormal spectra line from the received signal’s


HIGH PRECISION VECTOR-BASED RECEIVER In general, GNSS receiver architectures can be categorized into the scalar-based architecture and the vector-based architecture. The major difference between a vector-based receiver and a scalar-based receiver is the navigation solution feedback to each tracking channel. This enables the inter-channel aiding among the channels so that the strong signals can help track the weak signals. Since the accuracy of the pseudorange and pseudorange rate based navigation solution is only sufficient to close the Vector DLL (VDLL) and Vector FLL (VFLL), the carrier phase tracking loop in each channel is still required to be closed individually if carrier phase tracking is required. Petovello et al (2006) proposed a cascaded vector-based tracking loop. In such an architecture, carrier phase tracking is maintained by a Kalman filter in each channel individually. This architecture is used in this paper for carrier phase tracking and carrier phase measurement generation.

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MULTI-POLE COMPLEX ADAPTIVE NOTCH FILTERING Although many complex methods (e.g. STFT and Kalman filtering) have been proposed for CWI and swept interference mitigation, notch filtering is still the most efficient and practical solution for CWI and swept interference mitigation. This is especially true for the implementation in a GNSS software receiver (Lung et al 2011). In this section, the theoretical concepts, implementation and measurement impact of the proposed adaptive FIR and IIR notch filters are presented. Complex IIR Notch Filter

Figure 2: Phase of complex IIR notch filters

IIR is the most widely employed class of notch filter structures because of its low computational complexity and efficient implementation. A complex IIR notch filter can be characterized by a transfer function as (Borio 2008)

H IIR_NF ( z ) =

1 − z 0 z −1 1 − ka z0 z −1

(1)

where z0 is the notch filer zero corresponding to the interference frequency and ka is the pole contraction factor.

Linear Phase Complex FIR Notch Filter

Although the computation load of linear phase FIR notch filters is higher than that of the IIR ones, FIR filters can be easily designed to have a linear phase frequency response. This will allow a simple way on compensating the measurement biases induced by FIR filters. The transfer function of a linear phase FIR complex notch filter can be derived by utilizing a series of expansion of the IIR complex notch filter and is shown as follows (Montloin 2010): N −1

Figure 1 and Figure 2 show the amplitude and phase of the frequency response of a complex IIR notch filter with two different pole contraction factor values respectively. As shown in Figure 2, IIR filters have a non-linear phase characteristics. This non-linear phase characteristics will induce biases in pseudorange and carrier phase measurements, which are difficult to fully compensate for. More details on the impact of adaptive notch filters on navigation measurements are provided in the next subsection.

H FIR_NF ( z ) = 1 + ∑ z0n  kan − kan −1  z − n − kaN −1 z0N z − N n =1

(2)

hFIR_LP_NF [ n ] = hFIR_NF [ n ] ∗ hFIR_NF [ N − n ] where z0 is the notch filer zero corresponding to the interference frequency, ka is the pole contraction factor and N is the order of series expansion of the IIR transfer function. Figure 3 and Figure 4 show the amplitude and phase of the frequency response of a complex linear phase FIR notch filter respectively. The phase of a linear phase FIR notch filter is linear and independent from the pole contract factor used in a notch filter.

Figure 1: Amplitude response of complex IIR notch filters


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navigation solution. In addition to these delays, the amplitude distortion induced by an IIR notch filter will make the correlation asymmetric with respect to the correlation peak and introduce another bias term in pseudorange measurements. More theoretical and experimental analysis of the biases in pseudorange measurements due to an IIR notch filter can be found in Montloin (2010). The group and phase delays of a linear phase FIR notch filter can be evaluated by the definitions introduced above. They are given as follows: Figure 3: Amplitude response of the complex linear phase FIR notch filter

N 2 fs

τ group _ LP _ FIR _ NF ( f ) =

τ phase _ LP _ FIR _ NF ( f ) = −

(5)

N 2 fs

(6)

where N is the filter order and f s is the sampling frequency.

Figure 4: Phase response of the complex linear phase FIR notch filter Measurement Biases Induced by Notch Filters

The group and phase delays represent the delays in pseudorange and carrier phase measurements due to the phase of a filter. The group and phase delays are defined mathematically as follows:

τ group ( f ) =

−1 dψ ( f ) 2π df

(3)

τ phase ( f ) =

1 ψ(f) 2π f

(4)

where τ group ( f ) is the group delay in unit of seconds,

τ phase ( f ) is the phase delay in unit of seconds, ψ ( f ) is the phase of the frequency response of a filter and f is the frequency in Hz. Since an IIR notch filter is highly non-linear, the group and phase delays induced by an IIR notch filter are signal frequency dependent. Therefore the biases in pseudorange and carrier phase measurements due to the group and the phase delays of an IIR notch filter cannot be easily predicted and compensated by the clock bias state in the


Because of the linear phase property of a linear phase FIR notch filter, the group and phase delays are signal frequency independent. In addition, it has been proven that linear phase FIR notch filters will ensure that the correlation function remains symmetric (Montloin 2010). Therefore the bias terms in pseudorange and carrier phase measurements due to a linear phase FIR notch filter are common for all satellite signals with different Doppler frequencies. They can be fully absorbed by the clock bias term in the navigation solution. Given the benefits of the linear phase property of a linear phase FIR notch filter, it is theoretically expected that a linear phase FIR notch filter will outperform an IIR notch filter in terms of the quality of the navigation measurements. Adaptive Mechanism

The core of the proposed adaptive notch filter is represented by the adaptive block that tracks the CWI and swept interference frequency, and then adjusts the filter coefficients to minimize the desired cost function. In this paper, the normalized Least Mean Squares (LMS) criterion descried by Borio (2008) is employed. The cost function is defined as

{

J [ n ] = E xout [ n ]

z0 [ n + 1] = z0 [ n ] −

δ

{

E xin [ n ]

2

}

2

}

(7)

{

∇ z0 xout [ n ]

2

}

(8)

where xin [ n ] and xout [ n ] is the notch filer input and output respectively, z0 [ n ] is the complex parameter that

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represent the pole of the estimated CW frequency and magnitude, and δ is the normalized LMS step size. Detection Mechanism

In practice, not only is the CW frequency is unknown, the presence of interference is not known either. Fortunately the magnitude of the zero of the proposed adaptive notch filter can be used to determine the presence and the absence of interference. In Figure 5, three RF CWIs with different powers modulated by an on-off scheme were generated and passed-through an adaptive notch filter. The magnitude of the zero clearly indicates the presence and absence of interference signals. However, it should be noted that the threshold used for detection depends on the notch filter parameters. A look-up table is required to store the corresponding detection thresholds for various notch filter parameter combinations. The development of an optimum interference detection scheme remains as future work.

Figure 6: Cascaded Multi-Pole Notch Filtering

PERFORMANCE EVALUATION PROPOSED ARCHITECTURE

OF

THE

The theoretical background of the proposed multi-pole adaptive IIR and FIR notch filters have been presented in the previous sections. These two filters are implemented in the high precision vector-based GNSS software navigation receiver (GSNRx-vbTM) developed at the University of Calgary (Petovello et al 2008). The architecture of the modified GNSS software navigation receiver (GSNRx-vb-intTM) is shown in Figure 7. Figure 5: Detection mechanism Multi-Pole Notch Filtering

The adaptive notch filters described in the previous section can only mitigate one CW at a time. In practice, the number of CWI or swept interference is unknown. A cascaded schemed proposed by Borio (2008) is used in this paper to mitigate multiple interference signals. The basic idea behind this is to mitigate multiple interference signals one-after-one until no more interference is detected. Although this scheme is not optimal in the sense that the minimization of output signal power is not globally achieved, it is simple and practical. The processing flow of the cascaded scheme is shown in Figure 6.


Figure 7: GSNRx-vb-intTM architecture

As shown in Figure 7, the proposed architecture is a modified cascaded vector-based architecture. A multipole notch filter (in red) has been added the prior to the Doppler Removal and Correlation (DRC), since this process is common for all satellite signals. After notch filtering, the DRC outputs are fed to a Kalman filter channel filter to estimate the code phase error, the carrier Doppler error and the carrier phase error. The carrier

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phase tracking loop in each channel is closed individually while the code phase and carrier Doppler tracking are controlled via the feedback of the navigation solution. Data Collection Setup

To evaluate the performance of the proposed software receiver, a few simulation scenarios were carried out using a Spirent GSS 7700 simulator controlled by the SimGEN software. In these simulations, the user antenna was static. The RF signals from the Spirent hardware simulator were passed through a low noise amplifier (LNA) to a National Instrument RF front-end, which outputs raw IF samples at 5 MHz with 16 bit quantization. The Spirent simulator was used to generate different RF interference signals at different power levels and frequencies under controlled environments. The noise floor power level simulated from the Spirent simulator was -130 dBm.

Figure 8: C/N0 of PRN 1 - Data set 1

Assessment with Single CWI/Swept Interference Source

In this section, the performance comparison of the proposed vector-based tracking loop with adaptive FIR/IIR notch filters (GSNRx-vb-intTM), conventional vector-based tracking loops, and scalar tracking loops without interference mitigation technique is presented. The interference parameters in the simulation scenarios are summarized in Table 1. Table 1: Simulated Single CWI/Swept Interference Parameters Data Interference Frequency Power set Type (MHz) (dBm) #1 CWI 1575.42 [ -120 : -70] #2 Swept [1574.92: 1575.92] -80

Figure 9: FLI of PRN 1 - Data set 1

In both data sets, the CWI or swept interference was turned on after one minute. The first minute of the interference free signals allows the traditional vectorbased and scalar-based receivers to successfully acquire and track the GPS signals, achieve bit synchronization and navigation decoding, and obtain a navigation solution. The main objective of this test was to examine benefits of the proposed adaptive notch filters in vectortracking architecture. The Carrier-to-Noise density ratio (C/N0), FrequencyLock-Indicator (FLI), Phase-Lock-Indicator (PLI), and Doppler estimation with scalar-based tracking, vectorbased tracking, vector-based tracking with adaptive IIR notch filtering, and vector-based tracking with adaptive FIR notch filtering for PRN 1, Data set #1 are shown in Figure 8, Figure 9, Figure 10 and Figure 11, respectively.


Figure 10: PLI of PRN 1 - Data set 1

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interference mitigation technique (shown in Figure 11) under a strong CWI in this case.

Figure 11: Doppler of PRN 1 - Data set 1

It can be observed that the proposed vector-based tracking loop with an adaptive FIR notch filter or an adaptive IIR notch filter can maintain carrier phase tracking during the entire test period. When the CWI power is around -70 dBm, the estimated C/N0 values from the tracking loop with an adaptive FIR notch filter are higher than those from the tracking loop with an adaptive IIR notch filter. Even if no notch filtering is performed, vector-based tracking still outperforms scalar-based tracking because of the navigation solution feedback. The FLI and PLI values from the tracking loops without notch filtering dropped as the CWI power increased. The decreasing trend of the estimated C/N0 values from the vector-based tracking loop matches the increasing trend of the CWI power. This is the expected impact of CWI on C/N0, FLI, and PLI. Interestingly the vector-based tracking loop can still provide reasonable Doppler estimates while the scalar tracking loop cannot if no notch filtering is performed. The position errors from the vector-based tracking, the vector-based tracking with the IIR notch filtering, and the vector-based tracking with the FIR notch filtering in the presence of CWI are plotted in Figure 12, Figure 13 and Figure 14, respectively. The results from the scalar-based tracking are not shown here because of poor performance. It can be observed that the solution from the vector-based tracking loop with an adaptive FIR notch filter is more accurate than the other two. Large position fluctuations were observed in the IIR case when the CWI power was 70 dBm. Interestingly the position errors from the vectorbased only solution were not too large compared to others in general. This is because the antenna was static in the simulation, and the simulated interference was increased gradually. When the navigation measurements reached the point that they were too poor to use due to the strong power of CWI, the accuracy of the estimated velocity was already very high. Therefore, the prediction in the navigation Kalman filter was relatively accurate and still provided solutions with reasonable accuracy. Of course the good navigation solution will significantly improves the robustness of the code phase and carrier frequency tracking in a vector-based receiver. Essentially this explains why the vector-based carrier phase tracking performance was still decent even without applying any


Figure 12: Easting error - Data set 1

Figure 13: Northing error - Data set 1

Figure 14: Height error - Data set 1

In Data set #2, a swept interference signal with a power of -80 dBm (50 dB above the noise floor) was simulated. The estimated swept frequency from the adaptive notch filter compared to the true swept frequency is shown in Figure 15. The precise estimation of the swept frequency demonstrates the successful tracking and mitigation of swept interference.

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Figure 15: Estimated swept frequency - Data set 2

The position and velocity errors in the presence of a swept interference are shown in Figure 16 - Figure 21. Again, larger position errors from the IIR case are observed compared to the linear phase FIR case. As mentioned previously the group delay induced by the adaptive IIR notch filter is Doppler dependent and notch frequency dependent. Thus, the pseudorange bias induced by an adaptive IIR notch filter in this case is different from different PRNs and changes over time as the swept frequency changes. Therefore, the pseudorange bias cannot be fully compensated by the clock bias term in the navigation solution. Contrary to adaptive IIR notch filters, the linear phase property of linear phase adaptive FIR notch filters implemented in this paper ensures that the filter induced bias is common for all PRNs. Thus, it can be removed by the clock bias term in the navigation solution. The horizontal velocity errors from the adaptive IIR and adaptive linear phase FIR notch filters are similar. However, the vertical velocity errors from adaptive linear phase FIR notch filters are smaller compared to those from adaptive IIR notch filters. This is due to the nonlinear phase of the frequency response of adaptive IIR filters.



Figure 19: East velocity error - Data set 2



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reference station. A standard version of GSNRxTM was used to process the IF samples from the reference station. The estimated CWI and swept frequencies for Data set #3 are shown in Figure 22. The estimated frequencies match the frequencies of the simulated CWI and Swept. This demonstrates the successful mitigation of both CWI and swept interference by the cascaded multi-pole notch filtering scheme.

Figure 20: North velocity error - Data set 2

Figure 22: Estimated CWI and swept frequencies Data set 3 Figure 21: Vertical velocity error - Data set 2

Assessment with Multiple CWI/Swept Interference Sources

In this section, the performance of the proposed vectorbased tracking loop with adaptive IIR and linear phase FIR notch filters implemented in GSNRx-vb-intTM is presented in the presence of both CWI and swept sources. The main focus is the assessment of the cascaded scheme of multiple interference signals removal and the quality of the carrier phase measurements after removing interference. The characteristics of the simulated interference signals used in this test are summarized in Table 2.

The position and velocity errors after mitigating the CWI and the swept are shown in Figure 23 - Figure 28. Again larger position errors can be observed in the IIR case when comparing to the errors in the linear phase FIR case. This is because the group delay induced by an adaptive IIR notch filter is a function of both the Doppler frequency and the notch frequency mentioned in the previous section. The delay cannot be fully compensated by the clock bias term in the navigation solution. Larger vertical velocity errors (at the beginning of the test period) are observed in the IIR case compared to those in the FIR case. This is likely because an adaptive IIR notch filter has a frequency response with a non-linear phase. It is more sensitive to the pull-in effect of the CWI and swept tracking compared to an adaptive linear phase FIR notch filter.

Table 2 Parameters of simulated multi-CWI/swept interference Interference Frequency Power Data Type (MHz) (dBm) CWI: 1575.92 #3 CWI/Swept Swept: [1574.42 : -90 1575.42 ]

A reference station about 30 m away from the static rover station was created in the scenario for processing carrier phase measurements in Real Time Kinematic (RTK) mode. There was no interference simulated at the


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Figure 26: East velocity error - Data Set 3


Figure 27: North velocity error - Data Set 3


Figure 28: Vertical velocity error - Data Set 3

The 3D position errors after integer ambiguity fixed using the measurements from the receivers with an adaptive linear phase FIR notch filter and an adaptive IIR notch filter are shown in Figure 29 – Figure 31. The measurements from the rover station after interference mitigation and measurements from the reference station were processed by the RTK software package Flykin+™ developed at the University of Calgary. The results demonstrated successful ambiguity fixing under both


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CWI and swept interference with a power of -90 dBm using either the proposed adaptive linear phase FIR notch filter or the adaptive IIR notch filter. Sub-millimetre horizontal accuracy is achieved for the two static short baseline RTK solutions, while sub-centimetre accuracy for the vertical component is achieved. Besides, due to the linear phase property in the adaptive FIR notch filter, the positioning results show that the adaptive linear phase FIR notch filter requires less time to fix the ambiguities. Therefore, the proposed adaptive notch filters, especially the adaptive linear phase FIR notch filer can be used for carrier phase tracking under multiple strong CWI and/or swept interference and provide reasonable carrier phase measurements for RTK solutions. Figure 31: Vertical error after ambiguity fixing - Data set 3

CONCLUSIONS

Figure 29: Easting error after ambiguity fixing - Data set 3

Two multi-pole complex adaptive IIR and FIR notch filtering techniques were developed and implemented in a GNSS vector-based software receiver to mitigate the Continuous Wave Interference (CWI) and swept interference signals. The implementation details and the performance analysis of these two notch filters in a GNSS vector-based software receiver with simulated GNSS signals along with CWI and swept interference generated by a Spirent hardware simulator were presented. As shown, the linear phase adaptive FIR filter has better statistical performance compared to that of the adaptive IIR filter. The linear phase adaptive FIR notch filter is recommended for high precision applications since its group and phase delays are constant and can be easily compensated in the navigation solution. Initial RTK solutions indicate successful interference mitigation for generating the carrier phase measurements. However, further investigations are necessary for a thorough analysis of the carrier phase measurement quality under different interference sources and for different interference mitigation algorithms.

REFERENCES

Figure 30: Northing error after ambiguity fixing Data set 3

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(Available

at

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