DME interference suppression algorithm based on signal separation estimation theory for civil aviation system
 Jie Li^{1},
 Renbiao Wu^{2},
 Yun Hao^{1}Email author,
 Xihua Wang^{1},
 Yali Wang^{1} and
 An Zhao^{1}
https://doi.org/10.1186/s1363801607368
© The Author(s). 2016
Received: 2 June 2016
Accepted: 20 September 2016
Published: 18 October 2016
Abstract
Using exclusively for civil aviation system, GPS (Global Positioning System) L5 signal is set up and takes up an appointed frequency band. However, the DME (distance measurement equipment) signal which has already applied for distance measurement in the civil aviation system works as the same frequency band as GPS L5 signal. As a forced highpower pulse interference, DME signal will decrease SINR (signaltointerference and noise ratio) of GPS L5 signal and even give rise to failure of acquisition and tracking. In case the DME interferences come from more than one base station, the traditional DME interference suppression methods which bring about the loss of GPS signal will suffer from serious performance degradation. In the light of the received signal model, a new DME interference mitigation algorithm is presented in this paper. At first, frequency is estimated with tmwapDFT (timemodulated windowed allphase DFT). Then, we use the estimated frequency to get amplitude and signal delay information by signal separation estimation theory. In addition, we consider the previous estimated results as the initial value, and a twodimension search method is implemented in a small range for further improving the parameter estimation accuracy. Based on the estimated parameters, DME signal can be exactly reconstructed and then eliminated. It can be shown from the experiment results that the proposed method which keeps more useful signal has a better performance compared with the conventional ones.
Keywords
GPS L5 signal DME interference mitigation Timemodulated windowed allphase DFT Signal separation estimation theory Precise estimation of parameters1 Introduction
GPS (Global Positioning System) L5 signal with an exclusive frequency band is specially used for civil aviation system. Compared with L1 and L2 signal, L5 signal has higher power, more positioning accuracy, stronger antijamming capability, and can be implemented more conveniently. However, there are a lot of interference equipment working at the L5 frequency band, among which DME (distance measurement equipment) signals make greatest influence on L5 signal.
As forced highpower pulse interferences, DME signal will decrease SINR (signaltointerference and noise ratio) at the receiver, reduce the acquisition satellite numbers, make the tracking loop unlocked, and finally lead to the decoding error or failure of navigation information [1–4]. Therefore, it is very necessary to solve this problem in civil aviation systems.
Lots of research organizations and universities have been seeking for the methods to mitigate the error brought from DME interference. Pulse blanking, notch filter, and hybrid method are most often used among the developed antiinterference techniques. Pulse blanking method mitigates the interference by setting the signals which exceed the threshold to zero in time domain [5, 6]. It is easy to implement with less computation complexity and has already verified on experimental hardware receiver [7]. However, it cannot thoroughly eliminate the bellshaped interference pulse, leaving tails of the pulse buried under noise level. The main deficiency of this method is that some useful GPS signal will be got rid of as well when the interferences are suppressed. Owing to high density of DME signal, this method will eliminate a large part of GPS signal. Acquisition and tracking error will be brought from a great amount of data missing.
Notch filter method mitigates the interference by letting the signal pass a narrow band notch filter involving DME frequencies [5, 8]. Both the central part and the tail part of bellshaped interferences can be thoroughly suppressed with this method because the DME signal only exists on some special frequencies. Although the frequency domain method can maintain more useful signal than pulse blanking method, there are also some useful signals eliminated.
The hybrid method uses a moving window in time domain to detect the DME pulse interference. When the DME pulse is detected, the data section will be transformed to frequency domain and be filtered. The filtered data should then be converted back to time domain instead of the originals [5, 9]. The hybrid method combines the two mentioned methods together and preserves more useful signals, but the data missing problem still exists.
The above methods eliminate the interference in time or frequency domain, and at the same time, the useful signal will suffer from loss more or less to a certain extent. In this paper, we propose a new DME interference suppression method which can keep useful GPS signal to a large extent meanwhile suppress the interference. Hence, DME signal can be reconstructed and eliminated without losing much SNR. The efficiency of the proposed approach is verified in the experiments.
2 Data model and problem description
When the airborne DME equipment takes advantage of the 64–126 X channel for communication, the ground equipment may respond frequencies ranged from 1151 to 1213 MHz. Unfortunately, this frequency band covers the GPS L5 signal with central frequency 1176.45 MHz and bandwidth 24 MHz.
Generally, DME interrogators send 5 to 150 pulse pairs per second, and the peak power varies from 50 W to 2 kW. The maximum of the pulses from transponder may be up to 2700 per second. Thus, DME signal from the ground is a strong jamming to GPS L5. It will seriously cut down the GPS receiver SINR and then cause the acquisition, tracking, and positioning errors. Therefore, it is necessary to solve DME interference suppression problem.
When the airplanes are near the ground base of DME, the airborne GPS receiver will be interfered by the pulses sent by DME ground base stations. Owing to the relative movement between the airplane and DME station, Doppler frequency shift must be taken into account. When the GPS receiver is interfered by only one DME base station, the interference can be eliminated by the method based on the NLS (nonlinear least squares) criterion [10]. In case the DME pulse interferences come from two or more different base stations, multiple Doppler shifts will exist because of the different relative velocities.
 1)
Estimate the Doppler frequency \( {\widehat{\omega}}_{dp} \) by tmwapDFT (timemodulated windowed allphase DFT) in the case of DME signal time delay and amplitude are unknown.
 2)
According to the signal separation estimation theory, the time delay and amplitude of each DME signal \( {\widehat{\alpha}}_p,{\widehat{\tau}}_p \), respectively, can be estimated with obtained Doppler frequency \( {\widehat{\omega}}_{dp} \).
 3)
In order to reconstruct the DME interference more precisely, we consider doing the local twodimension search in a small range which can further improve the parameter estimation accuracy.
3 Algorithm implementation
3.1 Signal separation estimation theory
Because the DME pulse is much stronger than GPS signal, there is no need to analyze GPS signal separately. Once DME signal is detected, parameters can be estimated under the NLS criterion [10, 11]. The purpose of suppressing interferences can be achieved by subtracting the estimated DME signal which has been reconstructed according to the already known analytic formula from the received signal.
DME pulse pairs can be simply detected by means of calculating the correlation of received signal and a moving window with the width of 12 μs and discriminating whether or not the correlation value is beyond the threshold we have set in advance.
Note that the values of \( 2{\widehat{\omega}}_{dp} \) and \( {\widehat{\omega}}_p \) can be obtained as the location of the dominant peak of the magnitude squared of the Fourier transform \( {\left\mathbf{b}\left({\omega}_{dp}\right){\mathbf{y}}_p^2\right}^2 \) and a ^{ H }(ω _{ p })Ŝ*Y _{ p }^{2}, which can be efficiently computed by using the FFT with vectors \( {\mathbf{y}}_p^2 \) and Ŝ*Y _{ p } padded with zeros, respectively.

Step 1: Assume p = 1; take α _{1} s(n − τ _{1}) as unknown waveform. Then, calculate the frequency estimation \( {\widehat{\omega}}_{d1} \) according to Eq. 9. We can further estimate the complex amplitude \( {\widehat{\alpha}}_1 \) and time delay \( {\widehat{\tau}}_1 \) from Eqs. 10 and 11 with \( {\widehat{\omega}}_{d1} \).

Step 2: Assume p = 2; calculate ŷ _{2}(n) with Eq. 7 by using \( {\widehat{\alpha}}_1,{\widehat{\tau}}_1,{\widehat{\omega}}_{d1} \) obtained in step 1. After that, \( {\widehat{\alpha}}_2,{\widehat{\tau}}_2,{\widehat{\omega}}_{d2} \) can be obtained by the method similar to step 1.

Step 3: Compute ŷ _{1}(n) by using \( {\widehat{\alpha}}_2,{\widehat{\tau}}_2,{\widehat{\omega}}_{d2} \) and then redetermine \( {\widehat{\alpha}}_1,{\widehat{\tau}}_1 \) from ŷ _{1}(n).

Step 4: Iterate the previous two steps until convergence is achieved to get the final result of \( {\left\{{\widehat{\alpha}}_p,{\widehat{\tau}}_p,{\widehat{\omega}}_{dp}\right\}}_{p=1}^2 \).

Step 5: Assume p = 3, 4 … P; repeat the above steps until all parameters of P DME signals are estimated and the convergence conditions are satisfied in the meantime.
The traditional mitigation method directly makes the DME signal to zero whether interference is located in time or frequency domain. But in case the interference signals come from two or more base stations, the loss of useful data will be grown in a number of quantity due to the higher interference duty ratio. However, the method proposed in this paper can achieve a better performance because of the integrity guarantee of useful data.
3.2 Frequency estimation with timemodulated windowed allphase DFT
Although a rather effective method has been afforded above, it is difficult to analyze a frequency component accurately with DFT from Eq. 9 because of spectral leakage and picketfence effect [13]. For the sake of attaining a more accurate estimate and decreasing iterations, tmwapDFT is taken advantage instead of original DFT. Besides the property of restraining spectral leakage, the approach can compensate for the deficiency of leaving out a half of information caused by windowed apDFT [14, 15].
FFT can be used here to reduce computation complexity of windowed apDFT.
Compared with original DFT, tmwapDFT without spectral leakage and information loss can get a more precise frequency estimate. So it is a better choice to get estimate \( {\widehat{\omega}}_{dp} \) of ω _{ dp } by tmwapDFT in the algorithm mentioned above.
3.3 Precise estimation of parameters
So as to further improve the estimation accuracy, a fine estimation method of twodimension search which takes the previous estimated results as the initial values is presented in this paper.
Note that \( {\widehat{\tau}}_p,\kern0.5em {\widehat{\omega}}_{dp} \) represent the estimates of time delay and frequency of DME signal while τ _{ p }, ω _{ dp } represent the true value of them.
When the estimated value \( {\widehat{\omega}}_{dp} \) is in accord with the true value ω _{ dp }, the DME information can be correctly demodulated from s(n − τ _{ p }). Due to the DME signal that is a pulse pair in time domain, the correlation function cannot achieve the maximum value unless DME signal is completely aligned and \( {\widehat{\tau}}_p \) is exactly consistent with τ _{ p }. Therefore, we can precisely estimate time delay and Doppler frequency of the DME signal \( \left\{{\widehat{\tau}}_{p\_f},{\widehat{\omega}}_{dp\_f}\right\} \) by implementing a twodimension search within a small range. Relatively accurate estimation results have been given as initial values via Eqs. 10 and 22, consequently, a smallscale twodimension search does not bring in extravagant computation. Even so, the search step is still one of the main factors to determine the computational complexity of the algorithm. Although a comparatively coarser step can make the estimation process faster, the accuracy would have a partial loss in the meantime. A compromise between the processing speed and the accuracy should be taken into account in this method.
4 Experimental results
To test and verify the performance of the proposed method, the experiments have been carried out, where the satellite data are interfered by DME pulse and downconverted at the intermediate frequency of 1.25 MHz same as the GPS signal. The sample rate is 5 MHz and the SNR (signaltonoise ratio) is 18 dB.
5 Conclusions
The traditional DME interference suppression methods which bring about the loss of GPS signal will suffer from serious performance degradation of the GPS receiver. In this paper, a new DME interference suppression method for GPS L5 signal is proposed, when the DME interferences come from more than one base station. Firstly, frequency is estimated with tmwapDFT (timemodulated windowed allphase DFT). Then, we use the estimated frequency to get amplitude and signal delay information with signal separation estimation theory. In order to further improve the estimation accuracy, a twodimension fine estimation method is proposed, which takes the previous estimated results as the initial values. Experiment results show that the proposed method could estimate the parameters precisely, by which DME signal can be reconstructed and eliminated. It can be shown from the experiment results that the proposed method which keeps more useful satellite data has a better performance compared with conventional ones.
Declarations
Acknowledgements
The work of this paper is supported by the Project of the National Natural Science Foundation of China (Grant nos. 61172112, 61179064, and 61271404), Science and Technology Fund of Civil Aviation Administration of China (Grant no. MHRD0606), and the Fundamental Research Funds for the Central Universities (Grant no. ZXH2009A003).
Authors’ contributions
JL addressed the new method, carried out the antijamming studies and drafted the manuscript. RW conceived of the study. YH participated in its design and coordination. XW participated in the design of the study and performed the statistical analysis. YW participated in the design of the study. AZ participated in the sequence alignment. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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