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The L-band digital aeronautical communications system (L-DACS1) is subject to strong interference caused by distance measuring equipment (DME). For efficient statistical processing of interference, we adopt a Gaussian mixture (GM) distribution to model the impulsive nature of DME signals. Hence, we drive the parameters of the GM model in terms of p...
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... However, in practice, se ing appropriate thresholds for pulse blanking and clipping is challenging, leading to potential signal loss and inter carrier interference (ICI). Thus, several studies have explored the reconstruction of DME signals, employing methods such as compressed sensing, wavelet transform, and orthogonal transform [13][14][15]. However, these methods are noted to have limitations, including reconstruction errors and residual interference. ...
The L-band digital aeronautical communication system (LDACS) is one of the candidate technologies for future broadband digital aeronautical communications, utilizing the unused L-band spectrum between distance measuring equipment (DME) channels. However, the higher signal power of DME complicates LDACS implementation. This paper proposes an advanced DME mitigation approach for the LDACS, integrating joint direction of arrival (DOA) estimation with adaptive beamforming techniques. The proposed method begins by exploiting the cyclostationary characteristics of signals, accurately obtaining the preliminary direction of the LDACS signal using the Cyclic-MUSIC method. Subsequent precise steering vectors (SVs) are selected through Capon spectrum search, followed by the reconstruction of the interference plus noise covariance matrix (INCM). Using the obtained SV and INCM, the weight vector is calculated and beamforming is performed. Simulation results validate that the proposed method not only accurately estimates the direction of LDACS signal but also efficiently mitigates DME interference, demonstrating a superior performance and reduced algorithmic complexity, even in scenarios with lower signal-to-noise ratios (SNRs) and the presence of DOA estimation errors. Additionally, the proposed method achieves a low bit error rate (BER), further validating its ability to ensure communication quality and enhance the reliability of LDACS.
... However, the analysis of coexistence in the frequency or power domain provides the worst case results because the involved systems are assumed to transmit continuously [5], the result is consistent with the actual value only when baseband signal is analog, but the result is different from the actual value when the signal is pulsed with small duty cycle, since the interfering and desired signal do not overlap with each other most of the time, i.e., interference does not occur all the time. Some researchers use signal processing to analyze the coexistence between two pieces of equipment, for example Khodr A et al. [6] studied the cancellation of DME interference for aeronautical communications using signal processing. Miguel A. [7] assessed the impact of L-band digital aeronautical communications system (LDACS) on JTIDS (a military radio system known as Joint Tactical Information Distribution System) using signal processing based on some assumptions for JTIDS. ...
Analysis of the coexistence of two or more types of equipment is increasingly important. However, at present studies on the analysis method in the time domain are scant. Therefore, the aim of this paper is to explore the characteristics of signals and relations between interfering and desired signals in the time domain. Based on the periodicity of a signal, this paper presents a Periodic Pulse Overlap Method (PPOM). Using PPOM to analyze the interference from Distance Measuring Equipment (DME) to Air Traffic Control Radar Beacon System (ATCRBS) in the time domain, we obtain almost the same result as that based on the Monte Carlo Method (MCM). Furthermore, we discover the measures to reduce or even avoid interference, such as changing the Pulse Recurrence Frequency (PRF), adjusting the difference of initial time, and switching the operating modes of the equipment.
... A Markov model can be well integrated into a memoryless Gaussian mixture (GM) model to introduce memory between impulse noise samples (Shongwe, Vinck, & Ferreira, 2015). For efficient statistical processing of interference, (Saaifan, Elshahed, & Henkel, 2017) a GM distribution is adopted to model the impulsive nature of DME signals. It helps to design an optimum receiver for mitigating DME interference from LDACS1. ...
... Different frame types are distinguished based on their functionality. All the frames in FL and RL are arranged into super-frames (SF) and multi-frames (MF) as shown in Figures 3 and 4, respectively (Saaifan et al., 2017). The SF structure for FL is depicted in Figure 3, which is composed of one broadcast (BC) frame followed by four MFs. ...
In this article, an algorithm for mitigating distance measuring equipment (DME) interference in L-band digital aeronautical communication system type 1 (LDACS1)-based aeronautical communication systems is presented. The LDACS1 is a broadband system based on orthogonal frequency-division multiplexing (OFDM) is the most promising candidate for the future air-to-ground (A/G) communications. LDACS1 is operating as an inlay system and limited to the 500 kHz spectral gap between the two adjacent DME channels of bandwidth 1 MHz. In this work, a decision directed noise estimation (DDNE) approach to mitigating the pulsed interference from DME for LDACS1 system is proposed. It improves the conventional blanking and clipping approach for interference mitigation, which typically distorts the entire received signal, by combining the blanked and the original signal. The proposed designs are carried out for both cases of AWGN channel and En-Route aeronautical channel. The simulation results highlight the impact of DME interference onto the LDACS1 system and show the performance improvement of the proposed method compared to conventional interference mitigation methods in terms of bit error rate (BER).
... A Middletons Class-A model [1] represents one of the most applied models for narrowband radio frequency interference (RFI). This model is confirmed [1], [6] to represent a wide class of interference varying from a pure Gaussian distribution to a heavy-tailed distribution. For multiple antenna systems, a multivariate MCA model is verified to capture the noise statistics and the spatial coupling of impulse noise [4], [7]. ...
Construct a knowledge graph of anti-conventional jamming patterns based on the prior knowledge of conventional jamming patter, Firstly, adopt a manual extraction method to extract the prior information of the conventional jamming patterns and form a structured RDF data set; Then construct the anti-jamming knowledge graph pattern layer ontology from the bottom up and form the classification of anti-jamming decisions: Finally, use Java language to input the constructed data set and ontology classification into Neo4j to form an anti-jamming knowledge graph based on conventional jamming patterns. The result of the expansion of anti-jamming knowledge graph shows that the relationship between the entities of anti-jamming decision-making can be displayed directly by using the knowledge graph, which provides a basis for further implementation of low-complexity anti-jamming decision-making.KeywordsConventional jamming patternAnti-jammingKnowledge graph
In this paper, a low complexity sensing algorithm based on power spectrum density (PSD) for periodic impulsive interference is proposed. First, the PSD is computed by modified periodogram. Then the time occupancy of spectrum by interference and time interval of interference is computed in multiple detections to determine the presence of impulsive interference. Finally, main parameters of impulsive interference, such as period, duty cycle, bandwidth, and the peak power, are estimated. The computation afford of the proposed algorithm is quite low. The simulation results show that the sensing performance can satisfy the requirement of spectrum sensing.
