Modulation Parameter Estimation of LFM Interference for Direct Sequence Spread Spectrum Communication System in Alpha-Stable Noise

Liu, Mingqian; Han, Yuting; Chen, Yunfei; Song, Hao; Yang, Zhutian; Gong, Fengkui

doi:10.1109/jsyst.2020.2991078

Cited by 15 publications

(6 citation statements)

References 22 publications

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“…For noise with strong randomness and impulsiveness in complex electromagnetic environments, the symmetric α-stable (SαS) distribution noise model can be used to describe accurately [24,25]. Therefore, the impulsive noise model used in this paper is the SαS distribution noise model.…”

Section: The Impulsive Noise Modelmentioning

confidence: 99%

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A Novel Parameter Estimation Method Based on Piecewise Nonlinear Amplitude Transform for the LFM Signal in Impulsive Noise

et al. 2023

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In a complex electromagnetic environment, any noise present generally exhibits strong impulsive characteristics. The performance of existing parameter estimation methods carried out in Gaussian white noise for the linear frequency modulation (LFM) signal degrades or even fails under impulsive noise. This paper proposes a novel parameter estimation method to address this problem. Firstly, the properties of the piecewise nonlinear amplitude transform (PNAT) are derived. This manuscript verifies that the PNAT can retain phase information of the LFM signal while suppressing the impulsive noise. Subsequently, a new concept known as piecewise nonlinear amplitude transform parametric symmetric instantaneous autocorrelation function (PNAT-PSIAF) is proposed. Based on this concept, a novel method called piecewise nonlinear amplitude transform Lv’s distribution (PNAT-LVD) is proposed to estimate the centroid frequency and chirp rate of the LFM signal. The simulations show that the proposed algorithm can effectively suppress the impulsive noise without prior knowledge of the noise for both the single-component and double-component LFM signal. In addition, two parameters of the LFM signal can be precisely estimated by the proposed method under low generalized signal-to-noise ratios (GSNR). The stronger the impulsive characteristics of the noise, the better the performance of the algorithm.

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Section: The Impulsive Noise Modelmentioning

confidence: 99%

“…Equation (25) demonstrates that PN AT − LVD x ( f , k) is non-zero only when f = f 0i and k = k i h . By searching the peak of the PNAT-LVD, the coordinate (x i , y i ) corresponding to the peak can be obtained.…”

Section: The Improved Pnat-lvd Algorithmmentioning

confidence: 99%

A Novel Parameter Estimation Method Based on Piecewise Nonlinear Amplitude Transform for the LFM Signal in Impulsive Noise

et al. 2023

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“…Machine Learning systems can be classified according to the amount and type of supervision they get during training. There are many classification algorithms [22] like Support Vector Machines (SVMs) [23], Naïve Bayesian (NB) [24], K-Nearest Neighbors (KNN) [25], Decision Tree (DT) [26], and Artificial Neural Network (ANN). This work is based on artificial neural network to frequency and slope classification.…”

Section: Machine Learningmentioning

confidence: 99%

Instantaneous Frequency Estimation of FM Signals under Gaussian and Symmetric α-Stable Noise: Deep Learning versus Time-Frequency Analysis

Razzaq¹,

Hussain²

2022

Preprint

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Deep Learning (DL) and Machine Learning (ML) are widely used in many fields, but rarely used in Frequency Estimation (FE) and Slope Estimation (SE) of signals. Frequency and slope estimation for Frequency-Modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sonar, and radar measurements. In this work, artificial neural network (ANN) and convolutional neural network (CNN) are used in frequency and slope estimation for FM signals under Additive White Gaussian Noise (AWGN) and Additive Symmetric alpha Stable Noise (SαSN). SαS distributions are impulsive noise disturbances found in many communication environments like marine systems; their distribution lacks a closed-form Probability Density Function (PDF), except for specific cases, and infinite second-order statistic, hence Geometric SNR (GSNR) is used in this work to determine the impulsiveness of noise in a mixture of Gaussian and SαS noise processes. ANN is a machine learning classifier, designed with few layers for reducing FE and SE complexity while getting higher accuracy as compared with classical techniques. CNN is a deep learning classifier, designed with many layers for FE and SE, and proved to be more accurate than ANN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful for FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption, which is important in many systems such as some Internet of Things (IoT) sensor applications. After training DCNN for frequency and slope estimation of LFM signals, the performance of DCNN (in terms of accuracy) can give acceptable results at very low signal-to-noise ratios where TFD fails, giving more than 20dB difference in the GSNR working range.

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“…The α-stable distribution noise necessitates four parameters (α, γ, β, and µ), with the stable distribution characteristic function specified as [17,18]:…”

Section: Symmetric α-Stable Noisementioning

confidence: 99%

“…To generate SαS as shown in Equations (27b), ( 33) and ( 35) with four parameters chosen as follows: α = 1.8, β = 0, µ = 0, while the choice of γ is (scale parameter) relies on the ratio b = 20 as shown in Equation (19). Total power is p T = p x /GSNR as shown in Equation (17). SαS geometric power is p S = p T /(1 + b) and p G = b × p S as explained in the line above Equation (18).…”

Section: Hybrid Noise and Noisy Signal Generationmentioning

confidence: 99%

Instantaneous Frequency Estimation of FM Signals under Gaussian and Symmetric α-Stable Noise: Deep Learning versus Time–Frequency Analysis

Razzaq

Hussain

2022

Information

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Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation for LFM signals under additive white Gaussian noise (AWGN) and additive symmetric alpha stable noise (SαSN). DNN is composed of input, output, and two hidden layers, where several nodes in the first and second hidden layers are 25 and 8, respectively. CNN is the content input layer; many hidden layers include convolution, batch normalization, ReLU, max pooling, fully connected, and dropout. The output layer consists of a fully connected softmax and classification layers. SαS distributions are impulsive noise disturbances found in many communication environments such as marine systems, their distribution lacks a closed-form probability density function (PDF), except for specific cases, and infinite second-order statistics, hence geometric SNR (GSNR) is used in this work to determine the effect of noise in a mixture of Gaussian and SαS noise processes. DNN is a machine learning classifier with few layers for reducing FE and SE complexity. CNN is a deep learning classifier, designed with many layers, and proved to be more accurate than DNN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful to the FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption as compared to the classical FE based on time–frequency analysis, which are important requirements for many systems, such as some Internet of Things (IoT) sensor applications. After training CNN for frequency and slope estimation of LFM signals, the performance of CNN (in terms of accuracy) can give good results at very low signal-to-noise ratios where time–frequency distribution (TFD) fails, giving more than 20 dB difference in the GSNR working range as compared to the classical spectrogram-based estimation, and over 15 dB difference with Viterbi-based estimate.

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Modulation Parameter Estimation of LFM Interference for Direct Sequence Spread Spectrum Communication System in Alpha-Stable Noise

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 15 publications

References 22 publications

A Novel Parameter Estimation Method Based on Piecewise Nonlinear Amplitude Transform for the LFM Signal in Impulsive Noise

A Novel Parameter Estimation Method Based on Piecewise Nonlinear Amplitude Transform for the LFM Signal in Impulsive Noise

Instantaneous Frequency Estimation of FM Signals under Gaussian and Symmetric α-Stable Noise: Deep Learning versus Time-Frequency Analysis

Instantaneous Frequency Estimation of FM Signals under Gaussian and Symmetric α-Stable Noise: Deep Learning versus Time–Frequency Analysis

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