Detection of an Unknown Frequency Hopping Signal Based on Image Features

Luo, Siwei; Luo, Lai-yuan

doi:10.1109/cisp.2009.5301705

Cited by 4 publications

(2 citation statements)

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“…3(a). In order to represent the signal in a more plausible way, we adopt a widely used morphological dilation and erosion processes from the domain of image processing [12,13] to recover the received signal properly. A signal with impairments and the output of dilation and erosion processes are shown in Fig.…”

Section: Proposed Methodsmentioning

confidence: 99%

Measurement based FHSS–type Drone Controller Detection at 2.4GHz: An STFT Approach

Kaplan

Kahraman

Görçin

et al. 2020

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)

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The applications of the unmanned aerial vehicles (UAVs) increase rapidly in everyday life, thus detecting the UAVs and/or its pilot is a crucial task. Many UAVs adopt frequency hopping spread spectrum (FHSS) technology to efficiently and securely communicate with their radio controllers (RCs) where the signal follows a hopping pattern to prevent harmful interference. In order to realistically distinguish the frequency hopping (FH) RC signals, one should consider the real-world radio propagation environment since many UAVs communicate with RCs from a far distance in which signal faces both slow and fast fading phenomenons. Therefore, in this study different from the literature, we consider a system that works under realconditions by capturing over-the-air signals at hilly terrain suburban environments in the presence of foliages. We adopt the short-time Fourier transform (STFT) approach to capture the hopping sequence of each signal. Furthermore, time guards associated with each hopping sequence are calculated using the autocorrelation function (ACF) of the STFT which results in differentiating the each UAV RC signal accurately. In order to validate the performance of the proposed method, the results of normalized mean square error (MSE) respect to different signal-to-noise ratio (SNR), window size and Tx-Rx separation values are given.

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Section: Proposed Methodsmentioning

confidence: 99%

Measurement based FHSS–type Drone Controller Detection at 2.4GHz: An STFT Approach

Kaplan

Kahraman

Görçin

et al. 2020

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)

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“…The non-cooperative detection of the FHSS signal is the first step of the entire signal interception procedure [2]. Although various methods has been rendered since the 1990s (e.g., methods based on time-frequency analysis [3][4][5][6][7][8], wavelet analysis [4,[9][10][11][12][13], auto-correlation analysis [9,[14][15][16], likelihood analysis [17][18][19][20][21], etc. ), energy thresholding is the most commonly used in FHSS signal detection [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Enhanced Compressed Measurements for Detection of Frequency-Hopping Spread Spectrum Signals Based on Task-Specific Information and Deep Neural Networks

Liu

Jiang

2022

Entropy

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The frequency-hopping spread spectrum (FHSS) technique is widely used in secure communications. In this technique, the signal carrier frequency hops over a large band. The conventional non-compressed receiver must sample the signal at high rates to catch the entire frequency-hopping range, which is unfeasible for wide frequency-hopping ranges. In this paper, we propose an efficient adaptive compressed method to measure and detect the FHSS signals non-cooperatively. In contrast to the literature, the FHSS signal-detection method proposed in this paper is achieved directly with compressed sampling rates. The measurement kernels (the non-zero coefficients in the measurement matrix) are designed adaptively, using continuously updated knowledge from the compressed measurement. More importantly, in contrast to the iterative optimizations of the measurement matrices in the literature, the deep neural networks are trained once using task-specific information optimization and repeatedly implemented for measurement kernel design, enabling efficient adaptive detection of the FHSS signals. Simulations show that the proposed method provides stably low missing detection rates, compared to the compressed detection with random measurement kernels and the recently proposed method. Meanwhile, the measurement design in the proposed method is shown to provide improved efficiency, compared to the commonly used recursive method.

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