Compressive Sampling for Recognition of Frequency-Hopping Spread Spectrum Signals

In this paper, compressive sensing strategies for interception of Frequency-Hopping Spread Spectrum (FHSS) signals are introduced. Rapid switching of the carrier among many frequency channels using a pseudorandom sequence (unknown to the eavesdropper) makes FHSS signals dicult to intercept. The conventional approach to intercept FHSS signals necessitates capturing of all frequency channels and, thus, requires the Analog-to-Digital Converters (ADCs) to sample at very high rates. Using the fact that the FHSS signals have sparse instanta- neous spectra, we propose compressive sensing strategies for their interception. The proposed techniques are validated using Gaussian Frequency-Shift Keying (GFSK) modulated FHSS signals as dened by the Bluetooth specication.

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.

A robust parameter estimation of FHSS signals using time–frequency analysis in a non-cooperative environment

Recently, the use of unmanned aerial vehicles for both reconnaissance and fire strikes has been increasing. The control of such vehicles and the transmission of telemetry information are carried out using a combined command and telemetry radio link and frequency hopping spread spectrum technology. In such conditions, the task of developing automatic algorithms for detecting and separating radio links for portable radio monitoring devices becomes especially relevant. The aim of this work is to extend the capabilities of portable radio monitoring devices to detect unmanned aerial vehicles’ radio signals in sophisticated electronic environment by automating signal processing. Formally, the research task is to detect and separate several frequency hopping spread spectrum signals in same frequency band. To solve this task, it is first necessary to detect the frequency channels and estimate frequency and, if necessary, temporal and modulation parameters of the signals in these channels. Estimates of signal parameters values are recorded in associative arrays. The paper proposes a scheme of the algorithm for automatic detection and separation of signals with frequency hopping spread spectrum, as well as the structure of the array with the results of frequency band analysis. In the study of developed algorithm, we considered the practically important option of separating radio links with frequency hopping spread spectrum by the spectral width of the frequency element and the value of the signal-to-noise ratio in the frequency channel, since these features can be quite easily distinguished in the frequency domain. The radio links separation was performed by cluster analysis of scatter plots of parameter pairs estimates using a Gaussian mixture model. The developed algorithm can be implemented in existing and prospective portable radio monitoring devices to automatically detect and determine the number of unmanned aerial vehicles by their radio links signals, in particular, under conditions of a priori uncertainty about the values of signal parameters.

A hybrid direct sequence and frequency hopping (DSFH) spread spectrum signal can greatly improve ranging precision and jamming resisting capability in spacecraft TT and C (Tracking, Telemetry and Command). The frequency hopping adds another searching dimension to acquisition and increases the acquisition complexity. Therefore, this paper designs a phase-compensating-based acquisition algorithm. First, the model DSFH signal is introduced. Then the auto-correlation function and the acquisition problem are analyzed. Finally, a phase-compensating-based acquisition algorithm is proposed, its computation complexity is analyzed, and the signal-to-noise ratio (SNR) losses due to necessary approximations are shown. Simulations validate the advantageous performance of our algorithm. The proposed method can be modified to adapt to different PN chip rates and frequency hopping rates.

This study proposes a parameter estimation technique based on the short-time fractional Fourier transform (STFRFT) for Chirp /Frequency Hopping (Chirp/FH) spread spectrum signal. It is able to detect and estimate parameters for Chirp/FH signal at low signal-to-noise ratio (SNR). At the end, a comparison between the new method and short-time Fourier transform-based method is provided to illustrate the advantages.

In this paper we present research of the resistance of Frequency Hopped Spread Spectrum Signals (FHSSS) to intentional interference in case of coincidence of their code structures and different values of carrier and clock frequencies.

A novel approach to slow frequency hopping (FH) spread-spectrum signal tracking is presented and analyzed. This approach is based on utilization of additional synchronization data carrying information for FH signal tracking purposes which is added to the digital data stream. Using a renewal process model, the steady-state probability mass function for the phase error process is derived, along with expressions for the tracking error variance, and the mean time to lose lock. Results are presented for several different tracking algorithms and a rough comparison with a standard early-late gate tracking loop is made.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Initial synchronization (acquisition) schemes of frequency hopped (FH) spread spectrum signals often make use of serial-search acquisition techniques. Two FH serial-search acquisition schemes are introduced and analysed. These schemes are the fixed dwell-time with false alarm memory (FDT/FAM) and the double dwell-time with binary integration (DDT/BI) scheme. Statistical properties namely, probability density function, characteristic function and mean value of the random acquisiton time of each acquisition scheme are obtained. The performances of the FDT, FDT/FAM and DDT/BI acquisition schemes are established and compared in the presence of partial band noise jamming, partial band tone jamming and additive white gaussian noise (AWGN). The results indicate that the DDT/BI acquisition scheme outperforms the FDT and FDT/FAM acquisition schemes.

An analysis procedure of electromagnetic compatibility between radio equipment of a com-mand and control point (CP) of unmanned aircraft (UA) carrying a radio transmitter (RT) and a radio receiver (RR) and stationary land radio electronic equipment (REE) stations containing radio transmitters and/or radio receivers and surrounding the CCP is considered. The procedure involves analysis of the scenarios where emissions produced by the surrounding transmitters may have an effect on the quality of the signal received by the CCP receiver and where the emission produced by the CCP transmitter may have effect on the quality of signals received by the recei¬vers surrounding the CCP. It assumed that frequency-hopping spread spectrum signals are applied in both downlink and uplink. To assess the interference effect from emissions of the surrounding transmitters on the quality of the signal received by the CCP receiver the protection ratio is used. To assess the interference effect from emissions of the CCP transmitter on the quality of the signal received by the surrounding receivers the receiver desensitization is used.

Satellite Navigation Receiver Autonomous Integrity Monitoring (RAIM) is developed for detection and recognition of satellite faults using the redundant observation values of receiver itself. This article first makes a comparative analysis to the conventional and recently proposed RAIM fault detection and identification methods, and then points out that the conventional fault identification methods have such weakness as low fault detection probability and high false alarm probability in the situation of simultaneous multiple faulty satellites, while the recently proposed multi-satellite fault identification methods require excessive computation work though enjoy a higher detection probability. To solve these problems, a new RAIM multiple satellite fault detection and recognition method is proposed in this paper, which is characterized by high detection probability, low false alarm probability and low computation load, practicable for project implementation. Based on a simulation analysis, it turns out that this method has a higher detection probability without false alarm at all. This method requires so low computation work that it only needs 2 m times of least squares residuals fault detection and m times of fault identification in m (m > 0) faulty satellites situation, showing a very promising application value.

This paper introduces a sensor-array geolocation and blind interference rejection algorithm for multiple noncooperative wideband fast frequency-hop (FFH) spread spectrum signals of unknown hop sequences and unknown arrival angles. This algorithm avoids the need to adjust beamforming weights to accommodate frequency variations in the frequency-hopping signals. This method also exploits the sources' polarizational diversity in addition to their diversity in spatial angles and temporal characteristics. This novel approach deploys one electromagnetic vector sensor, which is composed of six spatially co-located non-isotropic polarization-sensitive antennas measuring all six electromagnetic-field components of the incident wave-field. The spatial co-location of all component antennas comprising such a vector-sensor frees the beamforming weights of any dependence on the signals' hopping frequencies. This algorithm is also applicable to blind co-channel interference rejection in frequency-hop code-division multiple access (CDMA) wireless communications.

A new method of detection of coding signals under the background of sea clutter is presented. The process of signal detection consists of three stages: modeling sea clutter signals based on chaos, one-step ahead prediction of chaotic signals and detection decision making. In this method, models of chaotic signals were created in the form of multi-layer perceptron neural networks, coded signals take on 13-element Barker code. The experiment results show detection of coding signal by using this method has higher detection probability and lower false alarm probability and good performance of the whole detection although with a low SNR. This method turned out to be very robust to different chaotic signals.

In order to solve the problem of low detection rate of frequency hopping (FH) signal under low SNR, an FH signal detection technology based on deep neural network (vgg16 network) is proposed. Firstly, this paper introduces the principle of FH signal. Then, this paper presents the SSD deep neural network detection framework, and uses this framework to detect FH signal and estimate its parameters. Time-frequency distribution maps of FH signals are used as the input of deep neural network model to carry out tag learning and training, and then the trained model is used to realise FH signal detection. At the same time, aiming at the noise problem of time-frequency distribution map, the time-frequency graph correction method of K-means clustering algorithm is proposed. After modification, it is more tolerant to noise. The simulation results show that when the SNR is –4 dB, the detection rate of FH signal can reach more than 88%. Finally, the performance of parameter estimation of FH signal is analysed.

The cognitive radio is considered a best solution for the limited spectrum resources problem. The periodogram based energy detection can be used for spectrum estimation in cognitive radio. It does not need any prior information about the primary signal. This paper presents a new periodogram by using the Discrete Cosine Transform (DCT). In addition, it analyses and compares the performance with raw periodogram. The result reveals the DCT based periodogram is better than the traditional one due to its low variance. Consequently, the proposed system has a high probability of detection with low probability of false alarm even in case of lower SNR.