Frequency Hopping Spread Spectrum Signals Research Articles

Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning.

As one of the most widely used spread spectrum techniques, the frequency-hopping spread spectrum (FHSS) has been widely adopted in both civilian and military secure communications. In this technique, the carrier frequency of the signal hops pseudo-randomly over a large range, compared to the baseband. To capture an FHSS signal, conventional non-cooperative receivers without knowledge of the carrier have to operate at a high sampling rate covering the entire FHSS hopping range, according to the Nyquist sampling theorem. In this paper, we propose an adaptive compressed method for joint carrier and direction of arrival (DOA) estimations of FHSS signals, enabling subsequent non-cooperative processing. The compressed measurement kernels (i.e., non-zero entries in the sensing matrix) have been adaptively designed based on the posterior knowledge of the signal and task-specific information optimization. Moreover, a deep neural network has been designed to ensure the efficiency of the measurement kernel design process. Finally, the signal carrier and DOA are estimated based on the measurement data. Through simulations, the performance of the adaptively designed measurement kernels is proved to be improved over the random measurement kernels. In addition, the proposed method is shown to outperform the compressed methods in the literature.

A modified convolutional neural network with rectangular filters for frequency-hopping spread spectrum signals

Classification of frequency-hopping spread spectrum (FHSS) signal in a complex electromagnetic environment is essential due to security concerns associated with its applications, such as drones. Traditional convolutional neural networks (CNNs) include square-designed filters and pooling operators at several layers, which are appropriate for two-dimensional images. Nevertheless, the information encoded in time-frequency representation (TFR) produced by the spectrogram method is different. The time and frequency of the FHSS signal are represented along the x- and y-axis, respectively, whereas the intensity at a specific spot indicates the amplitude, thereby producing it in a rectangular form. Therefore, in this study, a modified convolutional neural network (MCNN) with rectangular filters is proposed to classify FHSS signals, with a background signal and additive white Gaussian noise being present as interference. Rectangular filters of varying shapes and sizes with max-pooling in their regions are used to extract differentiative features from the TFR using MCNN. A problem of an unbalanced dataset occurred because of the unequal observations amongst the classes, which is solved by employing the random erasing method. The developed method efficaciously acquires distinguished features from the TFR and performs better than traditional CNN with 80.44% accuracy at −2 dB of signal-to-noise ratio (SNR). The code to verify the proposed work is available at: https://github.com/DrTuryalai/MCNN.

A Novel Parameter Estimation Method of FHSS Signal with Low SNR

A novel parameter estimation method of frequency hopping spread spectrum (FHSS) signal is proposed. The observed signal is divided into multiple segments and forms an observed matrix, performing FFT on the observed matrix with different segment lengths to obtain the corresponding spectrum matrix. By analyzing the convergence characteristics of the spectrum matrix, the estimation of the symbol period is realized, on this basis, the symbol‐frequency distribution is obtained and its projected spectrum in Frequency‐Amplitude plane is further obtained to select the candidate frequencies. The matrix composed of multiple single frequency signals is constructed according to the candidate frequencies and multiplied with the observed matrix under the segment length of symbol period, then the number of symbols per hopping duration is estimated according to the matrix multiplication result, so as to realize the estimation of the hopping duration, and finally the real hopping frequencies and hopping sequence can be found out from the candidate frequencies. Simulation results are presented to indicate that the proposed method has good estimation performance especially under the condition of low SNR. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Low-False-Alarm-Rate Timing and Duration Estimation of Noisy Frequency Agile Signal by Image Homogeneous Detection and Morphological Signature Matching Schemes.

Frequency hopping spread spectrum (FHSS) applies widely to communication and radar systems to ensure communication information and channel signal quality by tuning frequency within a wide frequency range in a random sequence. An efficient signal processing scheme to resolve the timing and duration signature from an FHSS signal provides crucial information for signal detection and radio band management purposes. In this research, hopping time was first identified by a two-dimensional temporal correlation function (TCF). The timing information was shown at TCF phase discontinuities. To enhance and resolve the timing signature of TCF in a noisy environment, three stages of signature enhancement and morphological matching processes were applied: first, computing the TCF of the FHSS signal and enhancing discontinuities via wavelet transform; second, a dual-diagonal edge finding scheme to extract the timing pattern signature and eliminate mismatching distortion morphologically; finally, Hough transform resolved the agile frequency timing from purified line segments. A grand-scale Monte Carlo simulation of the FHSS signals with additive white Gaussian noise was carried out in the research. The results demonstrated reliable hopping time estimation obtained in SNR at 0 dB and above, with a minimal false detection rate of 1.79%, while the prior related research had an unattended false detection rate of up to 35.29% in such a noisy environment.

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

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.

Detection of the Frequency Hopping Spread Spectrum Signal Sources and Estimation of Their Number by Direction Finding Data

Detection of the Frequency Hopping Spread Spectrum Signal Sources and Estimation of Their Number by Direction Finding Data

Adaptive Measurement and Decoding of Frequency-Hopping Spread Spectrum Signals Based on Knowledge Enhanced Compressed Sensing

Adaptive Measurement and Decoding of Frequency-Hopping Spread Spectrum Signals Based on Knowledge Enhanced Compressed Sensing

Detection, Identification, and Direction of Arrival Estimation of Drone FHSS Signals With Uniform Linear Antenna Array

Safety, security, and privacy are three critical concerns affiliated with the use of drones in everyday life. Considering their ever-shrinking sizes and capabilities, being aware of drone activities in the vicinity becomes an important surveillance item. Therefore, keeping track of drones and preferably their controllers should be included into the already-existing security measures. In this study, a frequency hopping spread spectrum (FHSS) type drone controller signal detection and emitter direction finding framework is proposed to achieve aforementioned goals. Since drone communications signals generally coexist with other FHSS signals in 2.4 GHz industrial, scientific, and medical (ISM) band, first, a method based on cyclostationarity analysis is proposed to distinguish the drone radio controller signals from other signals utilizing 2.4 GHz ISM band. Then, a variant of short-term Fourier transform is introduced to extract the parameters of detected drone remote controller signals. The correct hopping signals are then aggregated based on the clustered parameters to obtain their combined baseband equivalent signal. Furthermore, the resampling process is applied to reduce the unrelated samples in the spectrum and represent the spectrum with the reconstructed signal, which has a much lower bandwidth than the spread bandwidth. Finally, two different multiple signal classification algorithms are utilized to estimate the direction of the drone controller relative to the receiving system. In order to validate the overall performance of the proposed method, the introduced framework is implemented on hardware platforms and tested under real-world conditions. A uniform linear antenna array is utilized to capture over-the-air signals in hilly terrain suburban environments by considering both line-of-sight and non-line–of-sight cases. Direction estimation performance is presented in a comparative manner and relevant discussions are provided.

Decoding and measurement of frequency‐hopping spread spectrum signals using an adaptive algorithm‐based compressive sensing

SummaryDecoding of frequency‐hopping spread spectrum (FHSS) methods to intercept the symbol content in signals and brilliant measurement is presented in this paper. The switching of the frequency channels rapidly using a pseudorandom code makes interception of FHSS signals very difficult. Therefore, a proposed adaptive compressive measurement for decoding the frequency‐hopping receivers that have no prior knowledge of the hopping sequence is the main focus of this work. The proposed methods rely on the instantaneous sparsity of the spectra of FHSS signals, and the transmitted symbols can be recovered from the proposed compressive measurements. Furthermore, the contribution of this work is the design of a proposed adaptive method that is used to design measurement kernels for compressive measurement and decoding based on knowledge enhancement, which includes both prior information enhanced and adaptive methods based on the properties of the intersignals. Simulation results on Gaussian frequency‐shift keying (GFSK) FHSS signals illustrate that the proposed compressive method enhances the detection performance of the received frequencies with acceptable decoding accuracy compared with other techniques such as enhanced orthogonal matching pursuit (EOMP) and an adaptive sampling kernels method with a random compressive ratio. Also, an enhancement of the proposed algorithm is achieved due to enhancement in both the normalized mean square error (NMSE) and the probability of correction error (Pce). The comparison between these algorithms is performed using simulation based on MATLAB and receiver operating characteristic (ROC) curves.

CDMA Modulation for Communication System Environment using Frequency Hopping Spread Spectrum

In this paper, an overall framework for a joint Special emphasis is placed on the communication segment of the sensing system at 85 GHz. Code division multiplexing using frequency hopping spread spectrum signals is implemented at 85 ghz to take advantage of reduced interference between ambient communication. The framework, which spans the entire chain of signal processing, mat lab is enabled, explained, and simulated using data networking. A template, able to scatter, fraud detection, including radio frequency-block and synchronization non-idealities are built up and analyzed. Also, the implementation of a channel model is into the Win Prop technology and embedded into the simulation of simu link. In the previous paper, they implemented the code division multiple access using a direct sequence spread spectrum at 77ghz for secure communication. Because of using 77ghz for the direct sequence spread spectrum, there will occur noise distortion and interference in the communication system. So that there will be poor system communication between transmitter and receiver. To overcome these problems, our paper explains the implementation of code division multiple access using frequency hopping spread spectrum for better and secure communications. By using this frequency-hopping spread spectrum technology, we can reduce the noise distortion and interference between the transmitter and the receiver. So that our system will be in proper condition to transmit the signals in the same range of frequency without any interference and distortion. FHSS systems can allow a higher aggregate bandwidth for coverage because FHSS provides more channels in the same range of frequencies. In accordance with the bit-error-rate, the module is assessed. By creating white Gaussian additive noise. The attribute is proven to reconcile the theoretical assumptions with the outcomes. By organizing a Rake-Receiver, the system is further boosted with structure configuration without any distortions.

Detection and Frequency Estimation of Frequency Hopping Spread Spectrum Signals Based on Channelized Modulated Wideband Converters

It is challenging to detect and track frequency hopping spread spectrum (FHSS) signals due to their wideband frequencies and the limitations of current hardware. In the implementation, there has been a trend of conducting compressive sensing for blind signal processing of FHSS signals. The modulated wideband converter (MWC) is a type of sub-Nyquist sampling system, which accomplishes the recovery of highly accurate broadband sparse signals by multichannel sub-Nyquist sampling sequences. However, it is difficult to adjust MWC to FHSS signals, because the support set and sparsity change with the hop. In this paper, we propose a channelized MWC scheme in order to solve these problems. First, the proposed method distributes the sub-bands to different channels. We can derive and refresh the frequency support set rapidly without recovery. Secondly, by reconstructing the low-pass filter and decimation, we reduced the computational cost to 1/m as the traditional m-channel MWC scheme, where m is the number of channels. Moreover, we propose a series of strategies to estimate carrier frequency. The numerical simulations show that our method can detect the channel, which contains FHSS signals in the case of a low signal-to-noise ratio. Furthermore, the estimation method leads to the successful estimation of the FHSS carrier frequency. This indicates that our method is also effective in the broadband non-cooperative spectrum sensing.

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

Frequency hopping spread spectrum (FHSS) signals are widely implemented in both modern civilian and military applications. They are robust to channel impairments because of their low probability of interception. For applications that require the interception of FHSS signals, the signal parameters such as the hopping frequencies, hopping duration and hopping sequence should be accurately measured. In this paper, an accurate FHSS signal parameter estimation method is proposed based on quadratic time–frequency distributions (QTFDs). The extended modified B-distribution (EMBD) and the adaptive smoothed Wigner–Ville (SWWVD) are used which have the properties of high time–frequency resolution. The adaptive SWWVD requires no prior knowledge of the signal parameters since the kernel parameters are estimated from the signal characteristics and are compared to the EMBD which operates at the optimal kernel parameters. The proposed instantaneous frequency (IF) estimate method is compared to the time–frequency (TF) moments method and benchmarked with the Cramer–Rao lower bounds (CRLBs). The computational complexity of the IF estimation method is reduced by a factor of five compared to the TF moments method. Furthermore, the results show that the IF estimation method outperforms moments method where the mean-squared error (MSE) of the hopping frequencies estimate meets at minimum SNR of −3 dB and the hopping duration estimate MSE meets the CRLB at SNR of 0 dB.

Compressive Sampling for Detection of Frequency-Hopping Spread Spectrum Signals

In this paper, methods for detection of frequency-hopping spread spectrum (FHSS) signals from compressive measurements are proposed. Rapid switching of the carrier frequency in a pseudorandom manner makes detection of FHSS signals challenging. Conventionally, FHSS detection is performed by scanning small segments of the spectrum in a sequential manner using a sweeping spectrum analyzer (SSA). However, SSAs have the inherent risk of missing the transmitted signal depending on factors such as the rate of hopping and scanning. In this paper, we propose compressive detection strategies that sample the full FHSS spectrum in a compressive manner. We discuss the use of random measurement kernels as well as designed measurement kernels in the proposed architecture. The measurement kernels are designed to maximize the mutual information between the FHSS signal and the compressive measurements. Using a mixture-of-Gaussian model to represent the FHSS signal, we derive a closed-form gradient of the mutual information with respect to the measurement kernel. Theoretical analysis and simulation results are provided to compare different systems. These results demonstrate that the proposed compressive system with random measurement kernels is not subject to the performance limitations suffered by SSAs when their scanning rates are low and designed adaptive measurement kernels provide enhanced detection performance compared to random ones.

A zero power harmonic transponder sensor for ubiquitous wireless μL liquid-volume monitoring.

Autonomous liquid-volume monitoring is crucial in ubiquitous healthcare. However, conventional approach is based on either human visual observation or expensive detectors, which are costly for future pervasive monitoring. Here we introduce a novel approach based on passive harmonic transponder antenna sensor and frequency hopping spread spectrum (FHSS) pattern analysis, to provide a very low cost wireless μL-resolution liquid-volume monitoring without battery or digital circuits. In our conceptual demonstration, the harmonic transponder comprises of a passive nonlinear frequency multiplier connected to a metamaterial-inspired 3-D antenna designed to be highly sensitive to the liquid-volume within a confined region. The transponder first receives some FHSS signal from an interrogator, then converts such signal to its harmonic band and re-radiates through the antenna sensor. The harmonic signal is picked up by a sniffer receiver and decoded through pattern analysis of the high dimensional FHSS signal strength data. A robust, zero power, absolute accuracy wireless liquid-volume monitoring is realized in the presence of strong direct coupling, background scatters, distance variance as well as near-field human-body interference. The concepts of passive harmonic transponder sensor, metamaterial-inspired antenna sensor, and FHSS pattern analysis based sensor decoding may help establishing cost-effective, energy-efficient and intelligent wireless pervasive healthcare monitoring platforms.

Precise Ionosphere Monitoring via a DSFH Satellite TT&C Link

AbstractA phase-coherent and frequency-hopped PN ranging system was developed, originally for the purpose of anti-jamming TT&C (tracking, telemetry and telecommand) of military satellites of China, including the Beidou-2 navigation satellites. The key innovation in the synchronization of this system is the unambiguous phase recovery of direct sequence and frequency hopping (DSFH) spread spectrum signal and the correction of frequency-dependent phase rotation caused by ionosphere. With synchronization achieved, a TEC monitoring algorithm based on maximum likelihood (ML) principle is proposed and its measuring precision is analyzed through ground simulation, onboard confirmation tests will be performed when transionosphere DSFH links are established in 2014. The measuring precision of TEC exceeds that obtained from GPS receiver data because the measurement is derived from unambiguous carrier phase estimates, not pseudorange estimates. The observation results from TT&C stations can provide real time regional ionosphere TEC estimation.

Frequency hopping spread spectrum signalling using code quadratic FSK technique for multichannel

In this paper, we have proposed and designed a code quadratic frequency shift keying technique based frequency hopping (CQFSK/FH) spread spectrum signaling for multichannels of wireless personal communication. The dependence of BER and bandwidth on hope size of the technique is shown. It is seen that the bandwidth of the proposed signaling scheme with four channels is five times less than that of existing code M-ary frequency shift keying based frequency hopping spread spectrum (CMFSK/FH) system of same number channels with almost same BER of 7 × 10 −4 at optimum hope size of 0.5 MHz.

Acoustic Vector-Sensor FFH "Blind" Beamforming & Geolocation

An acoustic direction-finding and blind interference rejection algorithm for up to three noncooperative wideband, fast frequency-hop (FFH) spread spectrum signals of unknown hop sequences and unknown arrival angles is presented. This algorithm avoids the need to adjust beamforming weights to accommodate frequency variations in the frequency-hopping signals. This approach deploys one acoustic vector-sensor (a.k.a. vector-hydrophone), which is composed of two or three spatially collocated, but orthogonally oriented, acoustic velocity-sensors plus an optional collocated pressure-sensor. The spatial collocation of all component sensors comprising such an acoustic vector-sensor frees the beamforming weights of any dependence on the signals' hopping frequencies.

Multi channel frequency hopping spread spectrum signaling using code M-ary frequency shift keying

In this paper we have proposed code M-ary frequency shift keying technique based frequency hopping (CMFSK/FH) spread spectrum signaling for multi channel. In this technique, taking one bit from each channel, we have constructed data word (called as symbol) for CMFSK system and corresponding to each state of data word, the transmitter generates a frequency, which is selected and amplified for transmission. The received signal in the receiver is tuned to each transmitted frequency and decoded for separation of channels. Here, we have developed simulation model for bit error rate (BER) performance analysis of CMFSK/FH system and compared the same with analytical results of BER. It is seen that simulation model is matched closely with analytical result. The BER performance for CMFSK/FH is better than that of existing FH system. It is also seen that the BER decreases with increase of frequency hop size.

WIRELESS LOCAL AREA NETWORK PERFORMANCE IN A BLUETOOTH INTERFERENCE ENVIRONMENT

Since IEEE 802.11 wireless local area networks (WLANs) and Bluetooth (BT) personal area networks both use the 2.4 GHz Industrial, Scientific, and Medical band, interference can occur when these networks are collocated. A WLAN station's clear channel assessment algorithm will declare a channel busy and induce transmission delays when sufficient energy from a BT signal is received. By assuming the BT's frequency hopped spread spectrum signal will not corrupt the direct sequence spread spectrum WLAN transmission, we isolate and characterize transmission delays due solely to BT interference. We develop expressions for throughput, delay, expected backoff interval, expected number of collisions, and other metrics of interest. In situations where numerous collisions occur between WLAN stations, BT-induced transmission delays reduce the probability of expensive WLAN collisions while increasing overall throughput and decreasing delay. An analytic model is developed to predict interference effects and is verified t...

Blind high-resolution localization and tracking of multiple frequency hopped signals

This paper considers the problem of blind localization and tracking of multiple frequency-hopped spread-spectrum signals using a uniform linear antenna array without knowledge of hopping patterns or directions of arrival. As a preprocessing step, we propose to identify a hop-free subset of data by discarding high-entropy spectral slices from the spectrogram. High-resolution localization is then achieved via either quadrilinear regression of four-way data generated by capitalizing on both spatial and temporal shift invariance or a new maximum likelihood (ML)-based two-dimensional (2-D) harmonic retrieval algorithm. The latter option achieves the best-known model identifiability bound while remaining close to the Cramer-Rao bound even at low signal-to-noise ratios (SNRs). Following beamforming using the recovered directions, a dynamic programming approach is developed for joint ML estimation of signal frequencies and hop instants in single-user tracking. The efficacy of the proposed algorithms is illustrated in pertinent simulations.