Nyquist Folding Receiver Research Articles

Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver

Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver

Wideband Power Spectrum Sensing: A Fast Practical Solution for Nyquist Folding Receiver

Wideband Power Spectrum Sensing: A Fast Practical Solution for Nyquist Folding Receiver

Information Recovery for Signals Intercepted by Dual-Channel Nyquist Folding Receiver with Adjustable Local Oscillator

Conventional Nyquist folding receiver (NYFR) uses zero crossing rising (ZCR) voltage times to control the RF sample clock, which is easily affected by noise. Moreover, the analog and digital parts are not synchronized so that the initial phase of the input signal is lost. Furthermore, it is assumed in most literature that the input signal is in a single Nyquist zone (NZ), which is inconsistent with the actual situation. In this paper, we propose an improved architecture denominated as a dual-channel NYFR with adjustable local oscillator (LOS) and an information recovery algorithm. The simulation results demonstrate the validity and viability of the proposed architecture and the corresponding algorithm.

Wideband spectrum sensing using step-sampling based on the multi-path nyquist folding receiver

Wideband spectrum sensing with a high-speed analog-digital converter (ADC) presents a challenge for practical systems. The Nyquist folding receiver (NYFR) is a promising scheme for achieving cost-effective real-time spectrum sensing, which is subject to the complexity of processing the modulated outputs. In this case, a multipath NYFR architecture with a step-sampling rate for the different paths is proposed. The different numbers of digital channels for each path are designed based on the Chinese remainder theorem (CRT). Then, the detectable frequency range is divided into multiple frequency grids, and the Nyquist zone (NZ) of the input can be obtained by sensing these grids. Thus, high-precision parameter estimation is performed by utilizing the NYFR characteristics. Compared with the existing methods, the scheme proposed in this paper overcomes the challenge of NZ estimation, information damage, many computations, low accuracy, and high false alarm probability. Comparative simulation experiments verify the effectiveness of the proposed architecture in this paper.

Analysis of Sampling Aperture Impact on Nyquist Folding Receiver Output

Analysis of Sampling Aperture Impact on Nyquist Folding Receiver Output

Parameter Estimation of Multiple Mono-Pulse Radar Signals Intercepted by Nyquist Folding Receiver Using Periodic Chirp Local Oscillator Based on Periodic Fractional Autocorrelation

The frequency space of modern radar system has been extended to a wide range. To intercept the modern radar signals with high interception probability, the wideband receiving is of significance to the radar reconnaissance receiver. The Nyquist folding receiver (NYFR) is a novel ultra-wideband receiving architecture, which requires a small amount of equipment. Because the mono-pulse radar signal is widely used in radar systems, the multiple mono-pulse radar signals will be intercepted by the NYFR. The parameter estimation of multiple simultaneous arrival mono-pulse radar signals intercepted by the NYFR using periodic chirp local oscillator is considered. The definition of Periodic fractional autocorrelation (PFA) is proposed. Based on the PFA, a novel fast parameter estimation method is given. The proposed estimation approach can estimate the multiple NYFR outputs under frequency aliasing condition and the computational complexity of the proposed method is low. Simulation results show the effectiveness of the proposed method.

Deep learning-based LPI radar signals analysis and identification using a Nyquist Folding Receiver architecture

Nyquist Folding Receiver (NYFR) is a perceptron structure that realizes a low probability of intercept (LPI) signal analog to information. Aiming at the problem of LPI radar signal receiving, the time domain, frequency domain, and time-frequency domain problems of signals intercepted by NYFR structure are studied. Combined with the time-frequency analysis (TFA) method, a radar recognition scheme based on deep learning (DL) is introduced, which can reliably classify common LPI radar signals. First, the structure of NYFR and its characteristics in the time domain, frequency domain, and time and frequency domain are analyzed. Then, the received signal is then converted into a time-frequency image (TFI). Finally, four kinds of DL algorithms are used to classify LPI radar signals. Simulation results demonstrate the correctness of the NYFR structure, and the effectiveness of the proposed recognition method is verified by comparison experiments.

Parameter estimation of frequency shift keying radar signal intercepted by Nyquist folding receiver using periodic linear frequency modulation local oscillator

Parameter estimation of frequency shift keying radar signal intercepted by Nyquist folding receiver using periodic linear frequency modulation local oscillator

Parameter Estimation for Multiple Chirp Signals Based on Single Channel Nyquist Folding Receiver

Parameter Estimation for Multiple Chirp Signals Based on Single Channel Nyquist Folding Receiver

Nyquist Zone Index and Chirp Rate Estimation of LFM Signal Intercepted by Nyquist Folding Receiver Based on Random Sample Consensus and Fractional Fourier Transform.

The Nyquist folding receiver (NYFR) can achieve a high-probability interception of an ultra-wideband (UWB) signal with fewer devices, while the output of the NYFR is converted into a hybrid modulated signal of the local oscillator (LO) and the received signal, which requires the matching parameter estimation methods. The linear frequency modulation (LFM) signal is a typical low probability of intercept (LPI) radar signal. In this paper, an estimation method of both the Nyquist Zone (NZ) index and the chirp rate for the LFM signal intercepted by NYFR was proposed. First, according to the time-frequency characteristics of the LFM signal, the accurate NZ and the rough chirp rate was estimated based on least squares (LS) and random sample consensus (RANSAC). Then, the information of the LO was removed from the hybrid modulated signal by the known NZ, and the precise chirp rate was obtained by using the fractional Fourier transform (FrFT). Moreover, a fast search method of FrFT optimal order was presented, which could obviously reduce the computational complexity. The simulation demonstrated that the proposed method could precisely estimate the parameters of the hybrid modulated output signal of the NYFR.

Key Parameter Estimation for Pulse Radar Signal Intercepted by Non-Cooperative Nyquist Folding Receiver

Key Parameter Estimation for Pulse Radar Signal Intercepted by Non-Cooperative Nyquist Folding Receiver

Parameter Estimation of SAR Signal Based on SVD for the Nyquist Folding Receiver.

The Nyquist Folding Receiver (NYFR) is a novel ultra-wideband (UWB) receiver structure that can realize wideband signal monitoring with fewer components. The NYFR induces a Nyquist zone (NZ)-dependent sinusoidal frequency modulation (SFM) by a modulated local oscillator (LOS), and the intercepted linear frequency modulated (LFM) synthetic aperture radar (SAR) signal will be converted into an LFM/SFM hybrid modulated signal. In this paper, a parameter estimation algorithm is proposed for the complicated NYFR output signal. According to the NYFR prior information, a chirp singular value ratio (CSVR) spectrum method based on singular value decomposition (SVD) is proposed to estimate the chirp rate directly before estimating the NZ index. Then, a fast search algorithm based on golden section method for the CSVR spectrum is analyzed, which can obviously reduce the computational complexity. The simulation shows that the presented algorithm can accurately estimate the parameters of the LFM/SFM hybrid modulated output signal by the NYFR.

Parameter estimation of LFM signal intercepted by improved dual‐channel Nyquist folding receiver

A Nyquist folding receiver (NYFR) is a novel ultra-wideband (UWB) receiver architecture, which can realise a wideband receiving with fewer components. Intercepted by the NYFR with a sinusoidal frequency modulation (SFM) local oscillator, a linear frequency modulation (LFM) signal will be converted into a LFM/SFM hybrid modulated signal. To simplify the processing of this kind of complicated signal, the authors propose improved dual-channel NYFR architecture with an efficient parameter estimation algorithm. Compared with the existing parameter estimation methods for the NYFR, the proposed architecture offers better estimation performance, higher signal type adaptability, and simpler operation.

Multiple Signals Estimation for Overlapping Nyquist Folding Receiver

Multiple Signals Estimation for Overlapping Nyquist Folding Receiver

Estimation of both Nyquist zone index and code rate for BPSK radar signal intercepted by Nyquist folding receiver

Nyquist folding receiver (NYFR) can realize the wideband receiving with a small amount of equipment. As a tradeoff between the amount of equipment and the complexity of the signal processing, the NYFR output is usually a hybrid modulated signal. Therefore, it is necessary to investigate the parameter estimation method of the signal intercepted by the NYFR. Binary phase shift keying (BPSK) signal is a typical low probability of intercept radar signal and the authors focus on the parameter estimation of the BPSK radar signal intercepted by the NYFR. Firstly, matching component function is proposed to obtain Nyquist zone (NZ) index. Then, the Hankel-singular value decomposition (SVD) method is introduced to estimate the code rate of the BPSK signal. The efficacy of the Hankel-SVD method is proved in a mathematical way. Several numerical simulations demonstrate the merits of the proposed approach and finally, a brief investigation for case of the multiple BPSK signals intercepted by the NYFR is given. For NZ index estimation, the computational complexity of the matching component function is low and it has no need to use squaring process. In addition, the code rate estimation using the Hankel-SVD method is more accurate compared with the wavelet transform.

Spectral reconstruction of signals from periodic nonuniform subsampling based on a Nyquist folding scheme

Periodic nonuniform sampling occurs in many applications, and the Nyquist folding receiver (NYFR) is an efficient, low complexity, and broadband spectrum sensing architecture. In this paper, we first derive that the radio frequency (RF) sample clock function of NYFR is periodic nonuniform. Then, the classical results of periodic nonuniform sampling are applied to NYFR. We extend the spectral reconstruction algorithm of time series decomposed model to the subsampling case by using the spectrum characteristics of NYFR. The subsampling case is common for broadband spectrum surveillance. Finally, we take example for a LFM signal under large bandwidth to verify the proposed algorithm and compare the spectral reconstruction algorithm with orthogonal matching pursuit (OMP) algorithm.

NYFR output pulse radar signal TOA analysis using extended Fourier transform and its TOA estimation

Nyquist folding receiver (NYFR) is a typical wideband analog-to-information architecture. Focusing on the non-cooperative receiving, the pulse radar signal intercepted by the NYFR in time domain is analyzed. The NYFR outputs under different input conditions are investigated based on the extended Fourier transform (EFT) and the sampling theorem. Combining with the characteristic of the NYFR output in time domain, a new time of arrival (TOA) estimation method based on the energy envelope and the wavelet transform is proposed. The proposed estimation method can be adapted for the non-cooperative situation. It has no requirement for prior information to determine the threshold and is not necessary to transform the signal into baseband. Simulation results prove the correctness of the NYFR output expressions and show the efficacy of the proposed estimation method.

The RIP and block-RIP analysis of Nyquist folding receiver for recovering signals.

Modern radar and communication systems require the detection and parameter estimation of signal under a broadband radio frequency (RF) environment. The Nyquist folding receiver (NYFR) is an efficient analog-to-information (A2I) architecture. It can use the compressive sensing (CS) techniques to break the limitations of the analog-to-digital converter (ADC). This paper demonstrates the restricted isometry property (RIP) of the NYFR deterministically by applying the Gershgorin circle theory. And, the NYFR suffers a poor RIP for the broadband signal, which will lead the conventional CS algorithms to be invalid. So, we derive the Fourier spectrum of the broadband signal, which covered multiple Nyquist zones and received by the NYFR. Then, the broadband signal can be regarded as the block-sparse signal. And, the block CS algorithms are applied for recovering the signal based on the analysis of the block-RIP. Finally, the simulation experiments demonstrate the validity of the findings.

A parameter estimation algorithm for LFM/BPSK hybrid modulated signal intercepted by Nyquist folding receiver.

Nyquist folding receiver (NYFR) is a novel ultra-wideband receiver architecture which can realize wideband receiving with a small amount of equipment. Linear frequency modulated/binary phase shift keying (LFM/BPSK) hybrid modulated signal is a novel kind of low probability interception signal with wide bandwidth. The NYFR is an effective architecture to intercept the LFM/BPSK signal and the LFM/BPSK signal intercepted by the NYFR will add the local oscillator modulation. A parameter estimation algorithm for the NYFR output signal is proposed. According to the NYFR prior information, the chirp singular value ratio spectrum is proposed to estimate the chirp rate. Then, based on the output self-characteristic, matching component function is designed to estimate Nyquist zone (NZ) index. Finally, matching code and subspace method are employed to estimate the phase change points and code length. Compared with the existing methods, the proposed algorithm has a better performance. It also has no need to construct a multi-channel structure, which means the computational complexity for the NZ index estimation is small. The simulation results demonstrate the efficacy of the proposed algorithm.

Frequency Estimation Using SAMP-SVD Based on Nyquist Folding Receiver

The Nyquist Folding Receiver (NYFR) is an efficient analogy-to-information (A2I) architecture that folds the broadband RF inputs by subsampling so that can sample with a low-speed ADC. The compressive sensing (CS) model of the NYFR can be built in the sparse environment to recover by traditional CS algorithms. This paper presents an improved algorithm SAMP-SVD based on sparsity adaptive matching pursuit (SAMP) and singular value decomposition (SVD) which is intended to deal with the problem caused by SAMP recovering—the inversion of matrix, avoiding the problem of whether matrix is singular and reducing the time of computation. A comparative analysis between the presented method and sparse reconstruction by separable approximation (SpaRSA) is discussed; simulation results show the algorithm can reconstruct the signal effectively and estimate the frequencies accurately.