Segmented discrete polynomial‐phase transform with coprime sampling

Liu, Shengheng; Ma, Yahui; Shan, Tao

doi:10.1049/joe.2019.0312

Cited by 14 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such waveforms are called timevarying or nonstationary signals. They are encountered in various areas such as audio signals, synthetic aperture radar, and machinery [9,11,12]. Recent advances dealing with nonstationary signals mainly focus on the time-frequency analysis methods, since the signals are intrinsically sparse on the time-frequency plane.…”

Section: A Challenges For Nonstationary Signalsmentioning

confidence: 99%

See 1 more Smart Citation

Analog-to-Information Conversion for Nonstationary Signals

Wang¹,

Meng²,

Wang³

2020

IEEE Access

View full text Add to dashboard Cite

In this paper, we consider the problem of analog-to-information conversion for nonstationary signals, which exhibit time-varying properties with respect to spectral contents. Nowadays, sampling for nonstationary signals is mainly based on Nyquist sampling theorem or signal-dependent techniques. Unfortunately, in the context of the efficient ‗blind' sampling, these methods are infeasible. To deal with this problem, we propose a novel analog-to-information conversion architecture to achieve the sub-Nyquist sampling for nonstationary signals. With the proposed scheme, we present a multi-channel sampling system to sample the signals in time-frequency domain. We analyze the sampling process and establish the reconstruction model for recovering the original signals. To guarantee the wide application, we establish the completeness under the frame theory. Besides, we provide the feasible approach to simplify the system construction. The reconstruction error for the proposed system is analyzed. We show that, with the consideration of noises and mismatch, the total error is bounded. The effectiveness of the proposed system is verified in the numerical experiments. It is shown that our proposed scheme outperforms the other sampling methods state-of-the-art.

show abstract

Section: A Challenges For Nonstationary Signalsmentioning

confidence: 99%

“…Time-frequency analysis is an effective tool to characterize nonstationary signals [11][12][13][14][15]. It reflects the time-varying properties by mapping signals into the joint time-frequency domain.…”

Section: B Promlem Formulationmentioning

confidence: 99%

Analog-to-Information Conversion for Nonstationary Signals

Wang¹,

Meng²,

Wang³

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…However, two non-uniform subarrays are discarded to cause DOF loss in its application due to the holes. That is to say, the DOF provided by the coprime array is not fully utilized [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

STAP for Airborne Radar Based on Dual-Frequency Space-Time Coprime Sampling Structure

Liu¹,

Tang²,

Zheng³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

To overcome the space-time adaptive processing (STAP) performance loss caused by discarding the non-uniform parts of difference coarray and copulse for coprime sampling structure under the condition of single-frequency operation, this paper proposes a new STAP algorithm to improve the precision of filter weight vector estimation by the dual-frequency operation. By selecting a single proper additional operation frequency, we can obtain the different coarray and copulse, thus fill two missed virtual sensors and pulses of the difference structures in the single-frequency operation simultaneously. Compared with the single-frequency operation, the dual-frequency operation method can acquire the bigger uniform linear array (ULA) and coherent processing interval (CPI) pulse train to improve the system degrees of freedom (DOF), and result in higher angle-Doppler resolution. In addition, the coprime array has the little mutual coupling effect because of the larger inter-sensors spacing. Therefore, the resulting method can alleviate mutual coupling and enhance the system DOF. INDEX TERMSCoprime sampling structure, degrees of freedom, dual-frequency operation, space-time adaptive processing, mutual coupling.

show abstract

“…erefore, designing a sub-Nyquist sampling scheme which can effectively sense the power spectrum of nonstationary signals is a challenging task. Coprime theory [35,36], which is well suited for analyzing sparsely sampled signals in case that sampling rate is far lower than the Nyquist sampling rate, has gained increasing attention in recent years. Coprime theory can be well coupled with DFT filter banks theory in temporal domain to sense the power spectrum of WSS signals [35].…”

Section: Introductionmentioning

confidence: 99%

Sensing Fractional Power Spectrum of Nonstationary Signals with Coprime Filter Banks

Wang

et al. 2020

Complexity

View full text Add to dashboard Cite

The coprime discrete Fourier transform (DFT) filter banks provide an effective scheme of spectral sensing for wide-sense stationary (WSS) signals in case that the sampling rate is far lower than the Nyquist sampling rate. And the resolution of the coprime DFT filter banks in the Fourier domain (FD) is 2π/MN, where M and N are coprime. In this work, a digital fractional Fourier transform- (DFrFT-) based coprime filter banks spectrum sensing method is suggested. Our proposed method has the same sampling principle as the coprime DFT filter banks but has some advantages compared to the coprime DFT filter banks. Firstly, the fractional power spectrum of the chirp-stationary signals which are nonstationary in the FD can be sensed effectively by the coprime DFrFT filter banks because of the linear time-invariant (LTI) property of the proposed system in discrete-time Fourier domain (DTFD), while the coprime DFT filter banks can only sense the power spectrum of the WSS signals. Secondly, the coprime DFrFT filter banks improve the resolution from 2π/MN to 2π sin α/MN by combining the fractional filter banks theory with the coprime theory. Simulation results confirm the obtained analytical results.

show abstract

Segmented discrete polynomial‐phase transform with coprime sampling

Cited by 14 publications

References 11 publications

Analog-to-Information Conversion for Nonstationary Signals

Analog-to-Information Conversion for Nonstationary Signals

STAP for Airborne Radar Based on Dual-Frequency Space-Time Coprime Sampling Structure

Sensing Fractional Power Spectrum of Nonstationary Signals with Coprime Filter Banks

Contact Info

Product

Resources

About