Low-Rank Hankel Matrix Completion for Robust Time-Frequency Analysis

Zhang, Shuimei; Zhang, Yimin D.

doi:10.1109/tsp.2020.3032234

Cited by 21 publications

(7 citation statements)

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“…It is noted that the use of a long CPI may reduce the surveillance volume and frequency of revisits. However, a desirable frequency of revisits may be maintained by using multi-beam operation and time-frequency analysis for signals with missing samples [30]- [33]. The latter enables processing of discontinuous sensing data.…”

Section: A Doppler Signature Analysismentioning

confidence: 99%

Target Altitude Estimation in Over-the-Horizon Radar

2022

Self Cite

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Doppler signatures of local multipath signals provide useful information for target altitude estimation in over-the-horizon radar surveillance. In this paper, we develop a method to improve the resolution of these multipath Doppler signatures and enable enhanced altitude estimation of aircraft target which maintains a constant altitude. Moreover, we consider the impact of ionospheric layer motion on target parameter estimation and show that target parameters can be estimated under both stationary as well as time-varying ionospheric layer conditions. In order to improve the resolution and estimation accuracy of the target parameters and ionosphere velocity with a significantly reduced complexity, we exploit a frequency focused transform to the de-chirped target signals for dimension reduction before applying a least absolute shrinkage and selection operator (LASSO)-based high-resolution spectrum estimation technique. The proposed strategy outperforms fractional Fourier transform and classical subspace-based frequency estimation methods with a much lower computational complexity. The effectiveness of the proposed approach is especially evident for challenging cases where the multipath signal components have spectrally close Doppler signatures. Simulation results confirm the effectiveness of the proposed method.INDEX TERMS Doppler signature, dynamic ionosphere, fractional Fourier transform, over-the-horizon radar, target geo-location, time-frequency analysis.

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Section: A Doppler Signature Analysismentioning

confidence: 99%

Target Altitude Estimation in Over-the-Horizon Radar

2022

Self Cite

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“…Here,

and

are the received signal matrices of the tag, clutter and noise, respectively. Note that generating a Hankel matrix from a input vector of

is expressed as [ 33 ]

…”

Section: System Modelmentioning

confidence: 99%

“…Here, k is the split point and it is also known as the pencil parameter [ 33 ]. Further, the inverse Hankel operation denotes as

which takes a matrix as an input and then generates a vector.…”

Section: System Modelmentioning

confidence: 99%

Clutter Suppression for Indoor Self-Localization Systems by Iteratively Reweighted Low-Rank Plus Sparse Recovery

Sánchez-Pastor

Thanthrige

Ilgac

et al. 2021

Sensors

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Self-localization based on passive RFID-based has many potential applications. One of the main challenges it faces is the suppression of the reflected signals from unwanted objects (i.e., clutter). Typically, the clutter echoes are much stronger than the backscattered signals of the passive tag landmarks used in such scenarios. Therefore, successful tag detection can be very challenging. We consider two types of tags, namely low-Q and high-Q tags. The high-Q tag features a sparse frequency response, whereas the low-Q tag presents a broad frequency response. Further, the clutter usually showcases a short-lived response. In this work, we propose an iterative algorithm based on a low-rank plus sparse recovery approach (RPCA) to mitigate clutter and retrieve the landmark response. In addition to that, we compare the proposed approach with the well-known time-gating technique. It turns out that RPCA outperforms significantly time-gating for low-Q tags, achieving clutter suppression and tag identification when clutter encroaches on the time-gating window span, whereas it also increases the backscattered power at resonance by approximately 12 dB at 80 cm for high-Q tags. Altogether, RPCA seems a promising approach to improve the identification of passive indoor self-localization tag landmarks.

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“…It is necessary to deeply study the internal characteristics of radar signals. Researchers successively began to study and analyse the radar intra‐pulse signals [6], and extract the intra‐pulse features through time domain analysis, frequency domain analysis, and instantaneous autocorrelation [7]. Subsequently, machine learning methods were introduced.…”

Section: Introductionmentioning

confidence: 99%

Radar waveform recognition based on a two‐stream convolutional network and software defined radio

Xia

Huang

2022

IET Radar Sonar & Navi

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As one of the key technologies of electromagnetic spectrum operations, radar waveform recognition is an important basis for judging the threat degree of enemy’s weapons. At present, it is difficult to recognize the waveform at low signal‐to‐noise ratio (SNR), and the theoretical simulation is used in most research studies, which leads to the problem that the engineering practicality of the algorithm is poor. In this paper, a two‐stream convolutional network radar waveform recognition (TCNRWR) algorithm that fuses the original time–frequency image and the continuous frames autocorrelation time–frequency image is proposed, and an experimental platform is built by using software defined radio technology. Compared with traditional theoretical simulations, this paper focusses on the influence of spatial background interference and signal incompleteness on the recognition of intra‐pulse signals, and proposes an automatic removal method of invalid signal in the time–frequency domain. In order to further improve the recognition rate at low SNR, a deeper feature of short‐time autocorrelation flow is excavated, and a two‐stream time–frequency images fusion convolutional network (TTIFCNet) is constructed. The network effectively fuses the pre‐classification results of the two streams through Softmax layer and different weights. Finally, the hardware platform HackRF is used to realize the communication and processing of signals in real space, which proves the effectiveness of the TCNRWR algorithm.

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Low-Rank Hankel Matrix Completion for Robust Time-Frequency Analysis

Cited by 21 publications

References 50 publications

Target Altitude Estimation in Over-the-Horizon Radar

Target Altitude Estimation in Over-the-Horizon Radar

Clutter Suppression for Indoor Self-Localization Systems by Iteratively Reweighted Low-Rank Plus Sparse Recovery

Radar waveform recognition based on a two‐stream convolutional network and software defined radio

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