scholarly journals Focusing High-Resolution Airborne SAR with Topography Variations Using an Extended BPA Based on a Time/Frequency Rotation Principle

2018 ◽  
Vol 10 (8) ◽  
pp. 1275 ◽  
Author(s):  
Chunhui Lin ◽  
Shiyang Tang ◽  
Linrang Zhang ◽  
Ping Guo
Keyword(s):  
High Resolution ◽  
Time Domain ◽  
Phase Error ◽  
Projection Algorithm ◽  
Geometric Distortion ◽  
Back Projection ◽  
Time Frequency ◽  
Phase Errors ◽  
Airborne Sar ◽  
The Time Domain

With the increasing requirement for resolution, the negligence of topography variations causes serious phase errors, which leads to the degradation of the focusing quality of the synthetic aperture (SAR) imagery, and geometric distortion. Hence, a precise and fast algorithm is necessary for high-resolution airborne SAR. In this paper, an extended back-projection (EBP) algorithm is proposed to compensate the phase errors caused by topography variations. Three-dimensional (3D) variation will be processed in the time-domain for high-resolution airborne SAR. Firstly, the quadratic phase error (QPE) brought by topography variations is analyzed in detail for high-resolution airborne SAR. Then, the key operation, a time-frequency rotation operation, is applied to decrease the samplings in the azimuth time-domain. Just like the time-frequency rotation of the conventional two-step approach, this key operation can compress data in an azimuth time-domain and it reduces the computational burden of the conventional back-projection algorithm, which is applied lastly in the time-domain processing. The results of the simulations validate that the proposed algorithm, including frequency-domain processing and time-domain processing can obtain good focusing performance. At the same time, it has strong practicability with a small amount of computation, compared with the conventional algorithm.

scholarly journals Processing Missile-Borne SAR Data by Using Cartesian Factorized Back Projection Algorithm Integrated with Data-Driven Motion Compensation

2021 ◽  
Vol 13 (8) ◽  
pp. 1462
Author(s):  
Min Bao ◽  
Song Zhou ◽  
Mengdao Xing
Keyword(s):  
Time Domain ◽  
Motion Compensation ◽  
Phase Error ◽  
Data Driven ◽  
Projection Algorithm ◽  
Compressed Domain ◽  
Fast Time ◽  
Back Projection ◽  
Motion Error ◽  
Efficient Data

Due to the independence of azimuth-invariant assumption of an echo signal, time-domain algorithms have significant performance advantages for missile-borne synthetic aperture radar (SAR) focusing with curve moving trajectory. The Cartesian factorized back projection (CFBP) algorithm is a newly proposed fast time-domain implementation which can avoid massive interpolations to improve the computational efficiency. However, it is difficult to combine effective and efficient data-driven motion compensation (MOCO) for achieving high focusing performance. In this paper, a new data-driven MOCO algorithm is developed under the CFBP framework to deal with the motion error problem for missile-borne SAR application. In the algorithm, spectrum compression is implemented after a CFBP process, and the SAR images are transformed into the spectrum-compressed domain. Then, the analytical image spectrum is obtained by utilizing wavenumber decomposition based on which the property of motion induced error is carefully investigated. With the analytical image spectrum, it is revealed that the echoes from different scattering points are aligned in the same spectrum range and the phase error becomes a spatial invariant component after spectrum compression. Based on the spectrum-compressed domain, an effective and efficient data-driven MOCO algorithm is accordingly developed for accurate error estimation and compensation. Both simulations of missile-borne SAR and raw data experiment from maneuvering highly-squint airborne SAR are provided and analyzed, which show high focusing performance of the proposed algorithm.

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

scholarly journals A research on fiber-optic vibration pattern recognition based on time-frequency characteristics

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881346 ◽  
Author(s):  
Tabi Fouda Bernard Marie ◽  
Dezhi Han ◽  
Bowen An ◽  
Jingyun Li
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Time Domain ◽  
Fiber Optic ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Vibration Pattern ◽  
The Time Domain

To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.

scholarly journals Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2

2021 ◽  
Vol 12 ◽  
Author(s):  
Hua Zhang ◽  
Chengyu Liu ◽  
Zhimin Zhang ◽  
Yujie Xing ◽  
Xinwen Liu ◽  
...  
Keyword(s):  
Cardiac Arrhythmia ◽  
Time Domain ◽  
Original Data ◽  
Classification Problem ◽  
Recurrence Plot ◽  
Two Dimensions ◽  
Time Frequency ◽  
Ecg Signals ◽  
Physiological Signal ◽  
The Time Domain

The present study addresses the cardiac arrhythmia (CA) classification problem using the deep learning (DL)-based method for electrocardiography (ECG) data analysis. Recently, various DL techniques have been utilized to classify arrhythmias, with one typical approach to developing a one-dimensional (1D) convolutional neural network (CNN) model to handle the ECG signals in the time domain. Although the CA classification in the time domain is very prevalent, current methods’ performances are still not robust or satisfactory. This study aims to develop a solution for CA classification in two dimensions by introducing the recurrence plot (RP) combined with an Inception-ResNet-v2 network. The proposed method for nine types of CA classification was tested on the 1st China Physiological Signal Challenge 2018 dataset. During implementation, the optimal leads (lead II and lead aVR) were selected, and then 1D ECG segments were transformed into 2D texture images by the RP approach. These RP-based images as input signals were passed into the Inception-ResNet-v2 for CA classification. In the CPSC, Georgia, and the PTB_XL ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the RP-based method achieved an average F1-score of 0.8521, 0.8529, and 0.8862, respectively. The results suggested the excellent generalization ability of the proposed method. To further assess the performance of the proposed method, we compared the 2D RP-image-based solution with the published 1D ECG-based works on the same dataset. Also, it was compared with two traditional ECG transform into 2D image methods, including the time waveform of the ECG recordings and time-frequency images based on continuous wavelet transform (CWT). The proposed method achieved the highest average F1-score of 0.844, with only two leads of the 12-lead ECG original data, which outperformed other works. Therefore, the promising results indicate that the 2D RP-based method has a high clinical potential for CA classification using fewer lead ECG signals.

scholarly journals Orthogonal Time Sequency Multiplexing Modulation

2021 ◽  
Author(s):  
Tharaj Thaj ◽  
Emanuele Viterbo
Keyword(s):  
Time Domain ◽  
Orthogonal Frequency Division Multiplexing ◽  
High Performance ◽  
High Mobility ◽  
Low Complexity ◽  
Modulation Scheme ◽  
Frequency Space ◽  
Time Frequency ◽  
Single Carrier ◽  
The Time Domain

This paper proposes <i>orthogonal time sequency multiplexing</i> (OTSM), a novel single carrier modulation scheme based on the well known Walsh-Hadamard transform (WHT) combined with row-column interleaving, and zero padding (ZP) between blocks in the time-domain. The information symbols in OTSM are multiplexed in the delay and sequency domain using a cascade of time-division and Walsh-Hadamard (sequency) multiplexing. By using the WHT for transmission and reception, the modulation and demodulation steps do not require any complex multiplications. We then propose two low-complexity detectors: (i) a simpler non-iterative detector based on a single tap minimum mean square time-frequency domain equalizer and (ii) an iterative time-domain detector. We demonstrate, via numerical simulations, that the proposed modulation scheme offers high performance gains over orthogonal frequency division multiplexing (OFDM) and exhibits the same performance of orthogonal time frequency space (OTFS) modulation, but with lower complexity. In proposing OTSM, along with simple detection schemes, we offer the lowest complexity solution to achieving reliable communication in high mobility wireless channels, as compared to the available schemes published so far in the literature.

scholarly journals A SFTD Algorithm for Optimizing the Performance of the Readout Strategy of Residence Time Difference Fluxgate

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3985 ◽  
Author(s):  
Siyu Chen ◽  
Yanzhang Wang ◽  
Jun Lin
Keyword(s):  
Residence Time ◽  
Time Domain ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Transformation Method ◽  
Time Difference ◽  
Single Frequency ◽  
Time Frequency ◽  
Frequency Magnetic Field ◽  
The Time Domain

Residence time difference (RTD) fluxgate sensor is a potential device to measure the DC or low-frequency magnetic field in the time domain. Nevertheless, jitter noise and magnetic noise severely affect the detection result. A novel post-processing algorithm for jitter noise reduction of RTD fluxgate output strategy based on the single-frequency time difference (SFTD) method is proposed in this study to boost the performance of the RTD system. This algorithm extracts the signal that has a fixed frequency and preserves its time-domain information via a time–frequency transformation method. Thereby, the single-frequency signal without jitter noise, which still contains the ambient field information in its time difference, is yielded. Consequently, compared with the traditional comparator RTD method (CRTD), the stability of the RTD estimation (in other words, the signal-to-noise ratio of residence time difference) has been significantly boosted with sensitivity of 4.3 μs/nT. Furthermore, the experimental results reveal that the RTD fluxgate is comparable to harmonic fluxgate sensors, in terms of noise floor.

scholarly journals Preliminary comparative assessment of PM10 hourly measurement results from new monitoring stations type using stochastic and exploratory methodology and models

2018 ◽  
Vol 28 ◽  
pp. 01010 ◽  
Author(s):  
Piotr Oskar Czechowski ◽  
Tomasz Owczarek ◽  
Artur Badyda ◽  
Grzegorz Majewski ◽  
Mariusz Rogulski ◽  
...  
Keyword(s):  
Time Domain ◽  
Comparative Assessment ◽  
Reference Station ◽  
Portable Devices ◽  
Annual Series ◽  
Time Frequency ◽  
Measurement Results ◽  
Key Issues ◽  
The Time Domain ◽  
Series Measurement

The paper presents selected preliminary stage key issues proposed extended equivalence measurement results assessment for new portable devices - the comparability PM10 concentration results hourly series with reference station measurement results with statistical methods. In article presented new portable meters technical aspects. The emphasis was placed on the comparability the results using the stochastic and exploratory methods methodology concept. The concept is based on notice that results series simple comparability in the time domain is insufficient. The comparison of regularity should be done in three complementary fields of statistical modeling: time, frequency and space. The proposal is based on model’s results of five annual series measurement results new mobile devices and WIOS (Provincial Environmental Protection Inspectorate) reference station located in Nowy Sacz city. The obtained results indicate both the comparison methodology completeness and the high correspondence obtained new measurements results devices with reference.

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