Micro-Doppler Estimation and Analysis of Slow Moving Objects in Forward Scattering Radar System

Abdullah, Rusli; Alnaeb, Ali; Salah, Asem A.; Rashid, Nur Emileen Abdul; Sali, Aduwati; Pasya, Idnin

doi:10.3390/rs9070699

Cited by 15 publications

(12 citation statements)

References 18 publications

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“…The cross-term S (c) (36) represents the undesired spectral interference term consisting of M(M + 1)/2 components which reduce the resolution of the spectrogram. This term is real but not necessarily positive.…”

Section: Spectrogram Analysismentioning

confidence: 99%

“…The micro-Doppler effect of pendulum motion in bistatic and monostatic radar systems has been investigated in [36,37]. In [36], the micro-Doppler effect was analyzed by means of the one-sided spectrogram of both simulated radar signal and verified with the experiment. According to the best of the authors' knowledge, there are no studies on the micro-Doppler effect of a swinging pendulum on measured calibrated CSI with B2B connection.…”

Section: Introductionmentioning

confidence: 99%

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Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments

Abdelgawwad

Borhani

Pätzold

2020

Sensors

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This paper is about designing a 3D no n-stationary wideband indoor channel model for radio-frequency sensing. The proposed channel model allows for simulating the time-variant (TV) characteristics of the received signal of indoor channel in the presence of a moving object. The moving object is modelled by a point scatterer which travels along a trajectory. The trajectory is described by the object’s TV speed, TV horizontal angle of motion, and TV vertical angle of motion. An expression of the TV Doppler frequency caused by the moving scatterer is derived. Furthermore, an expression of the TV complex channel transfer function (CTF) of the received signal is provided, which accounts for the influence of a moving object and fixed objects, such as walls, ceiling, and furniture. An approximate analytical solution of the spectrogram of the CTF is derived. The proposed channel model is confirmed by measurements obtained from a pendulum experiment. In the pendulum experiment, the trajectory of the pendulum has been measured by using an inertial-measurement unit (IMU) and simultaneously collecting CSI data. For validation, we have compared the spectrogram of the proposed channel model fed with IMU data with the spectrogram characteristics of the measured CSI data. The proposed channel model paves the way towards designing simulation-based activity recognition systems.

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Section: Spectrogram Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments

Abdelgawwad

Borhani

Pätzold

2020

Sensors

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“…In recent years, many micro-Doppler parameter estimation methods [19][20][21] were designed to tackle the aircraft target classification problem in narrow-band radar. Two techniques of cubic polynomial fitting and three-point models are developed to estimate the micro-Doppler parameters from a fraction of the period in real-world scenarios [22].…”

Section: Introductionmentioning

confidence: 99%

Aircraft Target Classification for Conventional Narrow-Band Radar with Multi-Wave Gates Sparse Echo Data

Wang¹,

Tang²,

Chen³

et al. 2019

Remote Sensing

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For a conventional narrow-band radar system, the detectable information of the target is limited, and it is difficult for the radar to accurately identify the target type. In particular, the classification probability will further decrease when part of the echo data is missed. By extracting the target features in time and frequency domains from multi-wave gates sparse echo data, this paper presents a classification algorithm in conventional narrow-band radar to identify three different types of aircraft target, i.e., helicopter, propeller and jet. Firstly, the classical sparse reconstruction algorithm is utilized to reconstruct the target frequency spectrum with single-wave gate sparse echo data. Then, the micro-Doppler effect caused by rotating parts of different targets is analyzed, and the micro-Doppler based features, such as amplitude deviation coefficient, time domain waveform entropy and frequency domain waveform entropy, are extracted from reconstructed echo data to identify targets. Thirdly, the target features extracted from multi-wave gates reconstructed echo data are weighted and fused to improve the accuracy of classification. Finally, the fused feature vectors are fed into a support vector machine (SVM) model for classification. By contrast with the conventional algorithm of aircraft target classification, the proposed algorithm can effectively process sparse echo data and achieve higher classification probability via weighted features fusion of multi-wave gates echo data. The experiments on synthetic data are carried out to validate the effectiveness of the proposed algorithm.

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“…In [24], a comprehensive review of micro-Doppler signatures based on different kinds of targets together with its importance and applications, was given. In [25], micro-Doppler signature generated from a target's micro-motions has been extracted using Forward Scattering Radar. But the targets classification based on Micro-Doppler signatures is still in current research.…”

Section: Introductionmentioning

confidence: 99%

Classification of Ground Targets Based on Radar Micro-Doppler Signatures Using Deep Learning and Conventional Supervised Learning Methods

Cao¹,

Xia²,

Li³

2018

RADIOENGINEERING

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Radar has great potential in military and civilian areas, including automobile anti-collision, battlefield surveillance, etc., due to its high penetration and allweather capability. On the basis of traditional targets detection, targets classification can be realized. In this paper, a comparison of targets classification between deep learning (Deep Convolutional Neural Networks (DCNNs)) and conventional supervised learning methods (Support Vector Machine (SVM), Naive Bayes (NB) and SVM-Bayes fusion algorithm) has been made. Furthermore, several factors affecting the accuracy of classifying targets including SNR, decrease of samples, have been researched and discussed. We employ a K-band Doppler radar to acquire the raw signal due to its stationary clutter-rejection, movement detection ability and short wavelength. Then Shorttime Fourier Transform (STFT) is applied to the raw signal to characterize micro-Doppler signatures which is the fundament of the classification process. We adopt the DCNNs to deal with the spectrograms directly, while features have been designed and extracted for classification with conventional supervised learning methods. It is shown that the DCNN can achieve average accuracy approximately 99.4% followed by SVM-Bayes fusion algorithm reaching around 95.8%, while the accuracy for SVM and NB is about 94.4% and 91% respectively.

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Micro-Doppler Estimation and Analysis of Slow Moving Objects in Forward Scattering Radar System

Cited by 15 publications

References 18 publications

Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments

Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments

Aircraft Target Classification for Conventional Narrow-Band Radar with Multi-Wave Gates Sparse Echo Data

Classification of Ground Targets Based on Radar Micro-Doppler Signatures Using Deep Learning and Conventional Supervised Learning Methods

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