Radar Signal Processing for Jointly Estimating Tracks and Micro-Doppler Signatures

Wagner, Thomas; Feger, Reinhard; Stelzer, Andreas

doi:10.1109/access.2017.2667720

Cited by 56 publications

(33 citation statements)

References 24 publications

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“…Then from this Doppler, the object's velocity and direction can be extracted, while from the micro-Doppler, the velocity of the object's parts is extracted. In effect, the object can be recognized based on these Doppler and micro-Doppler, which are represented as the object's signatures [24]. In this context, the received Doppler (MD) as a function of time which shows the variation of the object (object's parts) with the time, where, the m-D frequency spectrum consists pairs of harmonic spectral lines around the transmitted frequency, as [6];…”

Section: Radar Received Signal and Doppler Effectmentioning

confidence: 99%

Human, Robotics Close Motion Classification Based on the Micro Doppler Signature Using HSVD

Al-Mufraji¹,

Saeed²,

Jabbar³

et al. 2019

IJIES

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In many applications, in industry and laboratory, there is a robot with a human, in close motion, to satisfy the work requirement. Therefore, for safety and monitoring the quality, the classification between them is important. The robot is considered as a semi-rigid object because it has many parts in the half upper, while the lower half is rigid, and a human is a non-rigid object. This paper presents a practical result which investigates the classification between these two objects based on the micro-Doppler signatures. The object's data were acquired using an S-band 2.4 GHz radar. The improved Stockwell transform was used to analyze the radar received signal in the timefrequency domain and feature extraction. The singular value decomposition is used for data filtering based on the Hankel matrix (HSVD) while using the Temporal Radial Basis Function to satisfy the classification. The classification accuracies gained are; 96.4%-100% for semi-rigid, 92%-95% for non-rigid closed objects.

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Section: Radar Received Signal and Doppler Effectmentioning

confidence: 99%

Human, Robotics Close Motion Classification Based on the Micro Doppler Signature Using HSVD

Al-Mufraji¹,

Saeed²,

Jabbar³

et al. 2019

IJIES

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“…Modern radar has been regarded as one of the most effective means to detect LSTG, because of its advantages of long detection range, high resolution and all-day/all-weather operation ability [5], [6]. There have been many researches on radar tracking algorithms for single and multiple targets situations [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

Robust Tracking for Small Target Groups With Dynamic Outlines

Zhou

Wang

2020

IEEE Access

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Low altitude small target group has the special motion characteristics of orderly as a whole and flexibly as individuals. The performances of traditional target tracking algorithms (such as centroid tracking, geometric centre tracking, etc.) decrease obviously when the group outline changes. Hence in this paper, a robust tracking algorithm for target group with dynamic outline is studied. Aiming at the two key issues of group division and equivalent tracking point estimation, a robust group division algorithm, which can maintain the stability of group division results under any reference point selection, is firstly introduced. Then, on this basis, a new optimal tracking point estimation method is proposed by introducing the group structure into the likelihood function. The simulation results show that this proposed method can effectively improve the robustness of low altitude small target group tracking in the scene of group merging and group splitting. INDEX TERMS Dynamic outlines, equivalent point estimation, group division, group tracking.

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“…Additionally, radar is nowadays the dominant sensor used for autonomous driving as it combines high resolution in range, velocity and depth perception. This motivated the use of radar in vital tasks such as on-road pedestrians and cyclists recognition [8].…”

Section: Introductionmentioning

confidence: 99%

Towards Adversarial Denoising of Radar Micro-Doppler Signatures

Abdulatif

Armanious

Aziz

et al. 2019

2019 International Radar Conference (RADAR)

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Generative Adversarial Networks (GANs) are considered the state-of-the-art in the field of image generation. They learn the joint distribution of the training data and attempt to generate new data samples in high dimensional space following the same distribution as the input. Recent improvements in GANs opened the field to many other computer vision applications based on improving and changing the characteristics of the input image to follow some given training requirements. In this paper, we propose a novel technique for the denoising and reconstruction of the micro-Doppler (µ-D) spectra of walking humans based on GANs. Two sets of experiments were collected on 22 subjects walking on a treadmill at an intermediate velocity using a 25 GHz CW radar. In one set, a clean µ-D spectrum is collected for each subject by placing the radar at a close distance to the subject. In the other set, variations are introduced in the experiment setup to introduce different noise and clutter effects on the spectrum by changing the distance and placing reflective objects between the radar and the target. Synthetic paired noisy and noise-free spectra were used for training, while validation was carried out on the real noisy measured data. Finally, qualitative and quantitative comparison with other classical radar denoising approaches in the literature demonstrated the proposed GANs framework is better and more robust to different noise levels.

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Radar Signal Processing for Jointly Estimating Tracks and Micro-Doppler Signatures

Cited by 56 publications

References 24 publications

Human, Robotics Close Motion Classification Based on the Micro Doppler Signature Using HSVD

Human, Robotics Close Motion Classification Based on the Micro Doppler Signature Using HSVD

Robust Tracking for Small Target Groups With Dynamic Outlines

Towards Adversarial Denoising of Radar Micro-Doppler Signatures

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