Vehicles Detection in Complex Urban Scenes Using Gaussian Mixture Model With FMCW Radar

Zhao, Yichao; Su, Yi

doi:10.1109/jsen.2017.2733223

Cited by 29 publications

(13 citation statements)

References 19 publications

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“…In recent studies, two or more different parameters of the waveform are combined to achieve better detection results (Li and O’Young, 2015; Zhao and Su, 2017; Lulu and Mobasseri, 2017). Li and O’Young (2015) proposes an inverted triangle chirp waveform and processing method that doubled the radar’s range resolution without increasing the bandwidth.…”

Section: Introductionmentioning

confidence: 99%

A flexible, efficient and low-cost experimental platform for FMCW radars

Wang

et al. 2019

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Purpose This paper proposes a design of an efficient and automated experimental platform for frequency modulated continuous wave (FMCW) radars. The platform can quickly flexibly generate the waveform that meets measurement requirements and significantly improve experimental efficiency. Design/methodology/approach This platform not only includes radio frequency devices but also integrates a programmable transmitter based on field programmable gate array. By configuring the waveform data, the experimental platform can generate waveforms with adjustable parameters and realize automatic emission, reception and processing of signals. Different from traditional fast Fourier transform, this paper uses a discrete-time Fourier transform to process low-frequency signals to get more accurate results. Findings The authors demonstrate the effectiveness of the platform through a single-path cable experiment, an indoor ranging experiment by using different modulating waveforms and a speed measurement experiment. With complete functions and strong flexibility, the platform can operate effectively in various conditions and greatly improve the efficiency of research and study. Practical implications The platform can accelerate the research studies and applications of FMCW radars in the fields of automatic drive, through-wall detection and health-care applications. Originality/value Cost and functionality are taken into account in the platform, which can significantly improve the efficiency of research. The proposed signal processing method improves the accuracy while its computation complexity does not increase significantly.

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Section: Introductionmentioning

confidence: 99%

A flexible, efficient and low-cost experimental platform for FMCW radars

Wang

et al. 2019

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“…In this regard, if the radar transmits a continuous wave with a carrier frequency f 0 [17], the baseband of the returned signal is the summation of Doppler signal S D ( t ), m‐D signal S m ( t ) and noise η ( t ), given as:

\begin{array}{l} S (t) = S_{D} (t) + S_{m} (t) + η (t) \\ S_{D} (t) = σ_{1} K \exp (V (t) + ς) \\ S_{m} (t) = \sum_{k = 1}^{l} σ_{k} K \exp (j (A_{k} \sin (2 π f_{k} t + φ_{k}) + V (t))) \\ K = \exp (- j \frac{4 π f_{0}}{c} R_{0}), V (t) = - \frac{4 π f_{0}}{c} v t \end{array}

…”

Section: Signal Modelmentioning

confidence: 99%

Intrinsic synchrosqueezing analysis on micro‐Doppler features for small unmanned aerial vehicles identification with dual‐channel radar

Zhao

2021

IET Microwaves Antenna & Prop

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The micro-Doppler (m-D) effect depends on the rotation of rotor blades in addition to the translation of the platform. Thus it is a characteristic for identifying small unmanned aerial vehicles (UAVs). However, compared with the Doppler signal induced by the translation of the platform, the m-D signal is weak. In this article, a highly localised dataassociation method, intrinsic synchrosqueezing analysis (ISA), is proposed for estimating m-D characteristics from the returned signal of small UAVs with a dual-channel radar. Employing synchrosqueezing transform on intrinsic mode functions derived from noiseassisted multivariate empirical mode decomposition, the proposed ISA method separates the Doppler signal and enables denoising and sharpening time-frequency representation of the m-D signal. Simulation results confirm the theoretical analysis, showing the feasibility of estimating m-D features in a noisy environment. Applications on field data illustrate brighter prospects for identifying small UAVs.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Collision warning systems are one of the major applications of ADAS and their task is to inform drivers of obstacles around the vehicle by giving visual, aural, or tactile feedback [3,4]. Reliable moving object detection (MOD) technology is an essential part of collision warning systems and various sensor-based techniques have been proposed, such as vision-, lidar- and radar-based techniques [5,6]. Since vision-based MOD technology is relatively more intuitive and cheaper than active sensor techniques, such as radar and lidar, many vision-based algorithms have been proposed [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29].…”

Section: Introductionmentioning

confidence: 99%

Moving Object Detection Based on Optical Flow Estimation and a Gaussian Mixture Model for Advanced Driver Assistance Systems

Cho

Jung

Kim

et al. 2019

Sensors

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Most approaches for moving object detection (MOD) based on computer vision are limited to stationary camera environments. In advanced driver assistance systems (ADAS), however, ego-motion is added to image frames owing to the use of a moving camera. This results in mixed motion in the image frames and makes it difficult to classify target objects and background. In this paper, we propose an efficient MOD algorithm that can cope with moving camera environments. In addition, we present a hardware design and implementation results for the real-time processing of the proposed algorithm. The proposed moving object detector was designed using hardware description language (HDL) and its real-time performance was evaluated using an FPGA based test system. Experimental results demonstrate that our design achieves better detection performance than existing MOD systems. The proposed moving object detector was implemented with 13.2K logic slices, 104 DSP48s, and 163 BRAM and can support real-time processing of 30 fps at an operating frequency of 200 MHz.

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Vehicles Detection in Complex Urban Scenes Using Gaussian Mixture Model With FMCW Radar

Cited by 29 publications

References 19 publications

A flexible, efficient and low-cost experimental platform for FMCW radars

A flexible, efficient and low-cost experimental platform for FMCW radars

Intrinsic synchrosqueezing analysis on micro‐Doppler features for small unmanned aerial vehicles identification with dual‐channel radar

Moving Object Detection Based on Optical Flow Estimation and a Gaussian Mixture Model for Advanced Driver Assistance Systems

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