Instantaneous full-motion estimation of arbitrary objects using dual Doppler radar

Kellner, Dominik; Barjenbruch, Michael; Klappstein, Jens; Dickmann, Jürgen; Dietmayer, Klaus

doi:10.1109/ivs.2014.6856449

Cited by 40 publications

(25 citation statements)

References 15 publications

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“…The radar sensor is modeled with a constant number of measurements on the OV, which are uniformly distributed over the azimuth angle. The influences of radar specific parameters on the radar-only-method are analyzed in detail in [4]. In Table I the standard parameter set is shown.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…A simple Monte-Carlo simulation determines the uncertainties by sampling from the distributions of the geometric parameters and the measurements with the specified measurement error. An improvement can be achieved by combining the augmented-method and the radar-only-method [4] (Eq. (4)).…”

Section: Motion Estimationmentioning

confidence: 99%

“…In contrast, the recently developed algorithm [4] allows an instantaneous estimation of the yaw rate as well as the velocity components of an observed vehicle (OV), utilizing two modern Doppler radar sensors. This approach is based on a special measurement model that describes the measured Doppler velocities dependent on the vehicle's motion state.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fusion of doppler radar and geometric attributes for motion estimation of extended objects

Brosseit

Kellner

Brenk

et al. 2015

2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF)

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A prime requirement for autonomous driving is a fast and reliable estimation of the motion state of dynamic objects in the ego-vehicle's surroundings. An instantaneous approach for extended objects based on two Doppler radar sensors has recently been proposed. In this paper, that approach is augmented by prior knowledge of the object's heading angle and rotation center. These properties can be determined reliably by state-ofthe-art methods based on sensors such as LIDAR or cameras. The information fusion is performed utilizing an appropriate measurement model, which directly maps the motion state in the Doppler velocity space. This model integrates the geometric properties. It is used to estimate the object's motion state using a linear regression. Additionally, the model allows a straightforward calculation of the corresponding variances. The resulting method shows a promising accuracy increase of up to eight times greater than the original approach.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Motion Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fusion of doppler radar and geometric attributes for motion estimation of extended objects

Brosseit

Kellner

Brenk

et al. 2015

2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF)

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“…Derivation shows that independent of the motion state of the vehicle with 3 degrees of freedom (DOF) the Doppler velocities always form a cosine over θ with 2 DOF (amplitude and phase shift) -called the velocity profile [1].…”

Section: Theorymentioning

confidence: 99%

“…To detect and react to critical situations, it is important to precisely capture other traffic participants. Their motion state is resolved model-free by analyzing their velocity profile (Doppler velocity over azimuth angle) [1]. The position of detected wheels can be used for classification and as a representation of the spatial position, extension and orientation.…”

Section: Introductionmentioning

confidence: 99%

Wheel extraction based on micro doppler distribution using high-resolution radar

Kellner¹,

Barjenbruch²,

Klappstein

et al. 2015

2015 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)

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With the advent of advanced driver assistant systems (ADAS) in urban scenarios, a fast and reliable classification and motion estimation of wheel-based vehicles such as cars, trucks or motorcycles is crucial. The fact that the wheels' velocities differ from the vehicle's chassis velocity is exploited. For the first time, a fully automated approach based on the Doppler distribution extracts the exact positions of the wheels. The Normalized Doppler Moment is calculated, describing the Doppler signature of each reflection based on the Doppler distributions of wheels. Locations with high values reveal the positions of the wheels. Besides the classification, the vehicle's orientation and therefore the driving direction can be estimated. Furthermore the position of the rear axle is estimated, which is essential for a reliable prediction of rotational movements and yaw rate estimation. Experimental results with a 77 GHz automotive radar sensor demonstrate the feasibility of the approach.

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Radar for Autonomous Driving – Paradigm Shift from Mere Detection to Semantic Environment Understanding

et al. 2019

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Instantaneous full-motion estimation of arbitrary objects using dual Doppler radar

Cited by 40 publications

References 15 publications

Fusion of doppler radar and geometric attributes for motion estimation of extended objects

Fusion of doppler radar and geometric attributes for motion estimation of extended objects

Wheel extraction based on micro doppler distribution using high-resolution radar

Radar for Autonomous Driving – Paradigm Shift from Mere Detection to Semantic Environment Understanding

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