Radar Taking Off: New Capabilities for UAVs

Huegler, Philipp; Roos, Fabian; Schartel, Markus; Geiger, Martin; Waldschmidt, Christian

doi:10.1109/mmm.2018.2862558

Cited by 83 publications

(47 citation statements)

References 30 publications

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“…To verify the performance of the fabricated sparse antenna, measurements are performed with an experimental 77 GHz MIMO radar system [12]. The modulation parameters are listed in Table 1.…”

Section: Measurement Resultsmentioning

confidence: 99%

“…This ensures that no high values directly outside of the ambiguity-free region are present and controls the side lobe level in general. The boundary conditions are mainly defined by the experimental radar system [12], which uses a waveguide transition to an exchangeable antenna printed circuit board (PCB). Four transmitters and eight receivers are available in total.…”

Section: Sparse Array Designmentioning

confidence: 99%

See 1 more Smart Citation

Compressed Sensing based Single Snapshot DoA Estimation for Sparse MIMO Radar Arrays

Roos¹,

Hügler²,

Torres³

et al. 2019

2019 12th German Microwave Conference (GeMiC)

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The angular resolution of a radar system can be enhanced with an increasing antenna aperture. Instead of using more antenna elements, the distances in the aperture can be increased with a sparse array. To mitigate the high side lobes originating from the sparse array, the missing antenna elements can be reconstructed by means of compressed sensing. In this paper a sparse antenna array with a low side lobe level is determined with a genetic algorithm and a cost function. An investigation is performed what difference in the radar cross section of two targets in the same range-Doppler cell can be achieved. Additionally, instead of considering point targets only, a target vehicle is measured with a 77 GHz MIMO radar.

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“…To verify the performance of the fabricated sparse antenna, measurements are performed with an experimental 77 GHz MIMO radar system [12]. The modulation parameters are listed in Table 1.…”

Section: Measurement Resultsmentioning

confidence: 99%

Section: Sparse Array Designmentioning

confidence: 99%

Compressed Sensing based Single Snapshot DoA Estimation for Sparse MIMO Radar Arrays

Roos¹,

Hügler²,

Torres³

et al. 2019

2019 12th German Microwave Conference (GeMiC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…4 shows the continuous phase of the beat signal for two adjacent range cells. A range shift according to (5) leads to a phase step if the size of a range cell is not an integer multiple of the wavelength. Therefore, an additional phase correction must be applied to mathematically match with the frequency shift in (4).…”

Section: B Error Correction Methodsmentioning

confidence: 99%

“…I MAGING radar sensors provide accurate measurements of range, velocity, and angle and are used in a variety of civil and military applications [1]- [5]. Due to a large number of applications, there is an increasing demand for low-cost and compact imaging radar sensors with an improved resolution and, thus, a higher separability of closely spaced targets.…”

Section: Introductionmentioning

confidence: 99%

Range-Angle Coupling and Near-Field Effects of Very Large Arrays in mm-Wave Imaging Radars

Dürr¹,

Schneele²,

Schwarz³

et al. 2021

IEEE Trans. Microwave Theory Techn.

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In order to improve the resolution of imaging radars, electrically large arrays and a high absolute modulation bandwidth are needed. For radar systems with simultaneously high range resolution and very large aperture, the difference in path length at the receiving antennas is a multiple of the range resolution of the radar, in particular for off-boresight angles of the incident wave. Therefore, the radar response of a target measured at the different receiving antennas is distributed over a large number of range cells. This behavior depends on the unknown incident angle of the wave and is, thus, denoted as range-angle coupling. Furthermore, the far-field (FF) condition is no longer fulfilled in short-range applications. Applying conventional signal processing and radar calibration techniques leads to a significant reduction of the resolution capabilities of the array. In this article, the key aspects of radar imaging are discussed when radars with both large aperture size and high absolute bandwidth are employed in short-range applications. Based on an initial mathematical formulation of the physical effects, a correction method and an efficient signal processing chain are proposed, which compensate for errors that occur with conventional beamforming techniques. It is shown by measurements that with an appropriate error correction an improvement of the angular resolution up to a factor of 2.5 is achieved, resulting in an angular resolution below 0.4 • with an overall aperture size of nearly 200 λ 0 .

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“…To verify the proposed sub-clustering algorithm, measurements were performed with a 77 GHz experimental radar sensor [13] operating in time-division multiplexing (TDM) multiple-input multiple-output mode. Its three transmitters and eight receivers form a uniform linear array with λ 2 spacing without overlapping virtual elements.…”

Section: Measurement Data Characteristicsmentioning

confidence: 99%

Clustering of Closely Adjacent Extended Objects in Radar Images using Velocity Profile Analysis

Schlichenmaier¹,

Roos²,

Hügler³

et al. 2019

2019 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)

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As high resolution automotive radars become more common, so does their usage for next-generation functionalities like advanced driver assistant systems and autonomous driving. This creates the need for robust clustering techniques to distinguish among multiple extended objects like vehicles in the same scenario. One especially challenging scenario is that of separating two extended targets close to each other, each following its own trajectory. This paper proposes a clustering algorithm based on the analysis of the velocity profile to divide target points of multiple vehicles into sub-clusters. The theoretical background is explained and shown on simulation data. The algorithm is verified using radar measurements of two extended vehicular targets.

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Radar Taking Off: New Capabilities for UAVs

Cited by 83 publications

References 30 publications

Compressed Sensing based Single Snapshot DoA Estimation for Sparse MIMO Radar Arrays

Compressed Sensing based Single Snapshot DoA Estimation for Sparse MIMO Radar Arrays

Range-Angle Coupling and Near-Field Effects of Very Large Arrays in mm-Wave Imaging Radars

Clustering of Closely Adjacent Extended Objects in Radar Images using Velocity Profile Analysis

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