Enhancement of Doppler Unambiguity for Chirp-Sequence Modulated TDM-MIMO Radars

Roos, Fabian; Bechter, Jonathan; Appenrodt, Nils; Dickmann, Jrgen; Waldschmidt, Christian

doi:10.1109/icmim.2018.8443352

Cited by 35 publications

(16 citation statements)

References 6 publications

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“…Such a set-up is common in the time division multiplexing scheme, which is widely used to obtain the orthogonality between multiple transmitters. In the following section, we demonstrate that the proposed network can resolve the velocity ambiguity problem without hardware changes (e.g., overlapping elements [42]), changes in the transmission scheme (e.g., frequency shift [43], [44]), or additional signal processing steps [45].…”

Section: A Data Acquisitionmentioning

confidence: 94%

Complex-Valued Channel Attention and Application in Ego-Velocity Estimation With Automotive Radar

et al. 2021

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Attention mechanisms have been widely integrated with various neural networks to boost performance. However, when an attention mechanism was applied to a radar ego-velocity estimation network, the importance of carefully handling the amplitude and phase of complex-valued tensor was revealed. Therefore, in this study, we present a self-attention mechanism designed to handle complexvalued tensors in order to capture the rich contextual relationships implied within amplitude and phase. To exploit the advantages of complex-valued attention (CA), we evaluated its impact while performing egovelocity estimation tasks based on radar data, whose amplitude and phase are related to the electromagnetic scattering of the target being observed. Radars are suitable sensors for such tasks as they are capable of long-range detection and instantaneous velocity measurement under variable weather and lighting conditions. In particular, we coupled our CA module with complex-valued neural networks, known to be particularly powerful for handling wave phenomena. The proposed method exhibits robust estimation performance, regardless of whether the Doppler ambiguity problem occurs and eliminates the dependence on preprocessing stages, including target detection and static target indication. Furthermore, it achieves improved stability during training via geometrical constraint regularization, and implicitly allows velocity conversion between the sensor and vehicle frames, even if the mount information of the sensor was not provided. Finally, ablation experiments conducted on extensive real-world datasets show noticeable improvement in estimation performance. INDEX TERMS Automotive radar; complex-valued attention; complex-valued neural network; deep learning; ego-velocity estimation This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Section: A Data Acquisitionmentioning

confidence: 94%

Complex-Valued Channel Attention and Application in Ego-Velocity Estimation With Automotive Radar

et al. 2021

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“…The motion-induced phase error which leads to errors in the DoA estimation is compensated as described in [14]. The TDM usually reduces the maximum unambiguous velocity, which is restored by evaluating the phase trend along the virtual aperture as described in [15]. The used bandwidth of 2 GHz leads to a range resolution of 7.5 cm.…”

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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“…by the number of used transmitters M . With the solution presented in [36] the same unambiguous Doppler velocity as for the single-input multiple-output (SIMO) case can be achieved with phase evaluation of the virtual aperture.…”

Section: Tdm Mimo Limitations and Their Compensationmentioning

confidence: 99%

Radar Taking Off: New Capabilities for UAVs

et al. 2018

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Modern consumer and industrial unmanned aerial vehicles (UAVs) are easy to use flying sensor platforms. They offer stable flight, good maneuverability, hovering, and even waypoint flights in autopilot mode. For stabilization and localization sensors such as internal measurement units (IMUs) including gyroscope and accelerometer, barometric sensor, and global navigation satellite system (GNSS) are used. To sense the direct environment of the UAV, for instance for collision avoidance or fully automated flights, additional sensors are needed. State-of-the-art combinations of infrared sensors, ultrasonic sensors as well as vision based sensors (monocular and/or stereo vision) capture the close vicinity. Using radar sensors is advantageous, as they are able to directly sense range and velocity and are not prone to lighting conditions and contrast. With the help of a multi-channel radar, the angular information can also be extracted. UAVs can lift a considerable payload with respect to their size. All these characteristics combined with radar sensors make them a promising tool for a large variety of applications.

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Enhancement of Doppler Unambiguity for Chirp-Sequence Modulated TDM-MIMO Radars

Cited by 35 publications

References 6 publications

Complex-Valued Channel Attention and Application in Ego-Velocity Estimation With Automotive Radar

Complex-Valued Channel Attention and Application in Ego-Velocity Estimation With Automotive Radar

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

Radar Taking Off: New Capabilities for UAVs

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