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

Roos, Fabian; Hügler, Philipp; Torres, Lizette Lorraine Tovar; Knill, Christina; Schlichenmaier, Johannes; Vasanelli, Claudia; Appenrodt, Nils; Dickmann, Jürgen; Waldschmidt, Christian

doi:10.23919/gemic.2019.8698136

Cited by 30 publications

(9 citation statements)

References 14 publications

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“…In [79] bounds on the number of targets recoverable in the angular domain are derived. In [78], [80]- [82], CS is combined with sparse antenna arrays to increase both target estimation robustness and accuracy without increasing the number of required physical antennas. The results show that sidelobes in DOA estimation are mitigated, and CS is capable of better performance than sophisticated methods such as MUSIC.…”

Section: B Compressed Sensingmentioning

confidence: 99%

“…The results show that sidelobes in DOA estimation are mitigated, and CS is capable of better performance than sophisticated methods such as MUSIC. Measurements of an automotive FMCW MIMO sparse array with CS DOA estimation are investigated in [78]. The results show that targets within the same range-velocity cell with up to 10 dB difference in RCS are still distinguishable with CS, which in turn reveals a clear restriction on the use of compressed sensing in the automotive sector.…”

Section: B Compressed Sensingmentioning

confidence: 99%

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Automotive Radar — From First Efforts to Future Systems

2021

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Section: B Compressed Sensingmentioning

confidence: 99%

Section: B Compressed Sensingmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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“…Most of the time, for example, in [8,42], a time-division multiplexing (TDM) technique is employed to switch transmitters on and off consecutively. The actual direction of arrival estimation can be calculated using several techniques such as MUltiple SIgnal Classification (MUSIC) [37], Maximum likelihood methods [41], and single snapshot DOA estimation [17,33], amongst others. One limitation of TDM techniques is the presence of phase errors when the targets and/or the radar is in motion between the switching among the transmitters.…”

Section: Principle Of Operations Of Radarsmentioning

confidence: 99%

Radar Perception for Autonomous Unmanned Aerial Vehicles: a Survey

Corradi

Fioranelli

2022

System Engineering for Constrained Embedded Systems

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The advent of consumer and industrial Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, has opened business opportunities in many fields, including logistics, smart agriculture, inspection, surveillance, and construction. In addition, the autonomous operations of UAVs reduce risks by minimizing the time spent by human workers in harsh environments and lowering costs by automating tasks. For reliability and safety, the drones must sense and avoid potential obstacles and must be capable of safely navigating in unknown environments. UAVs' perception requires reliability in various settings, such as high dust levels, humidity, intense sun glare, dark, and fog that can severely obstruct many conventional sensing methods. Radar systems have unique strengths; they can reliably estimate how far an object is and measure its relative speed via the Doppler effect. In addition, because radars exploit radio waves to sense, they perform well in rain, fog, snow, or smoky environments. This stands in contrast to optical technologies, such as cameras or LIght Detection And Ranging (Lidars), which are more susceptible to the same challenges as the human eye. This survey paper aims to address the signal processing challenges for the exploitation of radar systems in unmanned aerial vehicles for advanced perception, considering recent integration trends and technology capabilities. The focus is on signal processing techniques for low-cost and power-efficient radar sensors, which operate onboard the UAVs in real-time to ensure their needs in terms of perception, situational awareness, and navigation. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, safe, and autonomous way for UAVs to perceive and interact with the world. CCS CONCEPTS• Computer systems organization → System on a chip; Embedded hardware;This work is licensed under a Creative Commons Attribution International 4.0 License.

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“…Depending on the DoA approach, this gap results in ambiguities and artifacts. One solution, which is also used for sparse antenna arrays of MIMO radars, is the use of angular estimation based on the Fourier transform [28] combined with compressed sensing (CS) for sparsity reconstruction [29], [30]. Due to the special sampling structure with a repetition of the virtual array at every repeater and thus several rectangular functions, zero padding is crucial.…”

Section: Doa Estimation With Compressed Sensingmentioning

confidence: 99%

OFDM-Based Radar Network Providing Phase Coherent DOA Estimation

Werbunat¹,

Meinecke²,

Schweizer³

et al. 2021

IEEE Trans. Microwave Theory Techn.

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Next-generation radar sensors require imaging capabilities with high angular resolution. As for a single sensor, the aperture, and thus the achievable resolution, is limited due to the constraints of the front end, radar networks consisting of multiple sensors are a possible solution. However, their incoherency usually makes joint angle estimation impossible. This article presents a network concept consisting of an orthogonal frequency-division multiplexing (OFDM) radar and repeater elements, which receive the reflections from targets and retransmit them back to the radar. Thereby, any frequency conversion from radio frequency to baseband and vice versa is omitted such that the signal remains coherent to the initial transmit signal. To distinguish the bistatic signal transmitted by the repeater from the monostatic one of the OFDM radar, the orthogonal subcarrier structure of OFDM waveforms is exploited by combining a sparse radar transmit signal with a low-frequency modulation in the repeater. This allows to evaluate the bistatic signals at the radar with standard multiple-input-multiple-output (MIMO)-OFDM signal processing, leading to separate range-Doppler images for each virtual channel. Finally, it is shown that this method offers a coherent angular estimation based on the extended aperture of the network. For this purpose, a method to establish phase coherency by a reconstruction of the modulation phase is presented. The network concept is proved with measurements at 77 GHz.

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

Cited by 30 publications

References 14 publications

Automotive Radar — From First Efforts to Future Systems

Automotive Radar — From First Efforts to Future Systems

Radar Perception for Autonomous Unmanned Aerial Vehicles: a Survey

OFDM-Based Radar Network Providing Phase Coherent DOA Estimation

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