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Found 6 papers, 0 papers with code
RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation
Object detection in radar imagery with neural networks shows great potential for improving autonomous driving.
BoostRad: Enhancing Object Detection by Boosting Radar Reflections
Subsequently, a second DNN is employed to detect objects within the boosted reflection image.
Performance Analysis of Automotive SAR With Radar Based Motion Estimation
In this paper, we consider an automotive SAR system that produces SAR images of static objects based on ego vehicle velocity estimation from the radar return signal without the overhead in complexity and cost of using an auxiliary global navigation satellite system (GNSS) and inertial measurement unit (IMU).
Direction of Arrival Estimation and Phase-Correction for Non-Coherent Sub-Arrays: A Convex Optimization Approach
Estimating the direction of arrival (DOA) of sources is an important problem in aerospace and vehicular communication, localization and radar.
Direction of Arrival Estimation for Non-Coherent Sub-Arrays via Joint Sparse and Low-Rank Signal Recovery
Estimating the directions of arrival (DOAs) of multiple sources from a single snapshot obtained by a coherent antenna array is a well-known problem, which can be addressed by sparse signal reconstruction methods, where the DOAs are estimated from the peaks of the recovered high-dimensional signal.
Performance Advantages of Deep Neural Networks for Angle of Arrival Estimation
The problem of estimating the number of sources and their angles of arrival from a single antenna array observation has been an active area of research in the signal processing community for the last few decades.
