8 accepted papers
Radar-based object detection is essential for autonomous driving due to radar's long detection range. However, the sparsity of radar point clouds, especially at long range, poses challenges for accurate detection. Existing methods increase point density through temporal aggregation with ego-motion c…
Object detection in radar imagery with neural networks shows great potential for improving autonomous driving. However obtaining annotated datasets from real radar images crucial for training these networks is challenging especially in scenarios with long-range detection and adverse weather and ligh…
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. I…
We consider the problem of estimating the angles of arrival (AOAs) of multiple sources from a single snapshot obtained by a set of non-coherent sub-arrays, i.e., while the antenna elements in each sub-array are coherent, each sub-array observes a different unknown phase. Previous relevant works are…
In this paper we develop a deep neural network (DNN) method for estimating the object surface from radar 2D image (azimuth-range). The DNN is designed to maintain the input image angular resolution and produces two outputs per each angle, which are a classification bit and a regression value. The cl…
In this paper a multi-radar beamforming algorithm is developed that attains higher angular resolution than the individual radars. The method is based on jointly processing the received signals from mutually non-coherent radars that are widely spaced on a vehicle. The proposed method can separate and…
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. When the number of sources is large, the maximum likelihood estimator is intractable du…
Attaining accurate estimation of a signal time-of-arrival (TOA) in dense multipath channels is very challenging. This problem was traditionally solved with signal processing techniques. In this paper, a novel deep convolutional neural networks (DCNN) TOA estimator is developed. The DCNN was trained…