112 citations · 399 across the 29 of their papers we have counts for
9 papers · 1 filter
Compressed Sensing Radar Detectors based on Weighted LASSO
Siqi Na, Yoshiyuki Kabashima, Takashi Takahashi +3
The compressed sensing (CS) model can represent the signal recovery process of a large number of radar systems. The detection problem of such radar systems has been studied in many…
Direction Finding in Partly Calibrated Arrays Exploiting the Whole Array Aperture
Guangbin Zhang, Tianyao Huang, Yimin Liu +2
We consider the problem of direction finding using partly calibrated arrays, a distributed subarray with position errors between subarrays. The key challenge is to enhance angular…
Compressed sensing radar detectors under the row-orthogonal design model: a statistical mechanics perspective
Siqi Na, Tianyao Huang, Yimin Liu +3
Compressed sensing (CS) model of complex-valued data can represent the signal recovery process of a large amount types of radar systems, especially when the measurement matrix is r…
Designing the Waveform Bandwidth and Time Duration of Automotive Radars for Better Collision Warning Performance
Hang Ruan, Yimin Liu, Tianyao Huang +1
Automotive radar is a key component in an ADAS. The increasing number of radars implemented in vehicles makes interference between them a noteworthy issue. One method of interferen…
Decentralized Automotive Radar Spectrum Allocation to Avoid Mutual Interference Using Reinforcement Learning
Pengfei Liu, Yimin Liu, Tianyao Huang +2
Nowadays, mutual interference among automotive radars has become a problem of wide concern. In this paper, a decentralized spectrum allocation approach is presented to avoid mutual…
Cognitive Radar Using Reinforcement Learning in Automotive Applications
Pengfei Liu, Yimin Liu, Tianyao Huang +2
The concept of cognitive radar (CR) enables radar systems to achieve intelligent adaption to a changeable environment with feedback facility from receiver to transmitter. However,…