3 research outputs found
Deep Radar Waveform Design for Efficient Automotive Radar Sensing
In radar systems, unimodular (or constant-modulus) waveform design plays an
important role in achieving better clutter/interference rejection, as well as a
more accurate estimation of the target parameters. The design of such sequences
has been studied widely in the last few decades, with most design algorithms
requiring sophisticated a priori knowledge of environmental parameters which
may be difficult to obtain in real-time scenarios. In this paper, we propose a
novel hybrid model-driven and data-driven architecture that adapts to the ever
changing environment and allows for adaptive unimodular waveform design. In
particular, the approach lays the groundwork for developing extremely low-cost
waveform design and processing frameworks for radar systems deployed in
autonomous vehicles. The proposed model-based deep architecture imitates a
well-known unimodular signal design algorithm in its structure, and can quickly
infer statistical information from the environment using the observed data. Our
numerical experiments portray the advantages of using the proposed method for
efficient radar waveform design in time-varying environments
Efficient Waveform Covariance Matrix Design and Antenna Selection for MIMO Radar
Controlling the radar beam-pattern by optimizing the transmit covariance
matrix is a well-established approach for performance enhancement in
multiple-input-multiple-output (MIMO) radars. In this paper, we investigate the
joint optimization of the waveform covariance matrix and the antenna position
vector for a MIMO radar system to approximate a given transmit beam-pattern, as
well as to minimize the cross-correlation between the probing signals at a
number of given target locations. We formulate this design task as a non-convex
optimization problem and then propose a cyclic optimization approach to
efficiently approximate its solution. We further propose a local binary search
algorithm in order to efficiently design the corresponding antenna positions.
We show that the proposed method can be extended to the more general case of
approximating the given beam-pattern using a minimal number of antennas as well
as optimizing their positions. Our numerical investigations demonstrate a great
performance both in terms of accuracy and computational complexity, making the
proposed framework a good candidate for usage in real-time radar waveform
processing applications such as MIMO radar transmit beamforming for aerial
drones that are in motion.Comment: arXiv admin note: text overlap with arXiv:1910.0759
Unfolded Algorithms for Deep Phase Retrieval
Exploring the idea of phase retrieval has been intriguing researchers for
decades, due to its appearance in a wide range of applications. The task of a
phase retrieval algorithm is typically to recover a signal from linear
phaseless measurements. In this paper, we approach the problem by proposing a
hybrid model-based data-driven deep architecture, referred to as Unfolded Phase
Retrieval (UPR), that exhibits significant potential in improving the
performance of state-of-the art data-driven and model-based phase retrieval
algorithms. The proposed method benefits from versatility and interpretability
of well-established model-based algorithms, while simultaneously benefiting
from the expressive power of deep neural networks. In particular, our proposed
model-based deep architecture is applied to the conventional phase retrieval
problem (via the incremental reshaped Wirtinger flow algorithm) and the sparse
phase retrieval problem (via the sparse truncated amplitude flow algorithm),
showing immense promise in both cases. Furthermore, we consider a joint design
of the sensing matrix and the signal processing algorithm and utilize the deep
unfolding technique in the process. Our numerical results illustrate the
effectiveness of such hybrid model-based and data-driven frameworks and
showcase the untapped potential of data-aided methodologies to enhance the
existing phase retrieval algorithms