3 research outputs found
Recommended from our members
Direction of Arrival Estimation and Sensor Array Error Calibration Based on Blind Signal Separation
We consider estimating the direction of arrival (DOA) in the presence of sensor array error. In the proposed method, a blind signal separation method, the Joint Approximation and Diagonalization of Eigenmatrices (JADE) algorithm, is implemented to separate the signal vector and the mixing matrix consisting of the array manifold matrix and the sensor array error matrix. Based on a new mixing matrix and the reconstruction of the array output vector of each individual signal, we propose a novel DOA estimation method and sensor array error calibration procedure. This method is independent of array phase errors and performs well against difference of SNR of signals. Numerical simulations verify the effectiveness of the proposed method
Multi-Antenna Dual-Blind Deconvolution for Joint Radar-Communications via SoMAN Minimization
Joint radar-communications (JRC) has emerged as a promising technology for
efficiently using the limited electromagnetic spectrum. In JRC applications
such as secure military receivers, often the radar and communications signals
are overlaid in the received signal. In these passive listening outposts, the
signals and channels of both radar and communications are unknown to the
receiver. The ill-posed problem of recovering all signal and channel parameters
from the overlaid signal is terms as dual-blind deconvolution (DBD). In this
work, we investigate a more challenging version of DBD with a multi-antenna
receiver. We model the radar and communications channels with a few (sparse)
continuous-valued parameters such as time delays, Doppler velocities, and
directions-of-arrival (DoAs). To solve this highly ill-posed DBD, we propose to
minimize the sum of multivariate atomic norms (SoMAN) that depends on the
unknown parameters. To this end, we devise an exact semidefinite program using
theories of positive hyperoctant trigonometric polynomials (PhTP). Our
theoretical analyses show that the minimum number of samples and antennas
required for perfect recovery is logarithmically dependent on the maximum of
the number of radar targets and communications paths rather than their sum. We
show that our approach is easily generalized to include several practical
issues such as gain/phase errors and additive noise. Numerical experiments show
the exact parameter recovery for different JRCComment: 40 pages, 6 figures. arXiv admin note: text overlap with
arXiv:2208.0438