2 research outputs found
Direction Finding of Electromagnetic Sources on a Sparse Cross-Dipole Array Using One-Bit Measurements
Sparse array arrangement has been widely used in vector-sensor arrays because
of increased degree-of-freedoms for identifying more sources than sensors. For
large-size sparse vector-sensor arrays, one-bit measurements can further reduce
the receiver system complexity by using low-resolution ADCs. In this paper, we
present a sparse cross-dipole array with one-bit measurements to estimate
Direction of Arrivals (DOA) of electromagnetic sources. Based on the
independence assumption of sources, we establish the relation between the
covariance matrix of one-bit measurements and that of unquantized measurements
by Bussgang Theorem. Then we develop a Spatial-Smooth MUSIC (SS-MUSIC) based
method, One-Bit MUSIC (OB-MUSIC), to estimate the DOAs. By jointly utilizing
the covariance matrices of two dipole arrays, we find that OB-MUSIC is robust
against polarization states. We also derive the Cramer-Rao bound (CRB) of DOA
estimation for the proposed scheme. Furthermore, we theoretically analyze the
applicability of the independence assumption of sources, which is the
fundamental of the proposed and other typical methods, and verify the
assumption in typical communication applications. Numerical results show that,
with the same number of sensors, one-bit sparse cross-dipole arrays have
comparable performance with unquantized uniform linear arrays and thus provide
a compromise between the DOA estimation performance and the system complexity
Gridless Parameter Estimation for One-Bit MIMO Radar with Time-Varying Thresholds
We investigate the one-bit MIMO (1b-MIMO) radar that performs one-bit
sampling with a time-varying threshold in the temporal domain and employs
compressive sensing in the spatial and Doppler domains. The goals are to
significantly reduce the hardware cost, energy consumption, and amount of
stored data. The joint angle and Doppler frequency estimations from noisy
one-bit data are studied. By showing that the effect of noise on one-bit
sampling is equivalent to that of sparse impulsive perturbations, we formulate
the one-bit -regularized atomic-norm minimization (1b-ANM-L1) problem
to achieve gridless parameter estimation with high accuracy. We also develop an
iterative method for solving the 1b-ANM-L1 problem via the alternating
direction method of multipliers. The Cramr-Rao bound (CRB) of
the 1b-MIMO radar is analyzed, and the analytical performance of one-bit
sampling with two different threshold strategies is discussed. Numerical
experiments are presented to show that the 1b-MIMO radar can achieve
high-resolution parameter estimation with a largely reduced amount of data.Comment: 31 pages, 12 figure