  Passive radar systems can detect and track the moving targets of interest by
exploiting non-cooperative illuminators-of-opportunity to transmit orthogonal
frequency division multiplexing (OFDM) signals. These targets are searched
using a bank of correlators tuned to the waveform corresponding to the given
Doppler frequency shift and delay. In this paper, we study the problem of joint
delay-Doppler estimation of moving targets in OFDM passive radar. This task of
estimation is described as an atomic-norm regularized convex optimization
problem, or equivalently, a semi-definite programming problem. The alternating
direction method of multipliers (ADMM) can be employed which computes each
variable in a Gauss-Seidel manner, but its convergence is lack of certificate.
In this paper, we use a symmetric Gauss-Seidel (sGS) to the framework of ADMM,
which only needs to compute some of the subproblems twice but has the ability
to ensure convergence. We do some simulated experiments which illustrate that
the sGS-ADMM is superior to ADMM in terms of accuracy and computing time.
