2 research outputs found
Joint Filter and Waveform Design for Radar STAP in Signal Dependent Interference
Waveform design is a pivotal component of the fully adaptive radar construct.
In this paper we consider waveform design for radar space time adaptive
processing (STAP), accounting for the waveform dependence of the clutter
correlation matrix. Due to this dependence, in general, the joint problem of
receiver filter optimization and radar waveform design becomes an intractable,
non-convex optimization problem, Nevertheless, it is however shown to be
individually convex either in the filter or in the waveform variables. We
derive constrained versions of: a) the alternating minimization algorithm, b)
proximal alternating minimization, and c) the constant modulus alternating
minimization, which, at each step, iteratively optimizes either the STAP filter
or the waveform independently. A fast and slow time model permits waveform
design in radar STAP but the primary bottleneck is the computational complexity
of the algorithms.Comment: AFRL tech. treport, 2014. DTIC (www.dtic.mil) number unassigne
Constrained Least Squares, SDP, and QCQP Perspectives on Joint Biconvex Radar Receiver and Waveform design
Joint radar receive filter and waveform design is non-convex, but is
individually convex for a fixed receiver filter while optimizing the waveform,
and vice versa. Such classes of problems are fre- quently encountered in
optimization, and are referred to biconvex programs. Alternating minimization
(AM) is perhaps the most popu- lar, effective, and simplest algorithm that can
deal with bi-convexity. In this paper we consider new perspectives on this
problem via older, well established problems in the optimization literature. It
is shown here specifically that the radar waveform optimization may be cast as
constrained least squares, semi-definite programs (SDP), and quadratically
constrained quadratic programs (QCQP). The bi-convex constraint introduces sets
which vary for each iteration in the alternat- ing minimization. We prove
convergence of alternating minimization for biconvex problems with biconvex
constraints by showing the equivalence of this to a biconvex problem with
constrained Cartesian product convex sets but for convex hulls of small
diameter.Comment: 7 Pages, 1 figure, IET, International Conference on Radar Systems,
23-27 OCT 2017, Belfast U