  Range-dependent clutter suppression poses significant challenges in airborne
frequency diverse array (FDA) radar, where resolving range ambiguity is
particularly difficult. Traditional space-time adaptive processing (STAP)
techniques used for clutter mitigation in FDA radars operate in the physical
domain defined by first-order statistics. In this paper, unlike conventional
airborne uniform FDA, we introduce a space-time-range adaptive processing
(STRAP) method to exploit second-order statistics for clutter suppression in
the newly proposed co-pulsing FDA radar. This approach utilizes co-prime
frequency offsets (FOs) across the elements of a co-prime array, with each
element transmitting at a non-uniform co-prime pulse repetition interval
(C-Cube). By incorporating second-order statistics from the co-array domain,
the co-pulsing STRAP or CoSTAP benefits from increased degrees of freedom
(DoFs) and low computational cost while maintaining strong clutter suppression
capabilities. However, this approach also introduces significant computational
burdens in the coarray domain. To address this, we propose an approximate
method for three-dimensional (3-D) clutter subspace estimation using discrete
prolate spheroidal sequences (DPSS) to balance clutter suppression performance
and computational cost. We first develop a 3-D clutter rank evaluation
criterion to exploit the geometry of 3-D clutter in a general scenario.
Following this, we present a clutter subspace rejection method to mitigate the
effects of interference such as jammer. Compared to existing FDA-STAP
algorithms, our proposed CoSTAP method offers superior clutter suppression
performance, lower computational complexity, and enhanced robustness to
interference. Numerical experiments validate the effectiveness and advantages
of our method.
