Reduced-dimension STAP method for conformal array based on sequential convex programming

Compared with the uniform arrays, the conformal array can effectively reduce the radar cross section and improve the utilization of the limited space in the aircraft. However, the special array structure aggravates the non-stationarity of the clutter, and the typical blocking matrix construction met...

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Vydané v:Signal processing Ročník 228; s. 109745
Hlavní autori: Shi, Jingxi, Yao, Xueqi, Wang, Zhihang, Cheng, Ziyang, Xie, Lei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.03.2025
Predmet:
Conformal array
Convex optimization
Reduced dimension
Space-time adaptive processing (STAP)
Convex optimization
Reduced dimension
Conformal array
Space-time adaptive processing (STAP)
ISSN:0165-1684
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Shrnutí:Compared with the uniform arrays, the conformal array can effectively reduce the radar cross section and improve the utilization of the limited space in the aircraft. However, the special array structure aggravates the non-stationarity of the clutter, and the typical blocking matrix construction method in the space–time adaptive processing (STAP) is not appropriate any more. To address these issues, a reduced dimension STAP algorithm based on penalty sequential convex programming in the generalized sidelobe cancellation structure is proposed. The blocking matrix, channel selection vector and STAP weights can be optimized simultaneously in the algorithm framework. To tackle the resultant nonconvex problem, we formulate the original optimization function as a quasi-convex form and solve these parameters alternately within an iterative framework. Numerical simulations are provided to validate the proposed method and demonstrate its high performance. •Reduced dimension space-time adaptive processing for the conformal arrays.•Processing framework based on the generalized sidelobe cancellation.•Joint optimization of blocking matrix, channel selection and filter weights.•Solving resultant nonconvex problem with sequential convex programming.
ISSN:0165-1684
DOI:10.1016/j.sigpro.2024.109745