적은 STAP 데이터의 공간주파수-도플러 평면 변환을이용한 공분산행렬 추정
Covariance Matrix Estimation with Small STAP Data through Conversion into Spatial Frequency-Doppler Plane
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초록
STAP(space-time adaptive processing) 알고리즘의 성능은 CUT(cell under test) 내의 간섭에 대한 공분산 행렬 추정의 정확도가 결정적 역할을 한다. STAP 데이터는 일반적으로 많은 배열 소자 및 사용된 다수의 송신 펄스에 의해 결정되는 2차원 데이터구조를 가지고 있다. 그러므로 공분산 행렬 추정의 정확도를 높이기 위해서는 매우 많은 트레이닝 데이터가 요구된다. 본 논문에서는 수신된 적은 개수의 데이터를 공간주파수-도플러 평면으로 변환한 후 가상의 트레이닝 데이터를 생성하는 알고리즘을 제시한다. 클러터 점유 위치를 이론적으로 유도하며 이에 근거해서 가상 트레이닝 데이터 생성 절차를 제시하고 STAP 시뮬레이션을 통해서제시된 알고리즘이 STAP 성능을 개선할 수 있음을 보인다.
Abstract
Performance of a STAP(space-time adaptive processing) algorithm highly depends on how closely the estimatedcovariance matrix(CM) resembles the actual CM by the interference in CUT(cell under test). A STAP has 2dimensional data structure determined by the number of array elements and the number of transmitting pulses andboth numbers are generally not small. Thus, to meet the degree of freedom(DOF) of the CM, a huge amount oftraining data is required. This paper presents an algorithm to generate virtual training data from small receiveddata, via converting them into the data in spatial frequency-Doppler plane. We theoretically derive where theclutter exist in the plane and present the procedure to implement the proposed algorithm. Finally, with thesimulated scenario of small received data, we show the proposed algorithm can improve STAP performance.
참고문헌 (13)
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