雷达学报
雷達學報
뢰체학보
JOURNAL OF RADARS
2014年
6期
686-693
,共8页
陈伟%万显荣%张勋%饶云华%程丰
陳偉%萬顯榮%張勛%饒雲華%程豐
진위%만현영%장훈%요운화%정봉
外辐射源雷达%时域杂波抑制%扩展杂波相消(ECA)%并行实现
外輻射源雷達%時域雜波抑製%擴展雜波相消(ECA)%併行實現
외복사원뢰체%시역잡파억제%확전잡파상소(ECA)%병행실현
Passive radar%Time domain clutter suppression%Extensive Cancellation Algorithm (ECA)%Parallel implementation
直达波及杂波抑制是外辐射源雷达目标信息提取的关键步骤之一。扩展杂波相消批处理(ECA-B)算法是一种有效的时域杂波抑制算法,但算法空间和时间复杂度极大,在处理多通道(或多波束)数据时尤是如此。结合图形处理器(GPU)数据吞吐量大、浮点运算能力强的优点,该文提出一种适用于 GPU 处理的多通道 ECA-B 时域杂波抑制并行算法。首先推导了多通道 ECA-B 算法的原理,避免了原算法分别对单通道进行单独处理的计算冗余问题。然后针对其中耗时最大的自相关矩阵计算,提出一种迭代计算方法,使时间和空间复杂度均降至常规方法的约1/K (K 为杂波自由度)。最后给出了改进算法的 GPU 并行实现方案。仿真和实测结果验证了算法的准确性和实效性。
直達波及雜波抑製是外輻射源雷達目標信息提取的關鍵步驟之一。擴展雜波相消批處理(ECA-B)算法是一種有效的時域雜波抑製算法,但算法空間和時間複雜度極大,在處理多通道(或多波束)數據時尤是如此。結閤圖形處理器(GPU)數據吞吐量大、浮點運算能力彊的優點,該文提齣一種適用于 GPU 處理的多通道 ECA-B 時域雜波抑製併行算法。首先推導瞭多通道 ECA-B 算法的原理,避免瞭原算法分彆對單通道進行單獨處理的計算冗餘問題。然後針對其中耗時最大的自相關矩陣計算,提齣一種迭代計算方法,使時間和空間複雜度均降至常規方法的約1/K (K 為雜波自由度)。最後給齣瞭改進算法的 GPU 併行實現方案。倣真和實測結果驗證瞭算法的準確性和實效性。
직체파급잡파억제시외복사원뢰체목표신식제취적관건보취지일。확전잡파상소비처리(ECA-B)산법시일충유효적시역잡파억제산법,단산법공간화시간복잡도겁대,재처리다통도(혹다파속)수거시우시여차。결합도형처리기(GPU)수거탄토량대、부점운산능력강적우점,해문제출일충괄용우 GPU 처리적다통도 ECA-B 시역잡파억제병행산법。수선추도료다통도 ECA-B 산법적원리,피면료원산법분별대단통도진행단독처리적계산용여문제。연후침대기중모시최대적자상관구진계산,제출일충질대계산방법,사시간화공간복잡도균강지상규방법적약1/K (K 위잡파자유도)。최후급출료개진산법적 GPU 병행실현방안。방진화실측결과험증료산법적준학성화실효성。
Cancellation of clutter and multi-path is one of the key steps in passive radar target information extraction. Extensive Cancellation Algorithm Batches (ECA-B) is an effective time-domain clutter suppression algorithm, but with high time and space complexity, and even higher with multi-channel (or multi-beam) data processing. Combining high memory throughput and tremendous computational horsepower of GPU graphics processor, this paper proposes a multi-channel ECA-B algorithm which is suitable for parallel implementation on GPUs. Firstly, the principle of multi-channel ECA-B algorithm is derived, avoiding the redundancy of processing each channel singly. Then an iterative calculation method is presented for reducing the biggest time-consuming calculation of the correlation matrix, so that time and space complexity are both reduced to 1/K (K is clutter’s degree of freedom) of the conventional method. Finally, the full GPU parallel implementation of the algorithm is given. The simulation and experimental results verify the accuracy and effectiveness of the proposed algorithm.