發(fā)布時(shí)間：2018-10-20 19:47

【摘要】：針對傳統(tǒng)依靠于人工經(jīng)驗(yàn)提取雷達(dá)輻射源特征方法的不足,提出了一種新穎的基于聯(lián)合深度時(shí)頻特征的輻射源識(shí)別算法.首先將時(shí)域信號(hào)變換到二維時(shí)頻域,并利用隨機(jī)投影和主成分分析方法分別從維持子空間和能量角度對時(shí)頻圖像降維;接著在預(yù)訓(xùn)練階段,利用無標(biāo)簽的樣本信號(hào)層級(jí)訓(xùn)練深度模型,再根據(jù)類別信息精調(diào)網(wǎng)絡(luò)參數(shù);最后,構(gòu)造了邏輯回歸分類來完成識(shí)別任務(wù).仿真實(shí)驗(yàn)中利用6種輻射源信號(hào)驗(yàn)證了提出算法的有效性,結(jié)果表明,聯(lián)合深度特征更加有助于提高識(shí)別準(zhǔn)確度,算法運(yùn)行更加高效.
[Abstract]:Aiming at the shortcoming of the traditional method of extracting radar emitter features based on artificial experience, a novel emitter recognition algorithm based on joint depth time-frequency features is proposed. Firstly, the time domain signal is transformed into two dimensional time-frequency domain, and then the dimension of the time-frequency image is reduced from the maintenance subspace and the energy angle by the methods of stochastic projection and principal component analysis, and then in the pre-training stage, Using the untagged sample signal level training depth model, the network parameters are carefully adjusted according to the category information. Finally, the logical regression classification is constructed to complete the recognition task. The effectiveness of the proposed algorithm is verified by using six emitter signals in simulation experiments. The results show that the combined depth feature is more helpful to improve the recognition accuracy and the algorithm runs more efficiently.
【作者單位】： 海軍工程大學(xué)電子工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61501484) 國家“863”高技術(shù)研究發(fā)展計(jì)劃資助項(xiàng)目(2014AA7014061)
【分類號(hào)】：TN957.51

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