基于核空间联合稀疏表示和指数平滑的多基地水下小目标识别

Multi-Static Underwater Small Target Recognition Based on Kernel Joint Sparse Representation and Exponential Smoothing

针对多基地水下小目标分类识别问题,本文提出了一种基于核空间联合稀疏表示和指数平滑的多基地水下小目标识别方法 .对水下目标多角度散射信号提取6种典型的具有信息互补性和关联性的特征,提出一种随机森林(Random Forest,RF)和最小冗余最大相关(minimum Redundancy and Maximum Relevance,mRMR)相结合的特征选择方法(RF-mRMR),得出综合的特征重要性排序结果 .通过实验得出分类模型所需的最优特征子集,达到降低数据处理复杂度和提高目标分类结果的目的 .为了捕捉到数据中的高阶结构,在联合稀疏表示模型的基础上,使用核函数将线性不可分的特征数据映射到高维核特征空间.为了充分挖掘稀疏重构后包含在残差波段中的有用信息,使用指数平滑公式对具有一定意义的残差信息进行再利用,最后由核特征空间下的最小误差准则判定目标的类别.应用本文提出的方法对4类目标的海试数据进行识别,结果表明,相较于其他7种对比算法,本文提出的改进方法具有更好的分类性能,而且大多数情况下,本文提出的算法在双基地声呐模式下具有比单基地声呐更高的识别准确率和更低的虚警率.

Aiming at the problem of multi-static underwater small target classification and recognition,a multi-static underwater small target recognition method based on joint sparse representation of kernel space and exponential smoothing is proposed.Six typical features with information complementarity and correlation are extracted from the multi angle scat-tering signals of underwater targets.A feature selection method(RF-mRMR)combining random forest(RF)and minimum redundancy maximum correlation(mRMR)is proposed to obtain the comprehensive feature importance ranking results.The optimal feature subset required by the classification model is obtained through experiments,so as to reduce the com-plexity of data processing and improve the result of target classification.In order to capture the high-order structure in the data,based on the joint sparse representation model,the kernel function is used to map the linearly indivisible feature data to the high-dimensional kernel feature space.In order to fully mine the useful information contained in the residual band af-ter sparse reconstruction,the exponential smoothing formula is used to reuse the residual information with certain signifi-cance.Finally,the category of the target is determined by the minimum error criterion under the kernel feature space.The method proposed in this paper is applied to identify the sea trial data of four types of targets.The results show that the im-proved method has better classification performance than the other seven comparison algorithms in this paper.In most cas-es,the proposed algorithm has higher recognition accuracy and lower false alarm rate than mono-static sonar in bistatic so-nar mode.