基于改进视觉词袋模型的水声目标识别

Underwater Acoustic Target Recognition Based on Improved Bag of Visual Words

水声目标识别的任务是通过采集到水声目标的信号来对目标进行分类,在海洋勘探,监听技术等领域有着非常重要和广泛的应用.由于海洋环境的复杂性,以及船只目标发动机的多样性以及噪声的存在,水声目标识别是一个困难的任务.传统的特征提取方法无法提取到足够有效的特征表示,充分地表示目标.为了解决这个问题,本文提出了一种基于改进的视觉化词袋模型的水声识别算法,通过使用视觉化词袋模型对频谱图进行高维的特征提取,然后使用了自然语言处理领域中常见的词频-逆文件频率(TF-IDF)算法来对得到的特征向量进行权重调整,然后输入到多层感知机中,对水声目标进行分类识别.实验结果表明,本文提出的识别算法取得了92.53%的正确率,相比于当前效果最好的深度玻尔兹曼机(DBM)算法有了明显的提升.

Underwater acoustic target recognition is to classify the targets through collecting the signals of the underwater acoustic targets and has very important and extensive applications in the fields of ocean exploration and monitoring technology.Due to the complexity of the marine environment,the diversity of target ship engines,and the background noise,underwater acoustic target recognition is difficult.Traditional feature extraction methods cannot extract effective feature representations to fully represent the targets.In order to solve this problem,we propose an underwater acoustic target recognition algorithm based on the improved bag of visual words.Specifically,this algorithm adopts the bag of visual words to extract high-dimensional features in a spectro gram and then adjusts the weights of the obtained feature vectors using the Term Frequency-Inverse Document Frequency(TF-IDF)algorithm commonly used in the field of natural language processing.Furthermore,the vectors are input to a Multi Layer Perceptron(MLP)to classify and recognize the underwater acoustic targets.The experimental results show that the recognition algorithm proposed in this study achieves an accuracy of 92.53%,which is a significant improvement in comparison with the best Deep Boltzmann Machine(DBM)algorithm at present.