LOFAR/DEMON grams compression method for passive sonars

J. Ahn, Hyeon‑Deok Cho, Donghoon Shin, Taek-ik Kwon, Gwangtae Kim
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引用次数: 0

Abstract

LOw Frequency Analysis Recording (LOFAR) and Demodulation of Envelop Modulation On Noise (DEMON) grams are bearing-time-frequency plots of underwater acoustic signals, to visualize features for passive sonar. Those grams are characterized by tonal components, for which conventional data coding methods are not suitable. In this work, a novel LOFAR/DEMON gram compression algorithm based on binary map and prediction methods is proposed. We first generate a binary map, from which prediction for each frequency bin is determined, and then divide a frame into several macro blocks. For each macro block, we apply intra and inter prediction modes and compute residuals. Then, we perform the prediction of available bins in the binary map and quantize residuals for entropy coding. By transmitting the binary map and prediction modes, the decoder can reconstructs grams using the same process. Simulation results show that the proposed algorithm provides significantly better compression performance on LOFAR and DEMON grams than conventional data coding methods.
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无源声纳LOFAR/DEMON图压缩方法
低频分析记录(LOFAR)和噪声包络调制解调(DEMON)图是水声信号的承载时频图,用于可视化被动声呐的特征。这些克具有音调成分的特征,传统的数据编码方法不适合这种特征。本文提出了一种基于二值映射和预测方法的LOFAR/DEMON图像压缩算法。我们首先生成一个二值映射,从中确定每个频率bin的预测,然后将帧划分为几个宏块。对于每个宏块,我们采用内部和内部预测模式并计算残差。然后,我们对二值映射中的可用bin进行预测,并对残差进行量化以进行熵编码。通过传输二进制映射和预测模式,解码器可以使用相同的过程重建图。仿真结果表明,该算法对LOFAR图和DEMON图的压缩性能明显优于传统的数据编码方法。
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来源期刊
CiteScore
0.60
自引率
50.00%
发文量
1
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