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The detection of a target echo in a sonar image is usually a difficult task since the reverberation, consisting of a large number of spurious echoes, generates a lot of false alarms. In this paper, we propose two new detectors derived from image processing algorithms. These detectors are respectively based on a morphological and a statistical contrast. Each detector only requires setting a few parameters. This setting is done using some prior knowledge about the data (shape of the emitted signal and the used antenna, characteristics of the reverberation). Nevertheless, an extensive statistical study of the detection performances proves that the proposed methods are robust and that even an imprecise setting of the parameters leads to satisfactory results. Applied to the real data, these detectors and their sequential combination lead to a significant improvement on the performances: The false alarm rate is drastically reduced while the detection probability is preserved. Based on different contrasts, these detectors have complementary behaviors. Therefore, a further improvement is achieved by a fusion of the different results to classify the remaining echoes as whether spurious or true detection. 相似文献
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Detection in the presence of reverberation is often difficult in active sonar, due to the reflection/diffusion/diffraction of the transmitted signal by the ocean surface, ground, and volume. A modelization of reverberation is often used to improve detection because classical algorithms are inefficient. A commonly used reverberation model is colored and nonstationary noise. This model leads to elaborate detection algorithms which normalize and whiten reverberation. In this paper, we focus on a more deterministic model which considers reverberation as a sum of echoes issued from the transmitted signal. The Principal Component Inverse (PCI) algorithm is used with this model to estimate and delete the reverberation echoes. A rank analysis of the observation matrix shows that PCI is efficient in this configuration under some conditions, such as when the transmitted signal is Frequency Modulated. Both methods are validated with real sonar surface reverberation noise. We show that whitening has poor performance when reverberation and target echo have the same properties, while PCI maintains the same performance whatever the reverberation characteristics. Further, we extend the algorithms to spatio-temporal data. We propose a new algorithm for PCI which allows better echo separation. This new method is shown to be more efficient on real spatio-temporal data 相似文献
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