Feature Extraction for Acoustic Scattering from a Buried Target
Elastic acoustic scattering is important for buried target detection and identification. For elastic spherical objects, studies have shown that a series of narrowband energetic arrivals follow the first specular one. However, in practice, the elastic echo is rather weak because of the acoustic absorption, propagation loss, and reverberation, which makes it difficult to extract elastic scattering features, especially for buried targets. To remove the interference and enhance the elastic scattering, the de-chirping method was adopted here to address the target scattering echo when a linear frequency modulation (LFM) signal is transmitted. The parameters of the incident signal were known. With the de-chirping operation, a target echo was transformed into a cluster of narrowband signals, and the elastic components could be extracted with a band-pass filter and then recovered by remodulation. The simulation results indicate the feasibility of the elastic scattering extraction and recovery. The experimental result demonstrates that the interference was removed and the elastic scattering was visibly enhanced after de-chirping, which facilitates the subsequent resonance feature extraction for target classification and recognition.
KeywordsBuried target detection Acoustic scattering Elastic scattering De-chirping Feature extraction
The detection and recognition of underwater objects buried in the seafloor continue to be a challenge (Chen and Zhou 2012). For a buried target, the common methods can be broadly divided into two categories: imaging detection and non-imaging detection. With imaging sonar (such as synthetic aperture sonar) and image processing, a buried target can be detected and recognized. Studies have shown that the targets and clutter can be sorted according to their acoustic imaging characteristics (Waters et al. 2012b). With localized image symmetry and target strength correlation in 2D feature space, unexploded ordnance (UXO) targets can be separated from at least some non-UXO targets (Bucaro et al. 2008; Bucaro et al. 2012). Compared to imaging detection, non-imaging detection is easier for real-time process, and its requirements on the detection platform are less stringent. In addition, the detection range can be enlarged by transmitting a signal with a lower frequency and smaller grazing angle. Fishell (Fischell and Schmidt 2015) classified seabed spherical and cylindrical targets according to their rigid scattering features. Non-imaging detection implements buried target detection based on its elastic scattering. According to the resonance scattering theory, the scattering from an elastic target contains two terms: background (rigid scattering) and resonance spectrum (elastic scattering) (Junger 1952; Flax et al. 1978). The resonance spectrum reflects the material and internal construction of the target (Gaunaurd and Werby 1991; Morse et al. 1998; Tesei et al. 2002), which are crucial for buried target detection and classification. The elastic contribution to the backscattering from a target remains significant with respect to the specular echo, even when the object is deeply buried (Tesei et al. 2008). The background term has to be removed to obtain the resonance spectrum; however, this is rather difficult in practice because the transmission loss is considerable during wave insonification and propagation in seafloor sediment (Wan et al. 2006). In addition, with an active sonar, the presence of reverberation also impedes target detection. There are two popular methods for elastic scattering extraction, namely time inverse technology and the method of isolation and identification of resonance (MIIR). The time inverse technology for buried target detection was introduced by Waters (Waters et al. 2009). The procedure consists of exciting the target object with a broadband pulse, sampling the return with a finite time window, reversing the signal in time, and using this reversed signal as the source waveform for the next interrogation. It has been indicated that the spectrum of the returns rapidly converges to the dominant mode in the backscattering response, and the signal-to-noise of the target echo is enhanced for a target buried in different depths (Waters et al. 2012a; Waters and Barbone 2012; Yu et al. 2014). The MIIR, which was introduced by G. Maze (Maze et al. 1988; Maze et al. 1989), extracts elastic scattering in the time domain by transmitting a narrow pulse. Experimental results have shown that the resonance spectrum is visible with the MIIR, and it is effective for buried target detection (Maze 1991; Décultot et al. 2010).
It is well known that resonance characteristics are significant for target classification; however, the elastic scattering needs to be enhanced as it is usually disturbed by the propagation loss and the interference of reverberation. In this study, to remove the reverberation and specular echo and highlight the elastic resonant characteristics, de-chirping method (Caputi 1971) was adopted when a linear frequency modulation (LFM) signal was transmitted. An echo could be transformed into a series of signals with different frequencies, and the elastic ones could be filtered out with a band-pass filter and then recovered by remodulation. The experimental result shows that the resonance was visibly enhanced after processing, which is beneficial to the next resonance feature extraction for target classification and recognition.
2 Simulation and Analysis of Buried Target Scattering
Longitudinal wave speed
3 De-chirping Processing
In engineering, a target can be considered as a linear time-invariant system and the target echo as the system response to the incident signal. In the far field, according to the highlight model (Tang 1994), a target echo can be written as the superposition of a series of subwaves, i.e., highlights. The frequency modulation properties of specular echo and elastic scattering are basically the same (Li et al. 2015), although they have different frequency responses. To simplify the analysis, every highlight is considered as a time-delay copy of the incident signal, without considering the amplitude response and phase jump.
De-chirping the observed signal: With the known parameters of the incident signal, the scattering signal is de-chirped as mentioned above.
Band-pass filtering the de-chirped signal: The elastic components are filtered out by several band-pass filters, and the center frequencies of the filters are determined based on the peak frequencies of the de-chirped signal.
Recovering the filtered component: The elastic scattering components are recovered by remodulating the filtered signal with the transmitted signal.
4 Experimental Signal Processing
According to the result of the COMSOL simulation, the scattering of a buried sphere was consistent with the scattering when it was in free field. With a narrow bandwidth pulse, a series of narrowband energetic arrivals were observed to follow the specular arrival. However, for the propagation loss and the interference of reverberation, the elastic components which are significant for target detection and classification, were difficult to obtain. To extract and enhance the elastic scattering, the de-chirping method was adopted to address the buried target scattering. The experimental result demonstrates that with the de-chirping process, the elastic components could be improved compared with the MIIR result, and it was beneficial to the subsequent resonance feature extraction, which was crucial to the target classification and recognition.
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