Spatial Spectral Enhancement of Broadband Signals in a Towed Array Using Deconvolved Subband Peak Energy Detection

Abstract

Conventional energy detection is a robust method that is usually applied to underwater broadband acoustic signal processing for towed arrays. Due to its low resolution, the weak target detection performance of conventional energy detection is severely degraded in shallow sea environments with strong acoustical reverberation. Subband peak energy detection is an effective method to improve the display resolution of conventional energy detection. However, subband peak energy detection produces false alarms due to the presence of high sidelobe levels. In order to improve the underwater target detection performance, a deconvolved subband peak energy detection method for towed arrays is proposed in this paper. Compared with conventional beamforming, minimum-variance distortionless response with forward–backward averaging and diagonal loading algorithm and subband peak energy detection, the proposed method could robustly provide higher-resolution results and suppress the fake peaks induced by subband peak energy detection. The performance of the proposed method was evaluated with simulation results, and the sea experimental data processing results show that the proposed method is effective in engineering applications.

Weak Underwater Acoustic Target Detection and Enhancement with BM-SEED Algorithm

“…Ma et al [7] introduced SPED method into underwater acoustic broadband sonar signal energy detection, and further proposed a beam domain detection. The experiment results show that its performance is limited by the resolutions of the original BTRs; Zheng et al [8] combined SPED and image processing technology to extract the target trajectory of the BTR, while they did not solve the problem that the weak target at the intersection of the trajectory is covered; Jomon et al [9] uses a combination of MVDR and SPED for detection and tracking of fast moving targets, and proposes an efficient parallel scheme; Luo et al [10] used median filtering and order truncation averaging methods to estimate the background noise, and further determine the threshold value for peak judgment in the SPED algorithm, which can reduce the generation of false targets; Yang [11,12] used deconvolution algorithm in the underwater acoustic post-processing part, the main idea is to improve the quality of BTRs by computing its deconvolution with a point spread function (PSF); Zhao et al [13] also applied the deconvolution algorithm to SPED to reduce the generation of many false targets on traditional spatial spectral estimation methods; Zhang et al [14] proposed expanded-SPED algorithm improve cross-azimuth detection of weak targets under strong interference. Wang et al [15] proposed a target localization algorithm based on 2-D SPED, which can achieve higher localization accuracy compared with bearing-only target localization.…”