A distributed subband valley fusion (DSVF) method for low frequency broadband target localization

Abstract

A distributed subband valley fusion method is proposed in this paper for target localization for a multi-array fusion system. Instead of employing two-step processing like the traditional bearings-only target localization (BOTL) methods, the target location estimate is directly obtained from the intersection of the estimated bearing line of each array, which is automatically produced after applying a subband valley energy detection process to the reciprocal of the steered response power over predefined grid points. Bearing information at all frequencies and ranges are used to generate the bearing line and the intersection structure, leading to higher localization accuracy in comparison with the BOTL methods. In multiple-target situations, the data association problem is solved by distinguishing true targets from the ghost ones from the power and the number of bearing lines at the corresponding intersection grid point. The method is subsequently tested by the SWellEx-96 shallow water experiment data set. Simulation and experimental results demonstrate the localization accuracy of the proposed method.

Array shape calibration with phase unwrapping techniques for highly deformed arrays

“…A fluid seabed model consists of two sediment layers with gradient sound speeds over a homogeneous half-space. The seabed model parameters can be found in [21].…”

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

“…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.…”

Underwater Target Localization Using Opportunistic Ship Noise Recorded on a Compact Hydrophone Array

“…However, other algorithms have been introduced recently. For example, in [14], the distributed sub-band valley fusion (DSVF) method is proposed for a multi-array system. The target position is estimated using the intersection of the bearing lines of the receiver arrays with respect to the target.…”