Low-frequency broadband sound source localization using an adaptive normal mode back-propagation approach in a shallow-water ocean

Abstract

A variety of localization methods with normal mode theory have been established for localizing low frequency (below a few hundred Hz), broadband signals in a shallow water environment. Gauss-Markov inverse theory is employed in this paper to derive an adaptive normal mode backpropagation approach. Joining with the maximum a posteriori mode filter, this approach is capable of separating signals from noisy data so that the back-propagation will not have significant influence from the noise. Numerical simulations are presented to demonstrate the robustness and accuracy of the approach, along with comparisons to other methods. Applications to real data collected at the edge of the continental shelf off New Jersey, USA are presented, and the effects of water column fluctuations caused by nonlinear internal waves and shelfbreak front variability are discussed.

Ranging bowhead whale calls in a shallow-water dispersive waveguide

“…A broadband remote source's location can then be estimated by matching these amplitudes and phases with those obtained from modeling propagating modes, a technique known as matched-mode processing (Yang, 1989;Krolik, 1992;Collison and Dosso, 2000). The concept has also been called adaptive modal back-propagation and has recently been applied to tracking sei whales along the U.S. East Coast (Lin et al, 2012). A major challenge of any mode filtering technique is that the vertical array must span sufficient aperture in the water column to exploit the orthogonal relationships between the modes.…”

Long distance passive localization of vocalizing sei whales using an acoustic normal mode approach

“…Using these eigenvalues and mode functions, we can study how well we can resolve the source depth in the vertical using modal ratios, i.e., obtain the vertical resolution length versus depth. We have done such studies, using both this isovelocity profile and more realistic profiles (Lin et al, 2012). Due to the vertically oscillatory nature of the mode functions with depth, local minima and maxima can be found in the resolution kernel.…”

“…The range estimation is done using adaptive normal mode back propagation assuming adiabatic mode theory (Lin et al, 2012), and we will not discuss the details here. However, we are interested in a basic description and error analysis of the technique and for those we can use basic range independent mode theory to understand the physical issues.…”

“…Also, a by-product of this localization is that we can remove the effects of mode dispersion and attenuation on the received signal and recover the original signal as produced at the source. The details of the back-propagation method can be referred to Lin et al (2012), and we will discuss the theoretical basis for the localization algorithms and their associated errors in this paper.…”

Fruit classification by biogeography‐based optimization and feedforward neural network

“…Many methods exist for training the weights/biases of the single‐hidden‐layer FNN, which consists of BP (Shojaee et al, ), adaptive BP (Lin et al, ), momentum BP (MBP) (Karmakar et al, ), simulated annealing (SA) (Manoochehri & Kolahan, ), genetic algorithm (GA) (Chandwani et al, ), particle swarm optimization (PSO) (Momeni et al, ; Zhang et al, ), firefly algorithm (Gholizadeh, ), artificial bee colony (ABC) (Awan et al, ), Tabu search (Peyghami & Khanduzi, ), and ant colony optimization (ACO) (Mohammadhassani et al, ). However, the BP, adaptive BP, MBP, SA, GA, PSO, FA, ABC, Tabu search, and ACO algorithms either are easily trapped into the local best or require expensive computational resources (Wang et al, ).…”