Sonar Networks Research Articles

Efficient configuration of heterogeneous multistatic sonar networks: A mixed-integer linear programming approach

The work undertaken in this paper pertains to the optimal spatial configuration of a heterogeneous Wireless Sensor Network (WSN) for the Area Coverage (AC) problem. Specifically, this research falls under the heading of Anti-Submarine Warfare (ASW) with an emphasis on active sonar systems and, more pointedly still, on a specific type of sensor: sonobuoys (portmanteau word formed by “sonar” and “buoy”). These buoys are further divided into three main categories: transmitter-only (Tx), receiver-only (Rx) and transmitter–receiver (TxRx). In this paper, we will therefore try to determine the geographical location of the different buoys comprising a Multistatic Sonar Network (MSN), special case of WSN, so as to maximize the overall surface area covered. To do this, we discretize an Area of Interest (AoI) into regular cells using bathymetric and altimetric data, and place a deployment position and a fictitious target at the center of each cell so that we can evaluate the network’s performance. More precisely, we are taking into account a limited number of sensors (buoys) with possible pairwise incompatibilities, variable performances, probabilistic detection models, an adverse masking effect (direct blast) as well as coastlines features. Finally, in order to solve this problem, we have developed several efficient Mixed-Integer Linear Programs (MILPs), all of which have been thoroughly tried-and-tested on a benchmark set of 100 instances derived from real elevation data. This has led us to identify an ideal model, i.e. one that is significantly better than all the others in the statistical sense.

An improved two-phase heuristic for active multistatic sonar network configuration

An active sonar system consists of a source emitting a sound pulse (ping) and a receiver listening to the reflection of the wave on a target, known as the echo. Such a system is further divided into two distinct configurations. The first one, named monostatic, is made up of a collocated source and receiver, while the second one, referred to as bistatic, is based on a non-collocated source and receiver. To this extent, a Multistatic Sonar Network (MSN) is thus comprised of a set of sources and receivers deployed across a given Area of Interest (AoI), which, taken pairwise, form sonar systems in monostatic and/or bistatic configuration. In this paper, we therefore propose an efficient two-phase greedy heuristic to solve the Area Coverage (AC) problem in the scope of MSNs, a special case of Wireless Sensor Networks (WSNs), while taking into account existing coastlines. For this problem, the objective is to determine the optimal spatial layout of the MSN, i.e. the one that maximize the overall coverage of the AoI with regard to a limited number of sensors and a given probabilistic detection model. Furthermore, we use a Mixed Integer Linear Program (MILP) from the literature as a reference for the numerical experiments conducted on a dataset of diversified instances. The latter were specifically derived from Digital Elevation Models (DEMs) of AoIs selected throughout the globe and in such a way as to encompass a wide spectrum of peculiar geometric situations.

Coastal hydroacoustic surveillance

Coastal hydroacoustic surveillance network is necessary to identify submarines in due time and to have an anti-submarine protection at shores. A solution to detect, localize and track silent submarines in coastal waters is one to build a multi-static sonar network (MSN), which offers a numerous advantage compared to a monostatic network.

Robust Model-Dependent Poisson Multi Bernoulli Mixture Trackers for Multistatic Sonar Networks

This work proposes a robust tracker based on the Poisson Multi Bernoulli Mixture (PMBM) filter for multistatic sonar networks (MSNs) systems. The PMBM based trackers estimate the number of targets and provide the target information via Bernoulli and Poisson Point Processes. The PMBM based trackers handle existing tracks, undetected targets, and new births separately at each computation step by using these two processes together. In practice, the PMBM tracker aims to initiate the track as soon as possible and maintain the track continuity. Initiating track and maintaining track continuity is hard in challenging underwater environments without adapting the algorithm to changing environmental conditions. This paper uses the adaptive measurement-driven birth process and multistatic acoustic model-dependent probability of detection specifications. The adaptive measurement-driven birth process improves the robustness of the track initiation, and the multistatic acoustic model-dependent probability of detection advances the track continuity through the transition regions. These contributions to the PMBM tracker make it robust in terms of tracker performance in challenging underwater environments and acoustic transition regions where it is hard to get an accurate measurement.

Optimal source placement for point coverage in active multistatic sonar networks

AbstractThe allocation of underwater sensors for tracking, localization, and surveillance purposes is a fundamental problem in anti‐submarine warfare. Inexpensive passive receivers have been heavily utilized in recent years; however, modern submarines are increasingly quiet and difficult to detect with receivers alone. Recently, the idea of deploying noncollocated sources and receivers has emerged as a promising alternative to purely passive sensor fields and to traditional sonar fields composed of collocated sources and receivers. Such a multistatic sonar network carries a number of advantages, but it also brings increased system complexity resulting from its unusual coverage patterns. In this work, we study the problem of optimally positioning active multistatic sonar sources for a point coverage application where all receivers and points of interest are fixed and stationary. Using a definite range sensor model, we formulate exact methods and approximation algorithms for this problem and compare these algorithms via computational experiments. We also examine the performance of these algorithms on a discrete approximation of a continuous area coverage problem and find that they offer a significant improvement over two types of random sensor deployment.

Robust Target Tracking with Multi-Static Sensors under Insufficient TDOA Information.

This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated using TDOA (Time Difference of Arrival) measurements. However, since the sensor network may obtain insufficient and inaccurate TDOA measurements due to ambient noise and other harsh underwater conditions, target tracking performance can be significantly degraded. We propose a robust target tracking algorithm designed to operate in such a scenario. First, track management with track splitting is applied to reduce performance degradation caused by insufficient measurements. Second, a target location is estimated by a fusion of multiple TDOA measurements using a Gaussian Mixture Model (GMM). In addition, the target trajectory is refined by conducting a stack-based data association method based on multiple-frames measurements in order to more accurately estimate target trajectory. The effectiveness of the proposed method is verified through simulations.

Optimizing source and receiver placement in multistatic sonar networks to monitor fixed targets

Multistatic sonar networks consisting of non-collocated sources and receivers are a promising development in sonar systems, but they present distinct mathematical challenges compared to the monostatic case in which each source is collocated with a receiver. This paper is the first to consider the optimal placement of both sources and receivers to monitor a given set of target locations. Prior publications have only considered optimal placement of one type of sensor, given a fixed placement of the other type. We first develop two integer linear programs capable of optimally placing both sources and receivers within a discrete set of locations. Although these models are capable of placing both sources and receivers to any degree of optimality desired by the user, their computation times may be unacceptably long for some applications. To address this issue, we then develop a two-step heuristic process, Adapt-LOC, that quickly selects positions for both sources and receivers, but with no guarantee of optimality. Based on this, we also create an iterative approach, Iter-LOC, which leads to a locally optimal placement of both sources and receivers, at the cost of larger computation times relative to Adapt-LOC. Finally, we perform computational experiments demonstrating that the newly developed algorithms constitute a powerful portfolio of tools, enabling the user to slect an appropriate level of solution quality, given the available time to perform computations. Our experiments include three real-world case studies.

Sensor placement in active multistatic sonar networks

The idea of deploying noncollocated sources and receivers in multistatic sonar networks (MSNs) has emerged as a promising area of opportunity in sonar systems. This article is one of the first to address point coverage problems in MSNs, where a number of points of interest have to be monitored in order to protect them from hostile underwater assets. We consider discrete “definite range” sensors as well as various diffuse sensor models. We make several new contributions. By showing that the convex hull spanned by the targets is guaranteed to contain optimal sensor positions, we are able to limit the solution space. Under a definite range sensor model, we are able to exclude even more suboptimal solutions. We then formulate a nonlinear program and an integer nonlinear program to express the sensor placement problem. To address the nonconvex single-source placement problem, we develop the Divide Best Sector (DiBS) algorithm, which quickly provides an optimal source position assuming fixed receivers. Starting with a basic implementation of DiBS, we show how incorporating advanced sector splitting methods and termination conditions further improve the algorithm. We also discuss two ways to use DiBS to find multiple source positions by placing sensors iteratively or simultaneously. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 287–304, 2017

Cascading polar coding and LT coding for radar and sonar networks

In radar and sonar networks, heavy clutter and noise have generated strong impairments to information transmission and processing. Coding techniques have been widely used to cope with various channel noises and thereby to improve transmission performance. In the study, we investigate a novel cascaded coding scheme for systems operating in adverse electromagnetic environments, e.g., underwater acoustic communications (UAC), where the impulsive noise will be inevitable and the bit error ratio (BER) of receiver will be deteriorated greatly. Our cascaded encoding scheme employs the polar code as inner code while the Luby transform (LT) code as outer code. Inspired on a novel conception of channel polarization, the polar code uses a group of idea sub-channels to carry the useful information, while let other bad sub-channels bear no information. By cascading the outer LT code which accomplishes some parity check to the input of inner code, the performance of inner polar code is improved, as the decoding of polar code relies on a successive cancelation technique which is relatively sensitive to initial input. Error correcting performance of the new cascaded code is studied under impulsive noise characterized by a Middleton Class-A model. Simulations validate the proposed cascaded coding scheme. Compared with the popular low-density parity check (LDPC) code, the cascaded scheme can significantly improve the BER performance in the presence of impulsive interference, which also surpasses another cascaded code that is proved to be effective in impulsive noise channel, i.e., cascaded LDPC and LT coding scheme.

Two-stage active sonar network track-before-detect processing in a high clutter harbor environment

Reliable active acoustic detection and tracking of small targets while minimizing the number of false alerts is challenging in a shallow, multipath-inducing, high-clutter harbor environment. These targets often exhibit low target strength and the acoustic clutter fields can be dense and highly dynamic. One approach to detect and track targets in this environment involves a two-stage tracker due to the processing gain required to continually track a weak target in such a significant clutter field. The first stage uses more than a single frame of observations to not only initiate tracks but also to contribute to track maintenance. The second tracking stage is initiated by the tentative target tracks extracted from the first stage. Track segment association algorithms are employed to combat periods or locations track intermittency that inhibit detection and cause tracks to fail and restart. This paper describes the results of this two-stage approach to track-before-detect processing applied to a networked harbor surveillance active acoustic detection and tracking system that consists of a large number of relatively simple nodes, employs a windowed Hough-Transform tracker at the beam level, and a Kalman-based, multi-object tracker at the single- and multi-node levels.

Practical applications of track segment association algorithms to an active sonar network for underwater port surveillance

Acoustic detection and tracking of small (swimmer-size) targets while minimizing the incidents of false alerts can be challenging in a shallow, multipath, high-clutter harbor environment. One common problem involves track intermittency where periods or locations of high clutter or multipath interference inhibit detection and cause tracks to fail and restart. This paper describes the results of applying track segment association and related algorithms from airborne and ground radar applications [e.g., Zhang and Bar-Shalom, IEEE Trans. Aerosp. Electron. Syst. 47(3), 1899–1914 (2011)] to an active sonar network for underwater port surveillance [Edelson et al., J.Acoust. Soc. Am. 129(4), 2598 (2011), Stein et al., J. Acoust. Soc. Am. 121(5), 3084 (2007)] using real-world swimmer and synthetic target data.

A distributed micro sonar network for marine mammal monitoring.

The design of a distributed micro sonar network (DMSN) is presented. The DMSN is a miniaturized, low‐power, and high‐frequency version of the swimmer detection sonar network used to detect and track marine mammals at ranges out to 500 m. The intent is to use it for monitoring during potentially harmful military and commercial activities such as high‐powered Navy sonar, oil and gas exploration, explosive removal of offshore structures, seismic exploration for research and oil and gas, pile driving to install maritime structures including offshore wind farms, and marine hydrokinetic energy devices such as free turbines. The system will consist of multiple single‐beam sonars, each transmitting and receiving a unique signal in a narrow beam. The hardware and software will initially be ported to a Xilinx FPGA, with future versions done within a custom made ASIC. Initial hardware and software research and development will be described, as well as the work done to create computationally efficient signal processi...

Marine life detection and tracking using a swimmer detection sonar network.

There is a growing need for real‐time monitoring of marine life and floating debris during a wide variety of commercial operations. These include seismic exploration for oil and gas, explosive removal of offshore structures, pile driving for the installation of marine structures including offshore wind farms, and the operation of tidal turbines and wave power generation devices. Active acoustics is likely the best method for monitoring where there is a high‐danger region with a limited range around the activity. The SSI Swimmer Detection Sonar Network was originally designed as a human swimmer and diver detection and tracking system. However, extensive trials have demonstrated that the system is also capable of tracking marine life ranging from a large fish or marine mammals to schools of smaller fish. Analysis of the detection characteristics and movement behavior of marine life is being conducted as a means of tracking and classification at ranges out to roughly 500 m. Real‐time identification and tracking over time may also provide insight into the habituation of marine life to these facilities in addition to serving as a protective measure. [Research supported by ONR SBIR.]

Classification of shallow water passive sonar signals using stochastic channel model.

This paper addresses the problem of classifying passive sonar signals propagating in shallow water channels. A key problem is that such signals are corrupted by noise and multipath, which we model stochastically in terms of the expected multipath and noise, and the variance of the multipath and noise. We assume that free-field (e.g., deep water) training signals are available. We show that for classification, the accuracy can be improved by not committing to an estimate of the channel or signal, but rather by marginalizing out the channel and noise uncertainty. Specifically, we propose variants of the quadratic discriminant analysis (QDA) classifier and the support vector machine classifier (SVM) that probabilistically account for the unknown channel effects, and which avoid ill-posed deconvolution. The proposed classifiers can work either directly on a time signal or on subband power features. Results on simulated data and real Bowhead whale vocalizations show that we can significantly improve classification performance over traditional methods over a range of signal-to-noise ratios. In addition, we will discuss the case where the classification decision can be made by multiple cooperating receivers, such as in a sonar network.

Impact of the harbor environment on the performance of a swimmer.

Over the past several years, Scientific Solutions has been developing the Swimmer Detection Sonar Network (SDSN). It is a novel system that networks individual air‐backed parabolic transducers which both transmit and receive in narrow beams. In this paper, system performance issues as related to operating in the port/harbor environment will be discussed. This includes effects due to clutter, noise, multipath propagation, refraction, and tides. Data collected from deployments in two different harbor environments will be used to explore the issues. Both harbors have similar water depths and bottom properties, but one is in a tropical climate, where the sound speed is essentially uniform year‐round, and the other is a temperate (midlatitude) climate where the sound speed varies significantly depending on the time of year. The tests in the latter environment were conducted during the summer when the sound speed was strongly downward refracting. The methods by which the SDSN design overcomes the performance issues discussed will also be explored [Work supported by ONR.]

Swimmer detection sonar network

The Swimmer Detection Sonar Network (SDSN) was designed to be a cost effective method to protect a large area from terrorist swimmers. The in-water system consists of a number of sonar nodes, each consisting of a set of air-backed parabolic dish transducers that are used to both transmit and receive in sets of independent narrow beams. Each dish/channel can transmit and process its own unique signal. The Sonar Network is therefore composed of a distributed group of nodes each providing a set of independent narrow-beam sonars. This allows for strong rejection of interfering clutter, resulting in excellent performance. The nodes/channels are also synchronized, allowing for multi-static signal processing. A suite of signal processing algorithms has been developed for automated detection, tracking, and classification, including a fine-bearing estimation that achieves approximately 1 deg accuracy. These algorithms will be discussed. Data will be shown that shows the performance against swimmers and the possibilities for bi-static detection and classification. The SDSN has been productized and is now available for wide-scale deployment. It is a cost effective means for large harbor facilities to reliably detect underwater threats at long ranges.

Using artificial bat sonar neural networks for complex pattern recognition: Recognizing faces and the speed of a moving target

Using artificial bat sonar neural networks for complex pattern recognition: Recognizing faces and the speed of a moving target