Development of beamforming methods for towed sonar multiplet line arrays is stimulated by their increased use in undersea surveillance. Such methods employ various forms of narrowband optimal beamforming to resolve port/starboard ambiguity present when the array is conventionally beamformed. In this paper, we depart from the purely narrowband approach and discuss methods for port/starboard ambiguity rejection (PSAR) using a broadband formulation. The proposed methodology relies on subband beamforming for which we use non-adaptive variants of beamspace Minimum Variance Distortionless Response (MVDR) and the Linear Constraint Minimum Variance (LCMV) beamformers. We provide detailed description and comparison of both subband methods and establish a connection between them. The paper provides assessment of PSAR properties of the developed broadband beamformers using outputs of signal processing of both simulated and experimental sonar data. Experimental data for this work were obtained through the participation in the Littoral Continuous Active Sonar (LCAS) trials.

Sonobuoy fields, comprising a network of sonar transmitters and receivers, are used to search for and track underwater targets. Although normally such fields are operated from a maritime patrol aircraft, automated scheduling and processing creates opportunities for employing them as autonomous sensor systems. The automated search mechanism considered in this work is controlled by modelling the presence of undetected threats in an Operational Area (OA) using a spatial probability density function (PDF), known as a threat map. The algorithm decides how to schedule waveform transmissions, known as pings, to efficiently search and clear the OA. A conventional approach is to update the threat map based on just the characteristics of the sonobuoy field and switch to a separate metric to track a target after track confirmation. In this study we address the phase when there are potential contacts which cannot yet be promoted to confirmed tracks. We develop a mechanism for probing the associated areas of interest while still remaining in the threat map driven search scheduling. To this end, we propose reinitialising the threat map after each transmission using an augmented PDF, where unconfirmed tracks are represented by weighted Gaussians. Simulations show that this approach significantly improves search performance, reducing the number of pings required to confirm a track, distance from a confirmed track to the target and the proportion of falsely confirmed tracks.

Sonobuoy fields, comprising a network of sonar transmitters and receivers, are used to find and track underwater targets. For a given environment and sonobuoy field layout, the performance of such a field depends on the scheduling, that is, deciding which source should transmit, and which waveform should be transmitted at any given time. In this paper, we explore the choice of cost function used in myopic scheduling and its effect on tracking performance. Specifically, we consider 5 different cost functions derived from the predicted error covariance matrix of the track. Importantly, our cost functions combine both positional and velocity covariance information to allow the scheduler to choose the optimum source-waveform action. Using realistic multistatic sonobuoy simulations, we demonstrate that each cost function results in a different choice of source-waveform actions, which in turn affects the performance of the scheduler. In particular, we show there is a trade-off between position and velocity error performance such that no one cost function is superior in both.

Sonobuoy fields, comprising a network of transmitters and receivers, are commonly deployed to find and track underwater targets. For a given environment and sonobuoy field layout, the performance of such a field depends on the scheduling, that is, deciding which source should transmit, and which from a library of available waveforms should be transmitted at any given time. In this paper, we propose a novel scheduling framework based on multi-objective optimization. Specifically, we pose the two tasks of the sonobuoy field-tracking and searching-as separate, competing, objective functions. Using this framework, we propose a characterization of scheduling based on Pareto optimality. This characterization describes the trade-off between the search-track objectives and is demonstrated on realistic multistatic sonobuoy simulations.

Sonobuoy fields, consisting of many distributed emitter and receiver sonar sensors on buoys, are used to seek and track underwater targets in a defined search area. A sensor scheduling algorithm is required in order to optimise tracking performance by selecting which emitter sonobuoy should transmit in each time interval, and which waveform it should use. In this paper we describe a new long term sensor scheduling algorithm for sonobuoy fields, called the continuous probability states algorithm. This algorithm reduces the scheduling search space by keeping track of the probability that a target is undetected, rather than modelling all possible detection outcomes, which reduces the computation complexity of the algorithm. It is shown that this approach results in high quality tracking for multiple targets in a simulated sonobuoy field.

Sonobuoy fields, consisting of a large network of emitter and receiver sonar sensors on buoys, are increasingly being used for detection and tracking of underwater targets in a defined maritime area. This study presents a Gaussian mixture version of a multitarget–multisensor (MS) Bayesian‐type tracker developed specifically for multistatic sonobuoy fields. Its foundation is the optimal Bayesian MS filter for a single target in clutter. The multi target feature is incorporated using the linear‐multitarget paradigm, which is a fast and accurate approximation assuming the density of underwater targets is low. Reliable track initiation and false track discrimination for low signal‐to‐noise ratio targets are achieved using the amplitude feature of reported detections. The developed tracker is capable of processing measurements with different modalities, depending on the transmitted signal waveform. It is integrated and tested within a realistic multistatic sonar emulator developed by DST Group.

Sonobuoy fields, consisting of many distributed emitter and receiver sonar sensors on buoys, are used to seek and track underwater targets in a defined search area. The authors seek a scheduling protocol, selecting both the emitter and its waveform in each time interval that optimises tracking performance. This study describes a stationary scheduling algorithm for sonobuoy fields called the continuous probability states algorithm. The algorithm replaces a full partially observed Markov decision process by a computationally feasible Markov decision process by focusing on probability of target detection. This approach is shown to result in high-quality tracks for multiple targets in a realistic simulation of a sonobuoy field.

Assumed here is a multi-static sonar system consisting of a distributed field of emitter and receiver sonobuoys within a defined search area. The field is deployed to search for and estimate the location, heading, and speed of underwater targets within the search area. We describe a method for optimizing, over a long time horizon, choice of transmission locations, and of waveforms from a small waveform library to detect and estimate the states of multiple targets. The method used is based on Markov Decision Processes as a structure to describe the overall scheduling problem. The key innovations are in simplifications of the state space used in seeking the optimal schedule, which enable a computationally feasible algorithm.

The paper presents the latest stage in the development of a robust multitarget tracker to be deployed in a large multistatic sonobuoy system. The major technical challenges are: the association of multiple measurements from different receivers for every ping; target detection dependence on the source-target-receiver geometry, ping properties, target properties and acoustic propagation conditions; and the combination of different measurement modalities (with and without the Doppler). The tracker is formulated as the multi-sensor linear-multitarget Bernoulli filter and, due to the highly nonlinear measurements, implemented using the sequential Monte Carlo method. The amplitude of detections is exploited for improved track initiation and false track discrimination. Numerical simulations demonstrate its robust performance.

Optimal beamforming on synthetic noise and interference is a flexible and intuitive technique for shaping beam patterns. In this method, suppression of arrivals from undesirable directions is achieved through introduction of synthetic interferences and optimal beamforming using the resulting noise-interference covariance matrix. We apply this approach to a general multiplet line array and test the algorithm on representative multi-channel time-series obtained for a quadruplet line array.

The Threat Probability Density Map displays the outcomes of the search effort prior to detection and is a digital representation of the probability density function of location of the existing undetected threat. The Threat Map readily provides such diagnostics as the probabilities of the threat being present in different areas of interest and this information can be utilised in selection of sensor field controls. In this work we consider a GPU acceleration of the Monte-Carlo technique for Threat Map computation and discuss application to sonobuoys.

We describe a technique for detection and tracking of multiple targets using a multistatic sonobuoy array. The innovation in the proposed algorithm is the use of a clustering step, posed as Bayesian mixture estimation, to produce Cartesian position measurements. These are passed to a sequential Monte Carlo approximation of the multiple hypothesis tracker. The improvement offered by the proposed algorithm compared to an existing algorithm is demonstrated in a simulation analysis.

In this paper, we develop a tracking framework for multistatic sonar ping scheduling. This framework provides a unified approach to surveillance and tracking over a search area. The framework is used to dynamically schedule active sonar transmissions from a set of available sources and transmission parameters, including waveforms. This dynamic scheduling will be applied to the problem of detecting and tracking a target in a multistatic sonobuoy field. Results will be presented comparing the tracking performance using our ping schedules with alternative scheduling heuristics.

Location of a target detected by an air-deployed multistatic sonar can be determined using various cross fixes based on signal/echo time-of-arrival data and bearings from the sonobuoys involved in the contact. The resultant multiple measurements of the target location generally differ from each other and are not precise due to the input errors such as the sonobuoy positioning, target bearing, and temporal errors. To combine these individual measurements into an estimate that has the minimal mean square error, we employ the Wiener filter. We examine how this estimation error depends on the input errors and assess the extent to which the reduction of sonobuoy positioning errors can improve contact localization.

Marine organisms with gas inclusions, such as fish with swim bladders and bubble-carrying plankton, can scatter sound strongly thus contributing to volume reverberation of an active sonar and possibly introducing distortion to its pulses. Numerical evaluation of scattering effects from these objects is often based on reduction of their shapes to simple geometries, such as spheres or cylinders. In this work we use a viscous compressible spherical shell model with a gas inclusion to obtain a parameterisation of the frequency dependence of the scattering cross section of individual scatterers in terms of their effective size and material properties. We consider the range of sonar frequencies and scatterer sizes for which the contribution of non-monopole spherical modes becomes significant. Graphical interfacing of access to model parameters is discussed and an assessment of the characteristics of the echo returns from an ensemble of scatterers in frequency and time domains is given.