Histogram-PMHT for fluctuating target models

The histogram-probabilistic multi-hypothesis tracker (H-PMHT) is an efficient multi-target tracking approach to the track-before-detect problem. A fundamental feature of the H-PMHT is the discretisation of the energy in the sensor data and the assumption of a multinomial measurement model on the resulting image. A problem with the H-PMHT is that the multinomial measurement model fails to account for fluctuations in the target amplitude, which can degrade performance in realistic sensing conditions. The authors propose an alternative measurement model based on a Poisson mixture process to allow for fluctuating target amplitudes. Simulations show that this new approach, referred to as the Poisson H-PMHT, gives more accurate signal-tonoise ratio estimates than the standard H-PMHT, particularly for scenarios featuring targets with fluctuating amplitude.Han X. Gaetjens, Samuel J. Davey, Sanjeev Arulampalam, Fiona K. Fletcher, Cheng-Chew Li

Sensor scheduling for target tracking in large multistatic sonobuoy fields

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

Scheduling of Multistatic Sonobuoy Fields Using Multi-Objective Optimization

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

Non-myopic sensor scheduling for multistatic sonobuoy fields

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

Gaussian Mixture Multitarget Multisensor Bernoulli Tracker for Multistatic Sonobuoy Fields

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 de- fined maritime area. The paper presents a Gaussian mixture version of a multitarget multisensor Bayesian-type tracker developed specifically for multistatic sonobuoy fields. Its foundation is the optimal Bayesian multisensor filter for a single target in clutter. The multitarget feature is incorporated using the linear-multitarget (LMT) 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 SNR targets is 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