Gaussian Mixture Multitarget Multisensor Bernoulli Tracker for Multistatic Sonobuoy Fields

@article{Ristic2017GaussianMM,
  title={Gaussian Mixture Multitarget Multisensor Bernoulli Tracker for Multistatic Sonobuoy Fields},
  author={Branko Ristic and Daniel Angley and Sofia Suvorova and Bill Moran and Fiona Fletcher and H. Gaetjens and Sergey Simakov},
  journal={Iet Radar Sonar and Navigation},
  year={2017},
  volume={11},
  pages={1790-1797}
}
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… 
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8 Citations
Background Citations
2
Methods Citations
4

8 Citations

Multisensor Poisson Multi-Bernoulli Filter for Joint Target–Sensor State Tracking

This paper presents a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states and demonstrates the performance benefits of joint vehicle-target state tracking.

Bistatic sonobuoy deployment strategies for detecting stationary and mobile underwater targets

This study uses integral geometry and geometric probability concepts to derive analytic expressions for the optimal source and receiver separation distances to maximize the detection probability of a submerged target.

Covariance Cost Functions for Scheduling Multistatic Sonobuoy Fields

This paper considers 5 different cost functions derived from the predicted error covariance matrix of the track and shows there is a trade-off between position and velocity error performance such that no one cost function is superior in both.

Scheduling of Multistatic Sonobuoy Fields Using Multi-Objective Optimization

  • C. GilliamB. Ristic S. Simakov
  • Computer Science
    2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2018
A novel scheduling framework based on multi-objective optimization poses the two tasks of the sonobuoy field-tracking and searching-as separate, competing, objective functions and proposes a characterization of scheduling based on Pareto optimality.

Measurement-Driven Multi-Target Multi-Bernoulli Filter

A measurement-driven multi-target multi-Bernoulli (MeMBer) filter which modifies the MeMBer filter by the measurements information is proposed and can improve the stability and accuracy of the estimates and does not need the high detection probability hypothesis.

A Local Charged Particle Swarm Optimization to track an underwater mobile source

An innovative tracking method inspired by the Particle Swarm Optimization (PSO) algorithm that is adapted to range-dependant communication that characterizes the underwater context and includes flocking parameters is proposed.

Passive sonar target classification using multi-layer perceptron trained by salp swarm algorithm

Multisensor Poisson Multi-Bernoulli Filtering with Uncertain Sensor States

A Poisson multi-Bernoulli MTT filter, which models the uncertain sensor state in feature tracking as well as the reduction of sensor state uncertainty in a multisensor scenario compared to a per sensor Kalman filter.

References

SHOWING 1-10 OF 16 REFERENCES

Clustered Mixture Particle Filter for Underwater Multitarget Tracking in Multistatic Active Sonobuoy Systems

The problem of multitarget tracking in underwater multistatic active sonobuoy systems is challenging because of the large number of false contacts and multiple reflections that reach the receivers.…

Sensor scheduling for target tracking in large multistatic sonobuoy fields

  • D. AngleyS. Suvorova S. Simakov
  • Computer Science
    2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2017
A new long term sensor scheduling algorithm for sonobuoy fields is described, called the continuous probability states algorithm, that 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.

Target tracking via multi-static Doppler shifts

This article studies the problem of joint detection and tracking of a target using multi-static Doppler-only measurements. The assumption is that in the surveillance volume of interest a single…

Multi-Sensor Joint Detection and Tracking with the Bernoulli Filter

A filter for joint detection and tracking of a single target using measurements from multiple sensors under the presence of detection uncertainty and clutter is proposed and a closed-form solution for the linear-Gaussian model is derived and an analytic implementation is proposed for nonlinear models based on the unscented transform.

The GM-CPHD Tracker Applied to Real and Realistic Multistatic Sonar Data Sets

The Gaussian mixture-cardinalized probability hypothesis density (GM-CPHD) tracker was applied to three real and simulated multistatic sonar data sets. The goal was to test the versatility of the…

Bayesian Multi-Object Filtering With Amplitude Feature Likelihood for Unknown Object SNR

It is shown that the reliable estimation of the SNR requires a significant number of measurements, and so an alternative approach is proposed for situations where theSNR is unknown, and this can be incorporated into approximate multiple-object filters derived from FISST, including probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters.

Bayesian Tracking in Underwater Wireless Sensor Networks With Port-Starboard Ambiguity

This work shows that optimal disambiguation can be pursued by deriving the full Bayesian posterior distribution of the target state by means of particle filtering techniques, and corroborated by simulations that show the effectiveness of the particle-filtering tracking.

Multi-target tracking in clutter without measurement assignment

This paper presents a new approach for multitarget tracking in a cluttered environment that enumerates all possible joint measurement-to-target assignments and calculates the a posteriori probabilities of each of these joint assignments, e.g. J(I)PDA and MHT.

Multi-Stage Multiple-Hypothesis Tracking

Two multi-stage MHT architectures are introduced and compared to single-stage, trackwhile-fuse processing and it is found that in some applications multi- stage MHT processing outperforms single-Stage M HT processing.

A Tutorial on Bernoulli Filters: Theory, Implementation and Applications

The theory of Bernoulli filters is reviewed as well as their implementation for different measurement models, backed up by applications in sensor networks, bearings-only tracking, passive radar/sonar surveillance, visual tracking, monitoring/prediction of an epidemic and tracking using natural language statements.