Passive Underwater Target Tracking: Conditionally Minimax Nonlinear Filtering with Bearing-Doppler Observations

Борисов, А. A.; Bosov, A. V.; Miller, Bruce W.; Miller, Gregory

doi:10.3390/s20082257

Cited by 13 publications

(9 citation statements)

References 47 publications

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“…A detailed description of the CMNF approach to estimating the state of nonlinear stochastic systems, including a thorough justification of the fact that ( 13) is the solution to the problem ( 14) and the existence of solution conditions (14), can be found in [67]. The following papers are devoted to the further application of the concept along with a comparative numerical study [68][69][70]. Full detailed research is given in [65].…”

Section: Conditionally Minimax Nonlinear Filter (Cmnf)mentioning

confidence: 99%

Navigation of Underwater Drones and Integration of Acoustic Sensing with Onboard Inertial Navigation System

Miller

2021

Drones

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The navigation of autonomous underwater vehicles is a major scientific and technological challenge. The principal difficulty is the opacity of the water media for usual types of radiation except for the acoustic waves. Thus, an acoustic transducer (array) composed of an acoustic sonar is the only tool for external measurements of the AUV attitude and position. Another difficulty is the inconstancy of the speed of propagation of acoustic waves, which depends on the temperature, salinity, and pressure. For this reason, only the data fusion of the acoustic measurements with data from other onboard inertial navigation system sensors can provide the necessary estimation quality and robustness. This review presents common approaches to underwater navigation and also one novel method of velocity measurement. The latter is an analog of the well-known Optical Flow method but based on a sequence of sonar array measurements.

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Section: Conditionally Minimax Nonlinear Filter (Cmnf)mentioning

confidence: 99%

Navigation of Underwater Drones and Integration of Acoustic Sensing with Onboard Inertial Navigation System

Miller

2021

Drones

Self Cite

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“…Another type of underwater tracking utilises both bearings and Doppler measurements received from the target and is known as Doppler-bearing tracking (DBT). Due to their importance in underwater target tracking, both BOT and DBT have received considerable attention in the literature [7][8][9][10][11]. Since the main aim in the solution to this problem is to estimate the position and velocity of a moving target, this problem is often known as target motion analysis (TMA) [2,7].…”

Section: Introductionmentioning

confidence: 99%

Tracking an Underwater Object with Unknown Sensor Noise Covariance Using Orthogonal Polynomial Filters

Kumar

Bhaumik

Arulampalam

2022

Sensors

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In this manuscript, an underwater target tracking problem with passive sensors is considered. The measurements used to track the target trajectories are (i) only bearing angles, and (ii) Doppler-shifted frequencies and bearing angles. Measurement noise is assumed to follow a zero mean Gaussian probability density function with unknown noise covariance. A method is developed which can estimate the position and velocity of the target along with the unknown measurement noise covariance at each time step. The proposed estimator linearises the nonlinear measurement using an orthogonal polynomial of first order, and the coefficients of the polynomial are evaluated using numerical integration. The unknown sensor noise covariance is estimated online from residual measurements. Compared to available adaptive sigma point filters, it is free from the Cholesky decomposition error. The developed method is applied to two underwater tracking scenarios which consider a nearly constant velocity target. The filter’s efficacy is evaluated using (i) root mean square error (RMSE), (ii) percentage of track loss, (iii) normalised (state) estimation error squared (NEES), (iv) bias norm, and (v) floating point operations (flops) count. From the simulation results, it is observed that the proposed method tracks the target in both scenarios, even for the unknown and time-varying measurement noise covariance case. Furthermore, the tracking accuracy increases with the incorporation of Doppler frequency measurements. The performance of the proposed method is comparable to the adaptive deterministic support point filters, with the advantage of a considerably reduced flops requirement.

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“…Tracking of multiple moving targets with Doppler measurements in multi-sensor systems has attracted interest from researchers during the past few decades [1][2][3][4][5][6][7][8][9]. Compared with other forms of measurements, Doppler is attractive for a number of reasons.…”

Section: Introductionmentioning

confidence: 99%

Generalized Labeled Multi-Bernoulli Multi-Target Tracking with Doppler-Only Measurements

Zhu

Mallick²,

Liang

et al. 2022

Remote Sensing

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The paper addresses the problem of tracking multiple targets with Doppler-only measurements in multi-sensor systems. It is well known that the observability of the target state measured using Doppler-only measurements is very poor, which makes it difficult to initialize the tracking target and produce the target trajectory in any tracking algorithm. Within the framework of random finite sets, we propose a novel constrained admissible region (CAR) based birth model that instantiates the birth distribution using Doppler-only measurements. By combining physics-based constraints in the unobservable subspace of the state space, the CAR based birth model can effectively reduce the ambiguity of the initial state. The CAR based birth model combines physics-based constraints in the unobservable subspace of the state space to reduce the ambiguity of the initial state. We implement the CAR based birth model with the generalized labeled multi-Bernoulli tracking filter to demonstrate the effectiveness of our proposed algorithm in Doppler-only tracking. The performance of the proposed approach is tested in two simulation scenarios in terms of the optimal subpattern assignment (OSPA) error, OSPA(2) error, and computing efficiency. The simulation results demonstrate the superiority of the proposed approach. Compared to the approach taken by the state-of-the-art methods, the proposed approach can at most reduce the OSPA error by 58.77%, reduce the OSPA(2) error by 43.51%, and increase the computing efficiency by 9.56 times in the first scenario. In the second scenario, the OSPA error is reduced by 62.80%, the OSPA(2) error is reduced by 43.65%, and the computing efficiency is increased by 2.61 times at most.

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Passive Underwater Target Tracking: Conditionally Minimax Nonlinear Filtering with Bearing-Doppler Observations

Cited by 13 publications

References 47 publications

Navigation of Underwater Drones and Integration of Acoustic Sensing with Onboard Inertial Navigation System

Navigation of Underwater Drones and Integration of Acoustic Sensing with Onboard Inertial Navigation System

Tracking an Underwater Object with Unknown Sensor Noise Covariance Using Orthogonal Polynomial Filters

Generalized Labeled Multi-Bernoulli Multi-Target Tracking with Doppler-Only Measurements

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