TMA from bearings and multipath time delays

Blanc‐Benon, Philippe; Jauffret, Claude

doi:10.1109/7.599251

Cited by 23 publications

(11 citation statements)

References 9 publications

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“…A common example of this model is the OTHR [9], [10]. Other applications can be found in urban target tracking [32], target tracking in shallow underwater [33] and low-elevation target tracking [34]. Thus, we can cast the measurement mode defined in (2) to each multiple propagation path.…”

Section: A Generalization To a Multipath-aided Phd Filtermentioning

confidence: 97%

“…In this paper we still adopt all of these assumptions. We further assume that the posterior intensity at scan is a GM given by (33) where the GM is used to handle the case of missing the detections in all modes. Based on (10), is given by (34) where (35) and (36) Based on (11), , which is used to calculate the PHD of the detected target, is given by (37) (38) (39) In (39) the partition weights, , which can be interpreted as the probability of partition being true, are calculated using (19).…”

Section: A Md-gm-phd Derivationmentioning

confidence: 99%

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A Multiple-Detection Probability Hypothesis Density Filter

Tang

Chen

McDonald

et al. 2015

IEEE Trans. Signal Process.

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Most conventional target tracking algorithms assume that one target can generate at most one detection per scan. However, in many practical target tracking applications, one target may generate multiple detections in one scan, because of multipath propagation, or high sensor resolution or some other reason. If the multiple detections from the same target can be effectively utilized, the performance of the multitarget tracking system can be improved. However, the challenge is that the uncertainty in the number of targets and the measurement set-to-target association will increase the complexity of tracking algorithms. To solve this problem, the random finite set (RFS) modeling and the random finite set statistics (FISST) are used in this paper. Without any extra approximation beyond those made in the standard probability hypothesis density (PHD) filter, a general multi-detection PHD (MD-PHD) update formulation is derived. It is also established in this paper that, with certain reasonable assumptions, the proposed MD-PHD recursion can function as a generalized extended target PHD or multisensor PHD filter. Furthermore, a Gaussian Mixture (GM) implementation of the proposed MD-PHD formulation, called the MD-GM-PHD filter, is presented. The proposed MD-GM-PHD filter is demonstrated on a simulated over-the-horizon radar (OTHR) scenario.Index Terms-Bayesian filtering, Gaussian mixture, multiple-detection tracking, Over-the-horizon radar (OTHR), probability hypothesis density filter, random finite sets, target tracking.

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Section: A Generalization To a Multipath-aided Phd Filtermentioning

confidence: 97%

Section: A Md-gm-phd Derivationmentioning

confidence: 99%

A Multiple-Detection Probability Hypothesis Density Filter

Tang

Chen

McDonald

et al. 2015

IEEE Trans. Signal Process.

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“…The bilinear SSP was chosen because most real SSPs can be approximated by bilinear SSPs. Part of these CRBs have already been proposed by Blanc-Benon and Jauffret in a target motion analysis context; 12 they derived the CRBs of a target position as the range, depth, azimuth, and target speed vector of a moving target using horizontal bearings and multipath time-delay estimations. In the present paper, the CRBs of a target position are derived for a single sonar ping using the vertical bearings and arrival-time estimations.…”

Section: Cramer-rao Lower Bounds For Bilinear Sound-speed Profilesmentioning

confidence: 99%

“…We detail here the analytic expression of the functions G and H, which were introduced by Baer and Jacobson 16 and rewritten later more compactly by Jauffret and Blanc-Benon. 12 This function system describes the coordinates of any type of ray in a bilinear SSP, and these are correct assuming that c 1 > c 3 > c 2 , otherwise the bottom and the surface have to be exchanged, and the z axis has to be positive downwards…”

Section: Appendix A: Eigenrays In a Bilinear Sound-speed Profilementioning

confidence: 99%

Target-depth estimation in active sonar: Cramer–Rao bounds for a bilinear sound-speed profile

Mours¹,

Ioana²,

Mars³

et al. 2016

The Journal of the Acoustical Society of America

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This paper develops a localization method to estimate the depth of a target in the context of active sonar, at long ranges. The target depth is tactical information for both strategy and classification purposes. The Cramer-Rao lower bounds for the target position as range and depth are derived for a bilinear profile. The influence of sonar parameters on the standard deviations of the target range and depth are studied. A localization method based on ray back-propagation with a probabilistic approach is then investigated. Monte-Carlo simulations applied to a summer Mediterranean sound-speed profile are performed to evaluate the efficiency of the estimator. This method is finally validated on data in an experimental tank.

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“…However, it is mostly used to track stationary or constant velocity emitter and suitable maneuvers are required to resolve possibly existent ambiguities [3] . For a single non-maneuvering observer, the maneuvering emitter is not observable by BOT unless new proper measurements are introduced [1,3,4] .…”

Section: Introductionmentioning

confidence: 99%

Maneuvering Emitter Tracking by a Single Passive Observer Using SRUKF Based IMM Algorithm

Yang

Dan-xing

Guo

et al. 2007

2007 International Conference on Wireless Communications, Networking and Mobile Computing

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The interacting multiple model (IMM) algorithm is combined with the square root unscented Kalman filter (SRUKF), and explored to track maneuvering emitter with a single passive observer, which processes Doppler frequency changing rate and angle measurements concurrently, thus the observer maneuver is not required. Computer simulations are performed to compare the SRUKF based IMM(SRUKF-IMM) tracker with the traditional extended Kalman filter based IMM (EKF-IMM) tracker. Simulation results reveal that the SRUKF-IMM tracker is more stable and effective.

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TMA from bearings and multipath time delays

Cited by 23 publications

References 9 publications

A Multiple-Detection Probability Hypothesis Density Filter

A Multiple-Detection Probability Hypothesis Density Filter

Target-depth estimation in active sonar: Cramer–Rao bounds for a bilinear sound-speed profile

Maneuvering Emitter Tracking by a Single Passive Observer Using SRUKF Based IMM Algorithm

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