Profile-Free Launch Point estimation for ballistic targets using passive sensors

Kirubarajan, Thiagalingam; Kirubarajan, T.; Nandakumaran, N.; Bar‐Shalom, Yaakov

doi:10.1109/aero.2011.5747433

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…Bearings-only target tracking is commonly used in several domains, such as air [ 1 , 2 ], space [ 3 ], and underwater [ 4 ]. In this method, passive sensors provide line-of-sight (LOS) measurements from the sensor to the target.…”

Section: Introductionmentioning

confidence: 99%

BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors

Shalev

Klein

2021

Sensors

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Bearings-only target tracking is commonly used in many fields, like air or sea traffic monitoring, tracking a member in a formation, and military applications. When tracking with synchronous passive multisensor systems, each sensor provides a line-of-sight measurement. They are plugged into an iterative least squares algorithm to estimate the unknown target position vector. Instead of using iterative least squares, this paper presents a deep-learning based framework for the bearing-only target tracking process, applicable for any bearings-only target tracking task. As a data-driven method, the proposed deep-learning framework offers several advantages over the traditional iterative least squares. To demonstrate the proposed approach, a scenario of tracking an autonomous underwater vehicle approaching an underwater docking station is considered. There, several passive sensors are mounted near a docking station to enable accurate localization of an approaching autonomous underwater vehicle. Simulation results show the proposed framework obtains better accuracy compared to the iterative least squares algorithm.

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Section: Introductionmentioning

confidence: 99%

BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors

Shalev

Klein

2021

Sensors

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“…There have been numerous papers written on the theme of ballistic missile trajectory estimation from los observations (e.g. [4–8]) The common thread running through the sample that we have cited is dual satellite sensor angles‐only tracking of ballistic missiles with a nominal los error on the order of 10 µrad in local azimuth and elevation. The algorithms derived in this paper mark a considerable departure from the literature in both the modelling of the ballistic missile trajectory and the representation of the los measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [6] was specifically included to give an example of a profile‐free approach to estimate the launch parameters of a two‐stage intermediate range ballistic missile with individual sensor revisit times of 0.2 s. This paper did not cover prediction of position and velocity. It assumed measurement time synchronisation and polynomial data fitting to triangulated 3‐ d space points.…”

Section: Introductionmentioning

confidence: 99%

Cylindrical coordinate rocket trajectory profiles and line‐of‐sight projective coordinates for tracking ballistic missiles from satellite infrared sensor line‐of‐sight observations

Danchick¹

2017

IET Radar, Sonar & Navigation

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“…Recently Tharmarasa 10 , et al proposes a profile-free launch point estimator using smoothing followed by backward prediction. They focus on using less a priori information as far as possible, which makes their method work well only in a multi-sensor scenario.…”

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confidence: 99%

Profile-based Maximum Penalised Likelihood Trajectory Estimation from Space-borne LOS Measurements

Shen

Wang

et al. 2016

Def. Sc. Jl.

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IntroductIonSpace-borne infrared missile early-warning system is an important component of missile defense systems. By detecting the azimuth and elevation angles of a missile, the early-warning system can provide a real-time report of each occurrence of a missile launch, and estimate launch parameters and trajectory 1 . The estimation of boost-phase trajectory from space-borne line-of-sight (LOS) measurements. Due to the lack of distance information between a missile and observational satellites, the first problem that needs to be settled is the poor-observability 2,3 . There are two solutions to this problem: one is to increase the number of satellites; the other is to incorporate a priori information into estimation. We focus on the later approach in this paper.A priori information in trajectory estimation can be divided into two levels: a priori information about trajectory model and parameters. Generally, a priori information about model is expressed by a parameterless model with high accuracy. A typical parameterless model is the profile-based model 4,5,11 , which assumes that one can build a profile database before launch 6 . Since a profile-based model only contains four launch parameters, it has conquered the problem of poorobservability effectively. However, an accurate profile of an incoming missile is hard to obtain. Hence, the 'full knowledge' profile has to be improved to increase its adaptability. The pseudo-measurement approach, which takes constraints on trajectory as measurements, can also be treated as a priori information about trajectory model. For example, equalityconstraints are considered as pseudo-measurements in state estimation in 7 . In 8 , the constraints of altitude and speed at the first measurement epoch of a ballistic target are considered as pseudo-measurements.A priori information about parameters refers to prior distribution of model parameters. To include this kind of information, one can employ the so-called Bayesian paradigm. It has been pointed out by 9 that the Bayesian paradigm has no difficulty in treating non-identifiable parameters. But one might be misled by a wrong prior distribution, especially in poor-observability scenarios like the current situation.Recently Tharmarasa 10 , et al. proposes a profile-free launch point estimator using smoothing followed by backward prediction. They focus on using less a priori information as far as possible, which makes their method work well only in a multi-sensor scenario. Kim 11 et al. proposes a launch point estimator based-on k-NN search. Their method is a profilebased method too, but with a different kind of profile.An estimator for trajectory estimation from space-borne LOS measurements following the Bayesian paradigm were proposed in the paper. (i) A new kind of boost-phase trajectory profile, which is more adaptable than other existing profiles; (ii) A profile-based maximum penalised likelihood estimator (PMPLE), which can incorporate different kinds of information into trajectory estimation; (iii) A 1 L and log-barrier pe...

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Profile-Free Launch Point estimation for ballistic targets using passive sensors

Cited by 7 publications

References 8 publications

BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors

BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors

Cylindrical coordinate rocket trajectory profiles and line‐of‐sight projective coordinates for tracking ballistic missiles from satellite infrared sensor line‐of‐sight observations

Profile-based Maximum Penalised Likelihood Trajectory Estimation from Space-borne LOS Measurements

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