Basic tracking using nonlinear 3D monostatic and bistatic measurements in refractive environments

Crouse, David Frederic

doi:10.1109/maes.2014.120230

Cited by 11 publications

(6 citation statements)

References 42 publications

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“…The Fourier-Hermite Kalman filter is not suitable for applications like filtering using refraction corrupted measurements, which is considered in the companion tutorial [71], because it requires the ability to take derivatives of the measurement function. The explicit evaluation of such derivatives is not practicable when the measurement function is implicitly formulated through ray tracing.…”

Section: A Estimation Using Nonlinear Measurementsmentioning

confidence: 99%

“…Quaternions are also frequently used for animations in video games [254] and can be used in joint polarization-angular superresolution algorithms [208]. However, they are particularly useful when correcting for atmospheric refraction in the com-panion tutorial [71], because a rotation over an arbitrary axis must be performed.…”

Section: The Global Coordinate Systemmentioning

confidence: 99%

“…The validity of this assumption is discussed in Section V.B. However, because the curvature of the Earth matters a lot when accounting for atmospheric effects, it is directly considered in the companion paper [71].…”

Section: The Global Coordinate Systemmentioning

confidence: 99%

“…In the simulation sections, the initial state of the target is fixed, meaning that (68) where δ represents the Dirac delta function and μ 0 is the chosen initial location. Using the linear dynamic model of Section V, assuming that a uniform sampling interval is used, and omitting the subscript of k on F and Q, which are constant when the sampling interval is uniform, the PDF of the state at time k is (superscripts denote matrix powers) (69) The sum in (69) can be written as a block-diagonal matrix with block elements 39 (70) As derived in [294], the Bayes information matrix at step k + 1 may be calculated recursively as (71) where the initial condition is and the parameters of interest simplify to (72) 38 In the literature, both are typically represented by J. Here, a superscript of B is used to differentiate the two matrices.…”

Section: Crousementioning

confidence: 99%

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Basic tracking using nonlinear 3D monostatic and bistatic measurements

Crouse¹

2014

IEEE Aerosp. Electron. Syst. Mag.

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Monostatic and bistatic position and Doppler measurements used in radar and sonar systems are nonlinear transformations of a Cartesian state. These nonlinearities pose a challenge for many target tracking algorithms, causing the so-called contact lens problem, which describes the nonlinear appearance of the measurement probability density function in Cartesian coordinates. This tutorial considers methods for measurement filtering (tracking without considering data association) using a single-Gaussian approximation when monostatic and bistatic position and Doppler measurements are available. The connection between the cubature Kalman filter and numerous other filtering algorithms is shown, and the accuracy and consistency of different algorithms are compared through simulation. An effort is made to express the geometric relationships associated with multistatic tracking in a simple vectorial manner. This tutorial focuses on basic tracking, and the companion tutorials "Tracking Using 3D Monostatic and Bistatic Measurements in Refractive Environments" and "Basic Tracking Using Nonlinear Continuous-Time Dynamic Models" extend the results to more sophisticated physical models.

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Section: A Estimation Using Nonlinear Measurementsmentioning

confidence: 99%

Section: The Global Coordinate Systemmentioning

confidence: 99%

Section: The Global Coordinate Systemmentioning

confidence: 99%

Section: Crousementioning

confidence: 99%

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Basic tracking using nonlinear 3D monostatic and bistatic measurements

Crouse¹

2014

IEEE Aerosp. Electron. Syst. Mag.

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“…It aims to track and observe active and inactive satellites, monitor conditions of the sun, Earth's magnetosphere, ionosphere, and thermosphere and detect objects that can potentially impact Earth [4]. Among the components of SSA, the object tracking algorithm [5][6][7][8][9][10], especially the cooperative object tracking algorithm, plays a key role. To use the information of different sensors, different sensor fusion methods, such as the Kalman filtering [11][12][13] can be utilized.…”

Section: Introductionmentioning

confidence: 99%

Distributed sensor management for space situational awareness via a negotiation game

et al. 2015

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Space situational awareness (SSA) is critical to many space missions serving weather analysis, communications, and navigation. However, the number of sensors used in space situational awareness is limited which hinders collision avoidance prediction, debris assessment, and efficient routing. Hence, it is critical to use such sensor resources efficiently. In addition, it is desired to develop the SSA sensor management algorithm in a distributed manner. In this paper, a distributed sensor management approach using the negotiation game (NG-DSM) is proposed for the SSA. Specifically, the proposed negotiation game is played by each sensor and its neighboring sensors. The bargaining strategies are developed for each sensor based on negotiating for accurately tracking desired targets (e.g., satellite, debris, etc.) . The proposed NG-DSM method is tested in a scenario which includes eight space objects and three different sensor modalities which include a space based optical sensor, a ground radar, or a ground Electro-Optic sensor. The geometric relation between the sensor, the Sun, and the space object is also considered. The simulation results demonstrate the effectiveness of the proposed NG-DSM sensor management methods, which facilitates an application of multiple-sensor multiple-target tracking for space situational awareness.

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Errata and comments for the “Basic Tracking” tutorials

Crouse

2016

IEEE Aerosp. Electron. Syst. Mag.

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Basic tracking using nonlinear 3D monostatic and bistatic measurements in refractive environments

Cited by 11 publications

References 42 publications

Basic tracking using nonlinear 3D monostatic and bistatic measurements

Basic tracking using nonlinear 3D monostatic and bistatic measurements

Distributed sensor management for space situational awareness via a negotiation game

Errata and comments for the “Basic Tracking” tutorials

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