Exploitation of bistatic doppler measurements in multistatic tracking

Lang, Thomas; Hayes, Geoff

doi:10.1109/icif.2007.4408019

Cited by 8 publications

(2 citation statements)

References 20 publications

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“…The use of Doppler measurements in multistatic systems was also considered [171,331], resulting in reduced estimation errors for maneuvering targets. It was also noted [11,127,168] that multistatic tracking using only Doppler measurements is possible.…”

Section: A Estimation Using Nonlinear Measurementsmentioning

confidence: 99%

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%

Basic tracking using nonlinear 3D monostatic and bistatic measurements

Crouse¹

2014

IEEE Aerosp. Electron. Syst. Mag.

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“…The measurements are time different of arrival (TDOA) and bearing from target to sensor measured from Y axis in the clock wise direction. The j-th measurement at sensor i (at sampling time k, and k is omitted for simplicity), which is originated from transmitter m and reflected from target t, is given by [8] …”

Section: Simulationmentioning

confidence: 99%

Joint path planning and sensor subset selection for multistatic sensor networks

2008

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Due to the availability of cheap passive sensors, it is possible to deploy a large number of them for tracking purposes in anti-submarine warfare (ASW). However, modern submarines are quiet and difficult to track with passive sensors alone. Multistatic sensor networks, which have few transmitters (e.g., dipping sonars) in addition to passive receivers, have the potential to improve the tracking performance. We can improve the performance further by moving the transmitters according to existing target states and any possible new targets. Even though a large number of passive sensors are available, due to frequency, processing power and other physical limitations, only a few of them can be used at any one time. Then the problems are to decide the path of the transmitters and select a subset from the available passive sensors in order to optimize tracking performance. In this paper, the PCRLB, which gives a lower bound on estimation uncertainty, is used as the performance measure. We present an algorithm to decide jointly the optimal path of the movable transmitters, by considering their operational constraints, and the optimal subset of passive sensors that should be used at each time steps for tracking multiple, possibly time-varying, number of targets. Finding the optimal solution in real time is difficult for large scale problems, and we propose a genetic algorithm based suboptimal solution technique. Simulation results illustrating the performance of the proposed algorithm are also presented.

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One can do better than the unscented Kalman filter for multistatic tracking

Crouse

2013

2013 IEEE Aerospace Conference

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Exploitation of bistatic doppler measurements in multistatic tracking

Cited by 8 publications

References 20 publications

Basic tracking using nonlinear 3D monostatic and bistatic measurements

Basic tracking using nonlinear 3D monostatic and bistatic measurements

Joint path planning and sensor subset selection for multistatic sensor networks

One can do better than the unscented Kalman filter for multistatic tracking

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