Passive Tracking Algorithm with Transmitter Attribute Information

Traditional passive tracking algorithms are usually subjected to problems such as the poor measurement accuracy of parameters of target motion, complex algorithm, instable tracking precision under complex circumstances. Aiming at this issue, in this paper we combined the dual function of transmitter identification and target tracking in passive reconnaissance, discussed the selection of transmitter eigenvector in passive tracking based on transmitter attribute information, established the passive tracking algorithm framework combined with transmitter attribute information, and then investigated passive tracking arithmetic model based on statistical regulation and ratiocinative regulation respectively, in the hope of providing new idea for passive target tracking.

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Analysis and simulation of passive target tracking algorithm under polar coordinates

Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527) ◽

10.1109/wcica.2002.1020001 ◽

2003 ◽

Author(s):

Fei Chen ◽

Zhongliang Jing ◽

Feng Li

Keyword(s):

Target Tracking ◽

Tracking Algorithm ◽

Polar Coordinates ◽

Passive Target Tracking

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Attitude dynamics aiding for three-dimensional passive target tracking of strap-down seeker based on instrumental variable Kalman filter

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220923768 ◽

2020 ◽

Vol 42 (14) ◽

pp. 2645-2659

Author(s):

Yingchao Xiao ◽

Jun Zhou ◽

Bin Zhao

Keyword(s):

Kalman Filter ◽

Target Tracking ◽

Instrumental Variable ◽

Measurement Accuracy ◽

Sliding Mode ◽

Angular Rate ◽

Three Dimensional ◽

Measurement Unit ◽

State Equations ◽

Passive Target Tracking

The accuracy of three-dimensional (3D) passive target tracking under strap-down system is usually affected by the measurement accuracy of attitude angular rate and attitude angle. In order to save the problem, a novel 3D passive target tracking method based on instrumental variable Kalman filter (IVKF) aided by the attitude dynamic is proposed. At first, the maneuvering target motion model is established based on the “current” statistical model and the filtering equation of MEMS inertial measurement unit (IMU) is also set up. Then, linearize the nonlinear state equations and replace the nonlinear measurement equations with pseudolinear equations. The 3D pseudolinear Kalman filter (PLKF) algorithm is derived according to the linear Kalman filter (KF). To counter the severe bias problems with PLKF, bias compensation and recursive instrumental variable (IV) methods are considered. In order to enhance observability of the system, a 3D motion tracking sliding-mode guidance law is deduced. Finally, some mathematical simulations were made to verify the effectiveness of the proposed method. The simulation results show the effect of the measurement accuracy and complexity of the algorithm are reduced, which proves the validity of the method.

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Study on Velocity Constrained Particle Filters in Passive Tracking Applications

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.20-23.482 ◽

2010 ◽

Vol 20-23 ◽

pp. 482-486

Author(s):

Jian Kang ◽

Wei Kang ◽

Guo Sheng Rui

Keyword(s):

Convergence Rate ◽

Target Tracking ◽

Prior Information ◽

Particle Filters ◽

Poor Performance ◽

Maneuvering Target Tracking ◽

Maneuvering Target ◽

The Poor ◽

Passive Tracking

To improve the poor performance of the maneuvering target tracking, Particle Filters (PF) and velocity constrained Particle Filters (VCPF) are introduced to track the maneuvering target. According to the prior information, outrange particles are discarded during prediction step, and the distribution and the weight of particles are adjusted. Simulation result shows that VCPF can track the maneuvering target stablely. Furthermore, the convergence rate and track accuracy of the algorithm can also be effectively improved.

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Methods for Restricting Maximum Exposure Rate in Computerized Adaptative Testing

Methodology ◽

10.1027/1614-2241.3.1.14 ◽

2007 ◽

Vol 3 (1) ◽

pp. 14-23 ◽

Cited By ~ 9

Author(s):

Juan Ramon Barrada ◽

Julio Olea ◽

Vicente Ponsoda

Keyword(s):

Measurement Accuracy ◽

Computerized Adaptive Testing ◽

Computation Time ◽

Adaptive Testing ◽

Exposure Rate ◽

Control Parameters ◽

The Impact ◽

Two Alternatives ◽

Selection Of ◽

Maximum Exposure

Abstract. The Sympson-Hetter (1985) method provides a means of controlling maximum exposure rate of items in Computerized Adaptive Testing. Through a series of simulations, control parameters are set that mark the probability of administration of an item on being selected. This method presents two main problems: it requires a long computation time for calculating the parameters and the maximum exposure rate is slightly above the fixed limit. Van der Linden (2003) presented two alternatives which appear to solve both of the problems. The impact of these methods in the measurement accuracy has not been tested yet. We show how these methods over-restrict the exposure of some highly discriminating items and, thus, the accuracy is decreased. It also shown that, when the desired maximum exposure rate is near the minimum possible value, these methods offer an empirical maximum exposure rate clearly above the goal. A new method, based on the initial estimation of the probability of administration and the probability of selection of the items with the restricted method ( Revuelta & Ponsoda, 1998 ), is presented in this paper. It can be used with the Sympson-Hetter method and with the two van der Linden's methods. This option, when used with Sympson-Hetter, speeds the convergence of the control parameters without decreasing the accuracy.

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