Unified Registration Model for Both Stationary and Mobile 3D Radar Alignment

For mobile radar, offset biases and attitude biases influence radar measurements simultaneously. Attitude biases generated from the errors of the inertial navigation system (INS) of the platform can be converted into equivalent radar measurement errors by three analytical expressions (range, azimuth, and elevation, resp.). These expressions are unique and embody the dependences between the offset and attitude biases. The dependences indicate that all the attitude biases can be viewed as and merged into some kind of offset biases. Based on this, a unified registration model (URM) is proposed which only contains radar “offset biases” in the form of system variables in the registration equations, where, in fact, the “offset biases” contain the influences of the attitude biases. URM has the same form as the registration model of stationary radar network where no attitude biases exist. URM can compensate radar offset and attitude biases simultaneously and has minor computation burden compared with other registration models for mobile radar network.

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Integration of Navigation Data

Journal of Navigation ◽

10.1017/s0373463300012558 ◽

1995 ◽

Vol 48 (1) ◽

pp. 114-135 ◽

Cited By ~ 1

Author(s):

A. Svensson ◽

J. Holst

Keyword(s):

Measurement System ◽

Navigation System ◽

Measurement Errors ◽

Inertial Navigation System ◽

Inertial Navigation ◽

Measuring Instruments ◽

Extended Kalman Filtering ◽

Satellite Navigation System ◽

Navigation Data ◽

System A

This article treats integration of navigation data from a variety of sensors in a submarine using extended Kalman filtering in order to improve the accuracy of position, velocity and heading estimates. The problem has been restricted to planar motion. The measurement system consists of an inertial navigation system, a gyro compass, a passive log, an active log and a satellite navigation system. These subsystems are briefly described and models for the measurement errors are given.Four different extended Kalman filters have been tested by computer simulations. The simulations distinctly show that the passive subsystems alone are insufficient to improve the estimate of the position obtained from the inertial navigation system. A log measuring the velocity relative to the ground or a position determining system are needed. The improvement depends on the accuracy of the measuring instruments, the extent of time the instrument can be used and which filter is being used. The most complex filter, which contains fourteen states, eight to describe the motion of the submarine and six to describe the measurement system, including a model of the inertial navigation system, works very well.

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Attitude Bias Conversion Model for Mobile Radar Error Registration

Journal of Navigation ◽

10.1017/s0373463312000239 ◽

2012 ◽

Vol 65 (4) ◽

pp. 651-670 ◽

Cited By ~ 4

Author(s):

L. Chen ◽

G. H. Wang ◽

S. Y. Jia ◽

I. Progri

Keyword(s):

Measurement Errors ◽

Inertial Navigation System ◽

Unscented Kalman Filter ◽

Bias Estimation ◽

Radar Measurement ◽

Conversion Model ◽

Measurement Units ◽

Radar Measurements ◽

Rotation Transformation ◽

The Mean

Besides offset biases (such as range, the gain of range, azimuth, and elevation biases), for mobile radars, platform attitude biases (such as yaw, pitch, and roll biases) induced by the accumulated errors of the Inertial Measurement Units (IMU) of the Inertial Navigation System (INS) can also influence radar measurements. Both kinds of biases are coupled. Based on the analyses of the coupling influences and the observability of 3-D radars’ error registration model, in the article, an Attitude Bias Conversion Model (ABCM) based on Square Root Unscented Kalman Filter (SRUKF) is proposed. ABCM can estimate 3-D radars’ absolute offset biases under the influences of platform attitude biases. It converts platform attitude biases into radar measurement errors, by which the target East-North-Up (ENU) coordinates can be obtained from radar measurements directly without using the rotation transformation, which was usually used in the transition from platform frame to ENU considering attitude biases. In addition, SRUKF can avoid the inaccurate estimations caused by linearization, and it can weaken the adverse influences of the poor attitude bias estimation results in the application of ABCM. Theoretical derivations and simulation results show that 1) ABCM-SRUKF can improve elevation bias estimate accuracy to about 0·8 degree in the mean square error sense; 2) linearization is not the main reason for poor estimation of attitude biases; and 3) unobservability is the main reason.

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