Airborne GMTI radar position bias estimation using static-rotator targets of opportunity

Bar‐Shalom, Yaakov

doi:10.1109/7.937480

Cited by 24 publications

(11 citation statements)

References 8 publications

(21 reference statements)

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“…In order to estimate all the biases, there are more than two observable targets in the common surveillance area and the targets must be located in two different quadrants (Barsholm, 2001). It is easily known that the proposed method can't guarantee the independent observability of some biases when the conditions as follows are met (Topland et al, 2007):…”

Section: Simulation Analysismentioning

confidence: 99%

The Error Registration Technique for Maneuvering Radar Network

A¹

2013

Journal of Computer Science

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This study studies are error registration algorithm for the maneuvering radar network, which is one of the difficult problems of multi-sensor registration. When the maneuvering radar has no orientation bias, measurement equations of both attitude bias and measurement bias can be established by using the common targets observed by different radars. Based on the equations, the real-time estimation of both attitude bias and measurement bias can be obtained with the twofold unscented Kalman filter. The results show that the method has a fast convergence rate. When the distance of the sensors is very long, both the absolute registration and the relative registration of attitude bias and measurement bias can be implemented.

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Section: Simulation Analysismentioning

confidence: 99%

The Error Registration Technique for Maneuvering Radar Network

A¹

2013

Journal of Computer Science

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“…Then the system is called N-step observable if, and only if, its observation matrix M satisfies (Peters andIglesias, 1997 andBar-Shalom, 2001):…”

Section: O P T I M I Z E D B I a S E S T I M At I O N M O D E Lmentioning

confidence: 99%

Optimized Bias Estimation Model for Mobile Radar Error Registration

et al. 2012

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For mobile 3-D radar installed on a gyro-stabilized platform, its measurements are usually contaminated by the systematic biases which contain radar offset biases (i.e., range, azimuth and elevation biases) and attitude biases (i.e., yaw, pitch and roll biases) of the platform because of the errors in the Inertial Measurement Units (IMU). Systematic biases can NOT be removed by a single radar itself; however, fortunately, they can be estimated by using two different radar measurements of the same target. The process of estimating systematic biases and then compensating radar measurements is called error registration. In this paper, the registration models are established first, then, the equivalent radar measurement error expressions caused by the attitude biases are derived and the dependencies among attitude biases and offset biases are analysed by using the observable matrix criterion. Based on the analyses above, an Optimized Bias Estimation Model (OBEM) is proposed for registration. OBEM uses the subtraction of azimuth and yaw bias as one variable and omits roll and pitch biases in the state vector, which decreases the dimension of the state vector from fourteen of the All Augmented Model (AAM), (which uses all the systematic biases of both radars as state vector) to eight and has about 80% reduction in calculation costs. Also, OBEM can decrease the coupling influences of roll and pitch biases and improve the estimation performance of radar elevation bias. Monte Carlo experiments were made. Numerical results showed that the bias estimation accuracies and the rectified radar raw measurement accuracies can be improved. K E Y WO R D S1. Error Registration.2. Attitude Bias. 3. All Augmented Model (AAM). 4. Optimized Bias Estimation Model (OBEM).

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“…SBs are usually time-invariant or slowly varying variables. They can be modelled as constants or constants plus small random zero-mean Gaussian white noises or first-order Gauss-Markov process with a slow time constant (Bar-Shalom, 2001). To reduce the estimate errors caused by the wrong state equation model and omitting the small noises of biases for brevity , we assume that all SBs are time-invariant variables and the state equations can be written as:…”

Section: A N a Ly S I S O F M O B I L E 3 -D R A D A R E R R O R R E mentioning

confidence: 99%

“…(2) Bar-Shalom (2001) introduced the observability analysis for the first time. (3) Wang et al (2012) combined all OBs and ABs as a state vector to establish a registration model called AAM; however, AAM has poor estimation performance especially for the attitude and elevation biases because it does not consider the dependencies among biases.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Mobile 3-D Radar Error Registration when Radar Sways with Platform

Chen

Wang

et al. 2013

J. Navigation

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For mobile radars installed on a gyro-stabilised platform (GSP) that can steadily follow an East-North-Up (ENU) frame, attitude biases (ABs) of the platform and offset biases (OBs) of the radar are linear dependent variables. Therefore ABs and OBs are unobservable in the linearized registration equations; however, when combining them as new variables, the system becomes observable, and this model has been called the unified registration model (URM). Unlike GSP mobile radars, un-stabilised GSP (or UGSP) mobile radars are installed on the platform directly and rotate with the platform simultaneously. For UGSP, it is testified that both types of biases are independent and observable because the time-varying attitude angles (AAs) 1 of the platform are included in the registration equations, which destroy the dependencies of both kinds of biases and lead us to propose a completely different linearized registration model-the All Augmented Model (AAM). AAM employs all OBs and ABs in the state vector and a Kalman filter (KF) to produce their estimates. Numerical simulation results show that the estimated performance of AAM is close to the Cramér-Rao lower bound (CRLB) and that the Root Mean Square Errors (RMSEs) of the rectified measurements by using AAM are more than 500 m smaller than by URM in all directions. K E Y

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Airborne GMTI radar position bias estimation using static-rotator targets of opportunity

Cited by 24 publications

References 8 publications

The Error Registration Technique for Maneuvering Radar Network

The Error Registration Technique for Maneuvering Radar Network

Optimized Bias Estimation Model for Mobile Radar Error Registration

Analysis of Mobile 3-D Radar Error Registration when Radar Sways with Platform

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