Bias modeling and estimation for GMTI applications

Abstract

This paper. describes uti approach to sensor hius modeling arid estiniotioii ,for. groirnri target tracking applications rising nirrltiple ciirhorxe Ground Moving Target indicator (GMTI) radur sensors. This upproach was developed as part of tlrr i'recisioti Firecontrol Tracking (PFCT) segnient of the DARPA AffordaDle Moving Surjiace Target Engagenleiit (4MSTE) program For airborne sensors. slowli. vai?.itig platforni location, heading and veloci!ij errors lead to tinie-dependent nieasurenient biases. Track accuracv caii be improved bv using a Kalman j l t e r to estimate and correct the biases in real tinie. based on j x e d reference points. The reference point location can be known a priori or estiniated online as part of the bias correction algorithm.When the reference locations are known a prior, bias effects can be riearlv coriipletelv eliniinated, When the reference point is estiniated online, signrjcant perfor-niance iniprovenient is ohtairied relative to uncorrected nieasrrrenients.

A practical bias estimation algorithm for multisensor-multitarget tracking

“…In this approach, there is no approximation in deriving (17)- (21) unlike the methods previously proposed in [25]- [27]. This was one of the main contributions of [9].…”

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

“…These were obtained by iterating the Riccati equation of the optimal linear (Kalman) filter corresponding to (10) and (13) (see, e.g., [1]). Only the top 3 £ 3 block of the matrix is shown because the terms corresponding to each target are the same as that of the first one, which is P 33 .…”

“…The tracking filter is the final stage of target data processing in the radar systems. For radar target tracking, almost all proposed methods [1][2][3][4][5][6][7][8][9][10][11] follow the algorithmic path. The Kalman filter for linear estimate and the extended Kalman filter for nonlinear estimate are the most typical complex and precise algorithms used for target tracking.…”

“…The target maneuver is considered as an inherent part of its dynamics rather than noise. A simpler filter based on a constant-velocity model for a nonmaneuvering target case and a normal tracking filter based on a linear acceleration model for a maneuvering target case are proposed in [10]. An interacting multiple model algorithm is proposed in [11] in which a second-order model is used for a non-maneuvering target case and one or two third-order models with different process noise levels are used for a maneuvering target case.…”

“…Observability issues were discussed in [7]. More recently, [10,13] considered the problem for radars on moving platforms, also by decoupling the tracking of targets of opportunity and the estimation of the radar and platform biases.…”