Adaptive nonlinear algorithms for radar tracking with roll angle measurements of maneuvering targets

Lainiotis, Demetrios G.; Marinitsis, Ioannis A.; Κατσικάς, Σωκράτης Κ.

doi:10.1109/melcon.1994.381135

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…Hence, attitude angle estimating methods [9][10][11] and attitude angle-aided tracking algorithms [12][13][14] have been studied widely. With 15 state equations and 9 measurement equations derived for a rigid-body model, the simulation results validate the contribution of attitude angles to target tracking, but the linearisation and discretisation of equations are difficult because of complexity [12].…”

Section: Introductionmentioning

confidence: 99%

“…The correlation between attitude angles and target motion, which can be used to improve estimation, is closer than other attribute and feature information, such as classification, RCS or wingspan. Hence, attitude angle estimating methods [9–11] and attitude angle‐aided tracking algorithms [12–14] have been studied widely. With 15 state equations and 9 measurement equations derived for a rigid‐body model, the simulation results validate the contribution of attitude angles to target tracking, but the linearisation and discretisation of equations are difficult because of complexity [12].…”

Section: Introductionmentioning

confidence: 99%

“…With 15 state equations and 9 measurement equations derived for a rigid‐body model, the simulation results validate the contribution of attitude angles to target tracking, but the linearisation and discretisation of equations are difficult because of complexity [12]. Two adaptive algorithms to a cooperative civilian manoeuvering aircraft were proposed to fuse target kinematic measurements and roll angle [13]. The lateral target acceleration was calculated with roll angle and was modelled as increased dimension of state and measurement vectors.…”

Section: Introductionmentioning

confidence: 99%

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Model‐switched Gaussian sum cubature Kalman filter for attitude angle‐aided three‐dimensional target tracking

Zhang

Shan

2015

IET Radar, Sonar & Navigation

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Only target kinematic information is used in most conventional tracking systems, such as a radar or a sonar. Target attitude angles, which provide information about future trajectory curvature before radar measurement, can be used to improve tracking accuracy. The aim of this study is to track three-dimensional (3D) target with attitude angles (yaw and pitch) and radar measurement. Target velocity variations in each coordinate under attitude angles are derived after motion analysis under yaw and pitch angles separately. Then tracking models, the state vector of which includes attitude angles, are presented for target tracking. Targeting at non-zero mean characteristics of attitude measurement, and based on analysing Gaussian sum filter (GSF) and cubature Kalman filter (CKF), a Gaussian sum CKF (GSCKF) is presented to improve the filtering ability of non-linear non-Gaussian systems. Meanwhile, tracking models with different attitude components are established according to the changing law during target motion, and manoeuvering attitude angles are estimated through model switch. A comparison of performance with and without the use of attitude angles shows the benefits of attitude angleaided 3D target tracking. Simulation results of GSFs with different sub-filters demonstrate that the performance of the presented GSCKF has improved over conventional GSFs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model‐switched Gaussian sum cubature Kalman filter for attitude angle‐aided three‐dimensional target tracking

Zhang

Shan

2015

IET Radar, Sonar & Navigation

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“…Reference [3] firstly proposed that attitude angle can be used to estimate the direction of acceleration, which is significant for the filter to improve the performance of state estimation. Reference [4] made full use of roll angle provided by cooperative target itself to estimate lateral acceleration, and established an IMM algorithm according to the covariance of measurement noise. Reference [5] considered attitude angle and maneuver models as stochastic process, and established a continuous time filter for model probability which was used for ground target tracking.…”

Section: Introductionmentioning

confidence: 99%

Attitude angle aided IMMCKF algorithm

Hai

Shan

2011

IEEE 2011 10th International Conference on Electronic Measurement &Amp; Instruments

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To effectively solve the tracking problem of nonlinear maneuvering target, interacting multiple model cubature Kalman filter (immckf) algorithm brings cubature Kalman filter (ckf) into the interacting multiple model (imm) algorithm. this paper brings the attitude angle information into the immckf algorithm, and identifies the target maneuver mode through the fuzzy association between the attitude angle and the current motion mode of target; then the association result is used to fuse with the model probability of imm to enhance its model resolving power. a simulation of maneuvering target tracking shows that the attitude angle aided immckf (aa-immckf) algorithm can effectively improve the tracking accuracy and stability of the original immckf algorithm.

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A yaw-aided interacting multiple model tracking algorithm

Shan

Hai

2012

2012 5th International Congress on Image and Signal Processing

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Adaptive nonlinear algorithms for radar tracking with roll angle measurements of maneuvering targets

Cited by 3 publications

References 3 publications

Model‐switched Gaussian sum cubature Kalman filter for attitude angle‐aided three‐dimensional target tracking

Model‐switched Gaussian sum cubature Kalman filter for attitude angle‐aided three‐dimensional target tracking

Attitude angle aided IMMCKF algorithm

A yaw-aided interacting multiple model tracking algorithm

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