Modified input estimation technique for tracking manoeuvring targets

Khaloozadeh, Hamid; Karsaz, Ali

doi:10.1049/iet-rsn:20080028

Cited by 81 publications

(41 citation statements)

References 24 publications

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“…This example has been chosen from (Khaloozadeh and Karsaz, 2009 Table 1 shows the results of state estimation for the IMMAUKF and IMM2 algorithms, and Wang's method. In this table, the Root Mean Square Error (RMSE) of different parameters has been computed with the MonteCarlo analysis of 100 runs.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…However, this method has not been successful because the real targets move with nonconstant velocity or acceleration. To aid the IE approaches to cope with this trouble, Khaloozadeh and Karsaz (2009) have recently proposed a new SKF-based target tracker with IE approach. This method is an Augmented Kalman Filter (AUKF), and it has obtained lots of attention Bahari et al, , 2011Beheshtipour and Khaloozadeh, 2009;Yang and Ji, 2010) due to the elimination of the constant input assumption in the previous IE approaches.…”

Section: The Ie Approachmentioning

confidence: 99%

“…Augmented kalman filter (AUKF) has been developed based on the combination of Fisher and Bayesian uncertainties (Khaloozadeh and Karsaz, 2009). This method considers the acceleration as an additive state term in the state space equation and estimates the acceleration and the original states simultaneously.…”

Section: The Aukf Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

An IMM algorithm based on augmented kalman filter for maneuvering target tracking

Amirzadeh¹,

Karimpour²,

Moeini³

2011

Sci. Res. Essays

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In this paper, in order to increase the accuracy of interacting multiple model (IMM) algorithm in presence of low and medium maneuvers, a new IMM algorithm based on Augmented Kalman Filter (AUKF) has been proposed. The accuracy of the IMM algorithm depends upon having a set of filters with motion models which are similar at all times to the real target situations. One way to increase the accuracy of this estimator is to substitute more accurate filters instead of the Standard Kalman Filter (SKF) in it. In order to improve the performance of IMM algorithm, this paper proposes to replace each SKF in the IMM algorithm with the AUKF. Due to the better accuracy of the AUKF than SKF in presence of low and medium maneuvers, this substitution will improve the performance of IMM algorithm in these maneuvering levels. The Monte-Carlo simulation results show the accuracy of the proposed method.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: The Ie Approachmentioning

confidence: 99%

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An IMM algorithm based on augmented kalman filter for maneuvering target tracking

Amirzadeh¹,

Karimpour²,

Moeini³

2011

Sci. Res. Essays

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show abstract

“…For example, the ACAs are treated as the firstorder Markov process with zero mean in the Singer model [2] and as the state variables to participate in the filtering and estimated at each step of the iteration. The second is that the ACAs are taken as the unknown deterministic input vector, and the input estimation technique, which is relatively independent of the filtering process, is used to estimate it [7][8][9][10][11]. However, the estimation of MAs by the calculation of the ACAs is an open-loop method, and the estimation error cannot be corrected in the filter iteration.…”

Section: Introductionmentioning

confidence: 99%

Maneuvering Acceleration Estimation Algorithm Using Doppler Radar Measurement

Liu

Zhou

2018

Mathematical Problems in Engineering

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An algorithm to estimate the tangential and normal accelerations directly using the Doppler radar measurement in an online closed loop form is proposed. Specific works are as follows: first, the tangential acceleration and normal acceleration are taken as the state variables to establish a linear state transition equation; secondly, the decorrelation unbiased conversion measurement Kalman filter (DUCMKF) algorithm is proposed to deal with the strongly nonlinear measurement equation; thirdly, the geometric relationship between the range rate and the velocity direction angle is used to obtain two estimators of the velocity direction angle; finally, the interactive multiple model (IMM) algorithm is used to fuse the estimators of the velocity direction angle and then the adaptive IMM of current statistical model based DUCMKF (AIMM-CS-DUCMKF) is proposed. The simulation experiment results show that the accuracy and stability of DUCMKF are better than the sequential extended Kalman filter algorithm, the sequential unscented Kalman filter algorithm, and converted measurement Kalman filter algorithms; on the other hand they show that the AIMM-CS-DUCMKF can obtain the high accuracy of the tangential and normal accelerations estimation algorithm.

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“…IE techniques, which are not reliable on the prior information about maneuvering acceleration, consider the maneuvering acceleration as an unknown input and estimate it with least square method, but they need additional effort for the detection of acceleration. Khaloozadeh and Karsaz suggested the modified input estimation (MIE) [12], which considers the unknown acceleration as a new augmented component of the target state and estimate it with Kalman filter. New filters were incorporated into the MIE to improve its performance on high maneuver in [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Absolute Adaptive Cs Model and Modified Strong Tracking Unscented Filter for High Maneuvering Target Tracking

Zhou¹,

Jin-mang²,

Liu³

2013

PIER C

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Abstract-Absolute adaptive current statistical (AACS) model and modified strong tracking unscented filter (MSTUF) are proposed for maneuvering target tracking (MTT) under nonlinear measurement in this paper. The key point of the AACS model is to associate the instantaneous acceleration variance with some elements of state covariance matrix by constructing acceleration increment models of the acceleration limit and acceleration mean in the CS model, while the maneuvering frequency can adjust itself according to the change of the measurement residual. MSTUF is proposed for high maneuver tracking under nonlinear measurement by incorporating the modified strong tracking filter (STF) into the unscented filter (UF). Since the state covariance, process noise covariance and maneuvering frequency can adjust themselves jointly according to the residual, the proposed algorithm, called the AACS-MSTUF, has a good performance on both maneuver and non-maneuver. Simulation results indicate that the overall performance of the proposed algorithm is better than the interacting multiple-model unscented filter (IMM-UF), UF and original strong tracking unscented filter (STUF) based on the CS model (CS-STUF) when considering tracking accuracy, stability, convergence and computational complexity.

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Modified input estimation technique for tracking manoeuvring targets

Cited by 81 publications

References 24 publications

An IMM algorithm based on augmented kalman filter for maneuvering target tracking

An IMM algorithm based on augmented kalman filter for maneuvering target tracking

Maneuvering Acceleration Estimation Algorithm Using Doppler Radar Measurement

Absolute Adaptive Cs Model and Modified Strong Tracking Unscented Filter for High Maneuvering Target Tracking

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