Intelligent Kalman filter for tracking a manoeuvring target

Lee, B.J.; Park, J.B.; Joo, Young Hoon; Jin, Seung Hee

doi:10.1049/ip-rsn:20040894

Cited by 34 publications

(12 citation statements)

References 15 publications

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“…First, each time we need a value of MSE during global optimization procedure we have to run IMM_EKF and IMM-UKF on all availab le data. This requires a significant co mputational time since for example in order to find a global minimu m o f a smooth function of 3 parameters; we need to compute the function value many times [22][23][24].…”

Section: Applying Ga-pso In Filter Tuningmentioning

confidence: 99%

Performance Comparison of Hybrid GA-PSO Based Tuned IMMs for Maneuver Target Tracking

Jatoth

Kumar²

2013

IJISA

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Abstract-Target tracking is very important field of research as it has wider applicat ions in defense as well as civilian applications. Kalman filter is generally used for such applications. When the process and measurements are non linear extensions of Kalman filters like Extended Kalman Filter, Unscented Kalman Filters are widely used. UKF can give estimat ions up to second order characteristics of random process. The target is maneuvering and switching among different models like constant velocity (CV), constant acceleration (CA ) or constant turn (CT), Interactive Multiple Models (IMM) are employed. Imp lementation of IMM filters fo r any application is difficult because of init ialization of Kalman filter i,e, tuning of filter has to be performed before applying to real t ime situations. It demands prior estimations of Noise covariance matrices which are left for engineering intuitions. This paper presents the nonlinear state estimation using IMM and tuning of the filter is done using bio-inspired algorithms like PSO GA and Hybrid GA-PSO.

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Section: Applying Ga-pso In Filter Tuningmentioning

confidence: 99%

Performance Comparison of Hybrid GA-PSO Based Tuned IMMs for Maneuver Target Tracking

Jatoth

Kumar²

2013

IJISA

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“…In some situations, the sonar operator performs tracking manually, but modern sonars have an automatic system to support this task. Although Kalman filters (Lee, 2004) have often been used to implement passive tracking (Rao, 2006), other techniques, (Mellema, 2006) have also been obtaining good results in target tracking application.…”

Section: Trackingmentioning

confidence: 99%

Independent Component Analysis for Passive Sonar Signal Processing

Moura¹,

Filho²,

Seixas³

2009

Advances in Sonar Technology

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“…The time delay between the occurrence of the maneuver and its detection is unavoidable. (2) Methods based on the online identification of maneuvering acceleration and its statistical characteristics [4], [5], [6], which do not detect maneuver of targets, but need a priori assumption on the maneuvering characteristics of targets, perform well when targets maneuver but the performance declines as maneuver is eliminated. (3) Multiple model methods [7], [8], [9], which are a combination of the two methods mentioned above, to some extent, compensate for the disadvantages of them.…”

Section: Introductionmentioning

confidence: 99%

Tracking an underwater maneuvering target using an adaptive Kalman filter

Ren

et al. 2013

2013 IEEE International Conference of IEEE Region 10 (TENCON 2013)

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Abstract-To improve the tracking accuracy of an underwater maneuvering target, according to its characteristics of low speed and weak maneuvering performance, an adaptive Kalman filter is given based on the online estimation of the process noise variance. As the main filter analyzes the target motion, the process noise variance of the main filter is estimated by an auxiliary filter for being adaptively adjusted according to the target maneuvering intensity to improve the target tracking accuracy for uniform motions, as well as improving response speed of the filter for maneuvering behavior of the target. Simulation results show that the proposed algorithm performs well, which, to a certain extent, effectively improves the tracking accuracy of an underwater maneuvering target.

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Intelligent Kalman filter for tracking a manoeuvring target

Cited by 34 publications

References 15 publications

Performance Comparison of Hybrid GA-PSO Based Tuned IMMs for Maneuver Target Tracking

Performance Comparison of Hybrid GA-PSO Based Tuned IMMs for Maneuver Target Tracking

Independent Component Analysis for Passive Sonar Signal Processing

Tracking an underwater maneuvering target using an adaptive Kalman filter

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