Passive Multi-sensor Maneuvering Target Tracking Based on UKF-IMM Algorithm

Wu, Panlong; Li, Xingxiu

doi:10.1109/icie.2009.182

Cited by 7 publications

(7 citation statements)

References 6 publications

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“…The filter covariance matrix is initialized based on Eq. (11). The quality of the estimate depends on the sampling time T along with the assumed measurement process noise covariance matrix R θ and process noise variance q.…”

Section: Ranging Based On Automatic Triangulationmentioning

confidence: 99%

“…The bearing only estimation and tracking-based approach involving two or more passive sensors employs nonlinear filter-based approaches like the extended Kalman filter (EKF) 6,9,10 or the unscented Kalman filter. 11 These methods use angle-only information measured by multiple systems at the same time. These algorithms are generally used for medium-range target tracking where an accuracy of a few meters is considered sufficient.…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms are generally used for medium-range target tracking where an accuracy of a few meters is considered sufficient. 6,[9][10][11] An approach for a multiple IR sensor-based system is proposed in Ref. 12, where the estimates of the EKF are evaluated by a fuzzy logic algorithm that decides the authority of 3-D angle intersection results.…”

Section: Introductionmentioning

confidence: 99%

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Active vehicle protection using angle and time-to-go information from high-resolution infrared sensors

2015

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An algorithm for an active protection system with two or more passive infrared sensors based on the principle of triangulation and an extended measurement vector consisting of time-to-go (TTG) estimation is proposed to address high-speed short-range threats. The proposed algorithm exploits the full potential of passive sensors enabling the use of target irradiance in a manner to estimate the TTG along with angle information. The approach here uses target irradiance measured at successive scans to initialize a fixed interval iteration scheme for estimating the TTG. The Newton's method is used for estimating the TTG, the estimate is then added to the measurement vector and position estimation is performed using an extended Kalman filter. Computer simulations demonstrate the resulting improvement in estimation and feasibility for real-time application of the proposed algorithm.

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Section: Ranging Based On Automatic Triangulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Active vehicle protection using angle and time-to-go information from high-resolution infrared sensors

2015

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“…W(k − 1) is the model-dependent process noise sequences with zero mean and covariance Q. The coordinated turn model [15] is considered as the basic model, whose state transition matrix and noise gain matrix can be expressed as…”

Section: Adaptive Grid Immmentioning

confidence: 99%

Tracking algorithm with radar and infrared sensors using a novel adaptive grid interacting multiple model

Zhang

et al. 2014

IET Science, Measurement & Technology

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This study presents a novel adaptive grid interacting multiple model based on modified iterated extended Kalman filter (AGIMM-MIEKF) for tracking a manoeuvreing target using radar/infrared (IR) heterogeneous sensors. This tracking algorithm is developed by aligning observation data of radar/IR sensors in time, and fusing the synthesised data before applying to AGIMM-MIEKF algorithm. Under the architecture of the proposed algorithm, the AGIMM deals with the model switching, whereas the MIEKF accounts for non-linearity in the dynamic system models. A new measurement update equation and an iterated termination criterion are derived and applied to radar/IR tracking system. The simulation results show that the presented AGIMM-MIEKF has higher tracking precision than the traditional algorithms.

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“…The application of the IMM algorithm was studied in [8] where bearings only infrared sensors were used. IMM algorithm based on unscented Kalman filter was used to track a maneuvering target based on passive multi-sensor [9]. Nowadays, most of the nonlinear tracking systems are still based upon the process of linearization, such as the extended Kalman filter (EKF).…”

Section: Introductionmentioning

confidence: 99%

Maneuvering target tracking using passive TDOA measurements

Guo

Zhang

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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This paper proposes a novel interacting multiple model (IMM) algorithm to track a maneuvering target, with the aim of improving the tracking performance of a time difference of arrival (TDOA) passive tracking system. Under the architecture of the proposed algorithm, the multiple model deals with the model switching, while the iterated extended Kalman filter (EKF) accounts for non-linearity in the dynamic system models. The tracking performances of the proposed algorithm, EKF, IMM are compared via Monte Carlo simulations. Simulation results indicate that the proposed algorithm is an effective nonlinear filtering algorithm for TDOA passive tracking system, and has higher tracking precision than the IMM and EKF. The proposed algorithm can reduce nearly 6.75% and 61.23% of the positioning error than IMM and EKF algorithms.

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Passive Multi-sensor Maneuvering Target Tracking Based on UKF-IMM Algorithm

Cited by 7 publications

References 6 publications

Active vehicle protection using angle and time-to-go information from high-resolution infrared sensors

Active vehicle protection using angle and time-to-go information from high-resolution infrared sensors

Tracking algorithm with radar and infrared sensors using a novel adaptive grid interacting multiple model

Maneuvering target tracking using passive TDOA measurements

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