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

Amirzadeh, Ahmadreza; Karimpour, Ali; Moeini, Ali

doi:10.5897/sre10.980

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…This tracking algorithm is characterized by small calculation and nice real-time ability [10,11]. And the Kalman filtering method [12] is a time domain method which solves the problem of optimal filtering based on state space. It has high applicability to the estimation of moving targets that frequently change motion states.…”

Section: Introductionmentioning

confidence: 99%

UAV autonomous path optimization simulation based on radar tracking prediction

Wang

Jin

Zhang

et al. 2018

J Wireless Com Network

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In the UAV path planning study, due to the relative movement of multiple targets and the UAV, long-term and large-scale UAV, autonomous tracking has not been achieved. Therefore, aiming at this problem, this paper uses radar to provide a real-time feedback on target position, estimates the later motion state of the target according to its position, and then perform dynamic path planning by combining the feedback data and the state estimation result. Finally, The UAV path is optimized in real time. Experiments show that the proposed scheme can better plan the UAV path when multiple targets are in motion, thus improving the intelligence of the UAV and the capability of long time tracking.

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Section: Introductionmentioning

confidence: 99%

UAV autonomous path optimization simulation based on radar tracking prediction

Wang

Jin

Zhang

et al. 2018

J Wireless Com Network

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“…A common approach in tracking and fault detection applications for mechanical and electrical systems is the use of multiple model (MM) methods. − These methods work by having multiple models (each model representing one mode of the system) run in parallel. Model probabilities and the overall estimate are obtained according to the rules of the particular method being used.…”

Section: Introductionmentioning

confidence: 99%

Handling Model Plant Mismatch in State Estimation Using a Multiple-Model-Based Approach

Arulmaran

Liu

2017

Ind. Eng. Chem. Res.

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Accurate state estimates are important for the success of model predictive control (MPC). State estimates are obtained using a model, but, in real plants, there will always be model plant mismatch (MPM), which affects these estimates. In this work, we present a multiple-model (MM)-based approach to obtain unbiased state estimates in the presence of MPM. Necessary assumptions on the source of mismatch and models used are presented. It is shown that unbiased output estimates do not guarantee unbiased state estimates. Our approach is shown to provide unbiased state estimates when all the assumptions are met using a froth flotation system. A model-identification-based control approach using our multiple model estimation approach with a conventional MPC was tested on the froth flotation system and was found to successfully provide offset-free reference tracking when all the necessary assumptions for unbiased state estimation were met. A nonlinear offset-free MPC was also tested on the froth flotation system but was not able to provide offset-free reference tracking, because some necessary conditions were not met.

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Nonlinear Filtering with IMM Algorithm for Ultra-Tight GPS/INS Integration

Jwo

Tseng

2013

International Journal of Advanced Robotic Systems

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This paper conducts a performance evaluation for the ultra‐tight integration of a Global positioning system (GPS) and an inertial navigation system (INS), using nonlinear filtering approaches with an interacting multiple model (IMM) algorithm. An ultra‐tight GPS/INS architecture involves the integration of in‐phase and quadrature components from the correlator of a GPS receiver with INS data. An unscented Kalman filter (UKF), which employs a set of sigma points by deterministic sampling, avoids the error caused by linearization as in an extended Kalman filter (EKF). Based on the filter structural adaptation for describing various dynamic behaviours, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the ultra‐tight GPS/INS integration. The use of IMM enables tuning of an appropriate value for the process of noise covariance so as to maintain good estimation accuracy and tracking capability. Two examples are provided to illustrate the effectiveness of the design and demonstrate the effective improvement in navigation estimation accuracy. A performance comparison among various filtering methods for ultra‐tight integration of GPS and INS is also presented. The IMM based nonlinear filtering approach demonstrates the effectiveness of the algorithm for improved positioning performance

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

Cited by 4 publications

References 25 publications

UAV autonomous path optimization simulation based on radar tracking prediction

UAV autonomous path optimization simulation based on radar tracking prediction

Handling Model Plant Mismatch in State Estimation Using a Multiple-Model-Based Approach

Nonlinear Filtering with IMM Algorithm for Ultra-Tight GPS/INS Integration

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