scholarly journals MANEUVERING TARGET DOPPLER-BEARING TRACKING WITH SIGNAL TIME DELAY USING INTERACTING MULTIPLE MODEL ALGORITHMS

2008 ◽  
Vol 87 ◽  
pp. 15-41 ◽  
Author(s):  
Suzhi Bi ◽  
Xiao Yi Ren
Keyword(s):  
Time Delay ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target ◽  
Signal Time

Polynomial Model Set and its Interacting Multiple Model Algorithms

2012 ◽  
Vol 562-564 ◽  
pp. 2038-2044
Author(s):  
Jin Xiao Zhao ◽  
Jian Qiu Zhang ◽  
Dong Ming Zhou
Keyword(s):  
Sampling Interval ◽  
Polynomial Model ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Geometrical Properties ◽  
Filter Performance ◽  
Maneuvering Target ◽  
Polynomial Curve ◽  
Motion Modes ◽  
Model Set

From the maneuvering target orbit on the geometrical properties, according to different motion modes track corresponding to different order number polynomial curve, using the least squares fitting structure, this paper gives out a group of various motion modes matching the mathematical model—polynomial model set (PMS), and gives distinct mathematical process. PMS covers all the motion modes theoretically, easy to choose according to the practical situation and expand, especially suitable for single model can not accurately describe the complex sports scene. The model need not consider sampling interval, without lowering the filter performance at the same time, reduced prior knowledge dependence. Finally, simulation results indicate that the correctness and validity and practicality.

scholarly journals IMM Iterated Extended Particle Filter Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yang Wan ◽  
Shouyong Wang ◽  
Xing Qin
Keyword(s):  
Particle Filter ◽  
Density Function ◽  
The State ◽  
Arbitrary Distribution ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target ◽  
Particle Filter Algorithm ◽  
Non Gaussian ◽  
Importance Density Function

In order to solve the tracking problem of radar maneuvering target in nonlinear system model and non-Gaussian noise background, this paper puts forward one interacting multiple model (IMM) iterated extended particle filter algorithm (IMM-IEHPF). The algorithm makes use of multiple modes to model the target motion form to track any maneuvering target and each mode uses iterated extended particle filter (IEHPF) to deal with the state estimation problem of nonlinear non-Gaussian system. IEHPF is an improved particle filter algorithm, which utilizes iterated extended filter (IEHF) to obtain the mean value and covariance of each particle and describes importance density function as a combination of Gaussian distribution. Then according to the function, draw particles to approximate the state posteriori density of each mode. Due to the high filter accuracy of IEHF and the adaptation of system noise with arbitrary distribution as well as strong robustness, the importance density function generated by this method is more approximate to the true sate posteriori density. Finally, a numerical example is included to illustrate the effectiveness of the proposed methods.

Tracking a Maneuvering Target in Clutter by Asynchronous and Heterogeneous Sensors on Single Airborne Platform

2014 ◽  
Vol 701-702 ◽  
pp. 154-159
Author(s):  
Wei Hua Wu ◽  
Jing Jiang ◽  
Xun Feng ◽  
Chong Yang Liu
Keyword(s):  
Coordinate System ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Cartesian Coordinates ◽  
Heterogeneous Sensors ◽  
Maneuvering Target ◽  
Passive Sensors ◽  
Simulation Results

A new algorithm is proposed for tracking a maneuvering target in clutter using heterogeneous sensors, such as active and passive sensors, which are carried on single moving airborne platform and report their observations asynchronously. The algorithm combining interacting multiple model (IMM) with probabilistic data association filter (PDAF) is sequentially implemented to cope with asynchronous reports. In addition, in order to close to practice, the algorithm is based on Earth-centered Earth-fixed (ECEF) coordinate system while it considers the effect of the platform’s attitude. So the algorithm extends previous algorithms from synchronous case to asynchronous case, from fixed station to moving airborne platform, and from local Cartesian coordinates to general ECEF coordinates. The simulation results show that the proposed algorithm has a broader and more practical scope while being slightly worse than existing algorithm which only be applied under synchronous case.

scholarly journals IMM Fifth-Degree Spherical Simplex-Radial Cubature Filter for Maneuvering Target Tracking

2017 ◽  
Author(s):  
Hua Liu ◽  
Wen Wu
Keyword(s):  
Target Tracking ◽  
Tracking Performance ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Scenario Simulation ◽  
Tracking Accuracy ◽  
Maneuvering Target Tracking ◽  
Switching Speed ◽  
Maneuvering Target ◽  
Spherical Simplex

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named interacting multiple model fifth-degree spherical simplex-radial cubature filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and fifth-degree spherical simplex-radial cubature filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with IMMUKF, IMMCKF and IMM5thCKF.

scholarly journals Modular Interacting Multiple Model Based on Extended Viterbi Algorithm for Maneuvering Target Tracking

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Xia Liu ◽  
Fei Long ◽  
Wenjie Zhang ◽  
Lu Guo
Keyword(s):  
Target Tracking ◽  
Viterbi Algorithm ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Markov Jump ◽  
Maneuvering Target ◽  
Model Based ◽  
Time Simulation ◽  
Markov Jump Linear Systems

A new maneuvering target tracking algorithm is investigated, which is modeled as a class of Markov jump linear systems (MJLS). Drawing on the experience of combination idea of the extended Viterbi algorithm (EV) and the interacting multiple model algorithm (IMM), a modular interacting multiple model based on extended Viterbi (MIMMEV) is presented. The MIMMEV algorithm consists ofNindependent interacting multiple model-extended Viterbi (IMM-EV). Furthermore, these IMM-EV filters are independent and working in parallel in the MIMMEV algorithm. According to the derived probability, the estimated state of every moment is the weighted sum of each estimator at the corresponding time. Simulation results demonstrate that the proposed algorithm improves the tracking precision and reduces the computational burden compared with traditional IMM and IMM-EV.

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