scholarly journals Radar Selection Method Based on an Improved Information Filter in the LPI Radar Network

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Zhenkai Zhang ◽  
Bing Zhang ◽  
Zhibin Xie ◽  
Yi Yang
Keyword(s):  
Information Gain ◽  
Information Filtering ◽  
Selection Method ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Error Covariance ◽  
Information Filter ◽  
Relationship Model ◽  
The Relationship

In order to save the radar resources and obtain the better low probability intercept ability in the network, a novel radar selection method for target tracking based on improved interacting multiple model information filtering (IMM-IF) is presented. Firstly, the relationship model between radar resource and tracking accuracy is built, and the IMM-IF method is presented. Then, the information gain of every radar is predicted according to the IMM-IF, and the radars with larger information gain are selected to track target. Finally, the weight parameters for the tracking fusion are designed after the error covariance prediction of every working radar, in order to improve the IMM-IF. Simulation results show that the proposed algorithm not only saves much more radar resources than other methods but also has excellent tracking accuracy.

scholarly journals Multisensor Distributed Dynamic Programming Method for Collaborative Warning and Tracking

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Peng Ni ◽  
Bo Zhang ◽  
Yafei Song ◽  
Mingliang Zhang
Keyword(s):  
Dynamic Programming ◽  
Particle Filter ◽  
Information Gain ◽  
Variable Structure ◽  
Dynamic Programming Method ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Motion State ◽  
Real Time Tracking

Multisensor distributed dynamic programming for collaborative warning and tracking during antimissile combat serves to meet the tracking accuracy requirements of all ballistic targets in the battlefield under the circumstance of a limited total amount of sensor resources. This paper proposes a method of multisensor distributed dynamic programming for collaborative warning and tracking based on game theory. First, starting from the target tracking algorithm, according to the characteristics of antimissile multisensor combat planning, the box particle filter (BPF) theory capable of distributed filtering and inaccurate measurement is introduced. Using the flight phase characteristics of ballistic targets, a variable structure adaptive multimodel box-based particle filter tracking method is constructed. A box particle filter with the variable structure adaptive interacting multiple model (VSAIMM-BPF) is proposed. The method solves the continuous real-time tracking problem of the ballistic target in all the phases and achieves high tracking accuracy while reducing computational complexity. Then, the motion state of each ballistic target in combat is recursively evaluated by the filtering algorithm, and the calculated sensor information gain is used as a measure to obtain more or better sensor resources for the community of interest to track the corresponding ballistic target through the game. Ultimately, the method achieves distributed dynamic programming.

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 An Improved Interacting Multiple Model Algorithm Used in Aircraft Tracking

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Wei dong Zhou ◽  
Jia nan Cai ◽  
Long Sun ◽  
Chen Shen
Keyword(s):  
Transition Matrix ◽  
Continuous System ◽  
Minimum Variance ◽  
Interacting Multiple Model ◽  
Mode Transition ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Sojourn Times ◽  
Aircraft Tracking ◽  
Model Probability

There are some problems in traditional interacting multiple model algorithms (IMM) when used in target tracking systems. For instance, the mode transition matrix is inaccurate and cannot be determined when the sojourn times are not known. To solve these problems, an optimal mode transition matrix IMM (OMTM-IMM) algorithm is proposed in this paper. The linear minimum variance theory is used to calculate the mode transition matrix which depends on the continuous system state rather than the sojourn times in this algorithm. Moreover, the correlation of the subfilter is considered; hence the covariance matrices are utilized to compute mode transition matrix. In this algorithm, the model probability is defined as a diagonal matrix which is combined with the filters outputs; thus the effects produced by each state can be distinguished. Finally, to verify the superiority of the new algorithm, the theoretical proof and simulation results are given. They show that the OMTM-IMM algorithm can improve the tracking accuracy and can be utilized in the complex environment.

Adaptive Interacting Multiple Model Unscented Particle Filter Tracking Algorithm

2012 ◽  
Vol 190-191 ◽  
pp. 906-910 ◽  
Author(s):  
Hong Jiang Liu
Keyword(s):  
Probability Density Function ◽  
Particle Filter ◽  
Probability Density ◽  
Real Time ◽  
Density Function ◽  
Tracking System ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Unscented Particle Filter

In order to study the tracking problem of maneuvering image sequence target in complex environment with multi-sensor array, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The motion array tracking system dynamic model is established, and initialized probability density function also is defined based on unscented transformation, after that, the measured covariance and state covariance are online adjusted by measured residual and adaptive factor, then the self-adapting capability of filter gain and the real-time capability of posterior probability density function are improved. Finally, the simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.

An Adaptive Interacting Multiple Model for Vehicle Target Tracking Method

2013 ◽  
Vol 718-720 ◽  
pp. 1286-1289 ◽  
Author(s):  
Jin Song Du ◽  
Xin Bi
Keyword(s):  
Target Tracking ◽  
Traffic Safety ◽  
Information Structure ◽  
Likelihood Function ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Target Movement ◽  
Movement Model ◽  
Accelerated Motion

In the field of traffic safety vehicle target tracking prediction as the background, this paper proposes an adaptive interacting multiple model tracking algorithm. According to the field of transportation vehicle movement state characteristics, based on the uniform (CV) and uniformly accelerated motion (CA) model, based on new information structure model of motion of the likelihood function, online adaptive adjustment model of the noise variance and the Markov matrix, realization of maneuvering target movement model and model set adaptation, not only improved IMM algorithm for tracking accuracy, and enhances the real-time performance of system, the simulation results show that, the algorithm for tracking precision compared to the traditional IMM method has bigger improvement.

Adaptive Interacting Multiple Model Unscented Particle Filter for Dynamic Acoustic Array

2013 ◽  
Vol 300-301 ◽  
pp. 407-413
Author(s):  
Ya Lei Liu ◽  
Xiao Hui Gu
Keyword(s):  
Probability Density Function ◽  
Particle Filter ◽  
Probability Density ◽  
Real Time ◽  
Density Function ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Unscented Particle Filter ◽  
Acoustic Array

Abstract. In order to improve the tracking accuracy of 3D dynamic acoustic array to 2D maneuvering target in colored noise environment, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The 3D motion acoustic array tracking system dynamic model is established, and initialized probability density function also is defined based on unscented transformation, after that, the measured covariance and state covariance are online adjusted by measured residual and adaptive factor, then the self-adapting capability of filter gain and the real-time capability of posterior probability density function are improved. Finally, the simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.

Interacting Multiple Mode Tracking Algorithm Based on Modified Coordinate Turn Model

2014 ◽  
Vol 610 ◽  
pp. 534-539 ◽  
Author(s):  
Song Xiao ◽  
Xian Si Tan ◽  
Hong Wang
Keyword(s):  
Flight Test ◽  
Tracking Algorithm ◽  
Practical Significance ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Single Model ◽  
Near Space ◽  
Hypersonic Aircraft ◽  
Mode Tracking

The continuing success of near space hypersonic aircraft flight test has become a real threat to China's space attack-defense system, In view of the problem that the single model cannot track such target effectively, an interacting multiple model (IMM) tracking algorithm based on modified cornering model (MCT) was proposed. First the characteristics of near space hypersonic target were analyzed, and then the target real-time angular velocity according to the target motion equation was estimated, finally the near space hypersonic target tracking through the IMM was carried out. The Monte Carlo simulation results show that the IMM tracking algorithm can effectively track near space hypersonic target, and the tracking accuracy and stability are superior to single model, it has certain practical significance.

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