Passive tracking algorithm of single sensor based on multi-hypothesis unscented Kalman filter

2009 ◽  
Author(s):  
Liu Xiaohua ◽  
Xiu Jianjuan ◽  
Wang Guohong
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Tracking Algorithm ◽  
Passive Tracking ◽  
Single Sensor

Joint Registration and State Estimation for Two-Station Passive Tracking

2013 ◽  
Vol 427-429 ◽  
pp. 675-679 ◽  
Author(s):  
Qiang Zhu ◽  
Jian Xun Li
Keyword(s):  
Computer Simulation ◽  
Kalman Filter ◽  
Expectation Maximization ◽  
Unscented Kalman Filter ◽  
System Model ◽  
Fusion Algorithm ◽  
Factors Affecting ◽  
Measurement Function ◽  
Passive Tracking ◽  
Locating System

Registration and nonlinearity are two crucial factors affecting the performance of the two-station passive locating system. In this paper, an online joint registration and data fusion algorithm is proposed to estimate the sensor bias and target state simultaneously using the angle-only measurements from the two ownship stations. The system model of the passive radar is firstly developed followed by the expectation-maximization (EM) approach dealing with the derivation of maximum likelihood (ML) function of the complete data. The unscented Kalman filter (UKF) is chosen to alleviate the influence caused by nonlinearity generated in the measurement function. Computer simulation shows that the proposed method is effective and reliable for this specific tracking scenario.

A Maneuvering Target Tracking Algorithm Based on Gaussian Filter for Multiple Passive Sensors

2011 ◽  
Vol 467-469 ◽  
pp. 447-452
Author(s):  
Jin Long Yang ◽  
Hong Bing Ji ◽  
Jin Mang Liu
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Nonlinear Problem ◽  
Unscented Kalman Filter ◽  
Tracking Algorithm ◽  
Gaussian Filter ◽  
Maneuvering Target Tracking ◽  
Selection Scheme ◽  
Maneuvering Target ◽  
Passive Sensors

When tracking a maneuvering target by multiple passive sensors, two problems need to be considered, one is the nonlinear problem, another is the maneuvering problem. Taking these into account, a Gaussian filter (GF) for nonlinear Bayesian estimation is introduced based on a deterministic sample selection scheme, which can solve the nonlinear problem better than the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Then, a new maneuvering target tracking algorithm is proposed based on the GF and Interacting Multiple Mode (IMM), called IMM-GF method in this paper. Simulation results show that the proposed method has better performance than the IMM-EKF and IMM-UKF in tracking a maneuvering target for multiple passive sensors.

scholarly journals A Horizontal Tracking Algorithm Suitable for Airborne Collision Avoidance System

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Yuchen Wang ◽  
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Collision Avoidance ◽  
Unscented Kalman Filter ◽  
Simulation Method ◽  
Tracking Algorithm ◽  
Polar Coordinates ◽  
Tracking Model ◽  
Collision Avoidance System ◽  
Linear State

The horizontal tracker is essential for the reliable operation of the Airborne Collision Avoidance System (ACAS). In ACAS target tracking, for the non-linear state estimation problem of using sensor measurement values to track in Cartesian coordinates, this paper proposes a horizontal tracking algorithm based on the Augmented Unscented Kalman Filter (AUKF) to achieve the horizontal of the target. Accurate tracking of direction. Firstly, the tracking model of the relative horizontal state of the target aircraft is established, and then the data is processed by AUKF. In order to verify the effectiveness of the horizontal tracking algorithm, the computer simulation method is used to simulate the track of the local aircraft and the intruder in the horizontal direction, and noise is added to the measured values. The traditional Kalman Filter In Polar Coordinates (KFPC) and AUKF algorithm are used to filter the ACAS horizontal tracking. The simulation results show that AUKF can achieve more accurate target tracking.

A small UAV tracking algorithm based on AIMM-UKF

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhiwen Hou ◽  
Fanliang Bu
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Velocity Model ◽  
Tracking Algorithm ◽  
Observation Data ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Content Type ◽  
Small Uav ◽  
Kalman Filter Algorithm

Purpose The purpose of this study is to establish an effective tracking algorithm for small unmanned aerial vehicles (UAVs) based on interacting multiple model (IMM) to take timely countermeasures against illegal flying UAVs. Design/methodology/approach In this paper, based on the constant velocity model (CV), the maneuvering adaptive current statistical model (CS) and the angular velocity adaptive three-dimensional (3D) fixed center constant speed rate constant steering rate model, a small UAV tracking algorithm based on adaptive interacting multiple model (AIMM-UKF) is proposed. In addition, an adaptive robust filter is added to each model of the algorithm. The linear Kalman filter algorithm is attached to the CV model and the CS model and the unscented Kalman filter algorithm (UKF) is attached to the CSCDR model to solve the nonlinearity of the 3D turning model. Findings Monte-Carlo simulation comparison with the other two IMM tracking algorithms shows that in the case of different movement modes and maneuvering strength of the UAV, the AIMM-UKF algorithm makes a good trade-off between the amount of calculation and filtering accuracy, which can maintain more accurate and stable tracking and has strong robustness. At the same time, after testing the actual observation data of the UAV, the results show that the AIMM-UKF algorithm state estimation trajectory can be regarded as an actual trajectory in practical engineering applications, which has good practical value. Originality/value This paper presents a new small UAV tracking algorithm based on IMM and the advantages and practicability of this algorithm compared with existing algorithms are proved through experiments.

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