Multi-UAV Doppler Information Fusion for Target Tracking Based on Distributed High Degrees Information Filters

Hamza Benzerrouk, Alexander Nebylov, Meng Li
2018 Aerospace (Basel)  
Multi-Unmanned Aerial Vehicle (UAV) Doppler-based target tracking has not been widely investigated, specifically when using modern nonlinear information filters. A high-degree Gauss-Hermite information filter, as well as a seventh-degree cubature information filter (CIF), is developed to improve the fifth-degree and third-degree CIFs proposed in the most recent related literature. These algorithms are applied to maneuvering target tracking based on Radar Doppler range/range rate signals. To
more » ... ate signals. To achieve this purpose, different measurement models such as range-only, range rate, and bearing-only tracking are used in the simulations. In this paper, the mobile sensor target tracking problem is addressed and solved by a higher-degree class of quadrature information filters (HQIFs). A centralized fusion architecture based on distributed information filtering is proposed, and yielded excellent results. Three high dynamic UAVs are simulated with synchronized Doppler measurement broadcasted in parallel channels to the control center for global information fusion. Interesting results are obtained, with the superiority of certain classes of higher-degree quadrature information filters. The nonlinear filtering problem statement remains the same, but it becomes more attractive in a centralized fusion based on distributed information processing, and is given by the following equations: (1) In this paper, a 7 th -degree and multiple 5 th -degree cubature and quadrature information filters are derived and applied to multi-sensor information fusion multi-UAV target tracking. The 5 th -degree Gauss-Hermite information filter has clearly demonstrated its superiority to the 5 th -degree and 3 rd -degree cubature information filters. The seventh-degree cubature information filter (7 th degree-CIF) has been demonstrated to be a stronger algorithm with higher precision than previous lower-degree versions such as the fifth-and third-degree cubature Kalman filter (CKF). Nevertheless, it has yielded slightly lower performances compared to the 5 th -degree Gauss-Hermite information filter [4, 5] . The sensor considered is a pulse-Doppler Radar which produces a measurement vector containing the range, azimuth, elevation, and Doppler; see Figure 1 . The Doppler measurement is defined as the target speed in the sensor direction. Doppler radars can measure the component of the velocity of targets toward or away from the radar. Therefore, the Doppler is not expressed as a frequency in Hz, but as a speed in m/s. In this paper, tracking in Cartesian coordinates is considered, for which the state vector contains at least the position and speed in the x, y, and turn rate [6, 7] . 3km 15km 3-4km
doi:10.3390/aerospace5010028 fatcat:xfadt5sunjg6rcjsbebamgdwmq