Article
Multi-UAV Doppler Information Fusion for Target
Tracking Based on Distributed High Degrees
Information Filters
Hamza Benzerrouk
1,∗
ID
, Alexander Nebylov
2
and Meng Li
1
1
Department of Electrical Engineering, Polytechnique de Montréal, 2900 Boulevard Edouard-Montpetit,
Montréal, QC H3T 1J4, Canada; meng.li@polymtl.ca
2
Department of Aerospace Measuring and Computing Systems, Saint Petersburg State University of
Aerospace Instrumentation, 67 Bolshaya Morskaya, Sankt-Peterburg 190000, Russia; nebylov@aanet.ru
* Correspondence: hamza.benzerrouk@polymtl.ca; Tel.: +1-514-340-5121 (ext. 3279)
Received: 1 January 2018; Accepted: 22 February 2018; Published: 8 March 2018
Abstract:
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 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.
Keywords:
target tracking; Kalman filtering; multi-sensor fusion; information fusion; high-degree
cubature; Gauss–Hermite quadrature; Doppler shift; multi-UAV
1. Introduction
Previous works have selected the Gauss–Hermite Kalman filter (GHKF) and its information
version (GHIF) as the more accurate nonlinear filter for different multi-sensor fusion and target
tracking problems, but did not propose any alternative to its high computational complexity and its
limited implementation in practice. A special case, when sensors are non-stationary, has not been
well investigated, which is assumed in this paper with high-speed dynamic unmanned aerial vehicles
(UAVs). It is well-known that GHKFs are impracticable for medium- and high-dimensional systems; the
curse of the dimensionality problem existing in the tensor product-based Gauss–Hermite information
filter can be elegantly alleviated using the novel seventh-degree cubature information filter derived
by authors in [
1
,
2
]. In parallel to estimation accuracy and the alleviated computation complexity, the
7
th
-degree cubature information filter (CIF) has also been tested and analyzed against small and high
non-Gaussian measurement noise statistics. In the frame of information space, a modification of the
Kalman filter is processed, where the state estimates and their corresponding covariance are replaced
by the information matrices and corresponding vectors, respectively, in the information space [
3
].
Aerospace 2018, 5, 28; doi:10.3390/aerospace5010028 www.mdpi.com/journal/aerospace
