As a result of recent technological advances in modernized sensor sets and sensor platforms, sensor management combined with sensor platform path planning are studied to conduct intelligence, surveillance and reconnaissance (ISR) operations in novel ways.
This thesis addresses the path planning and sensor management for aerial vehicles to cover areas of interest (AOIs), scan objects of interest (OOIs) and/or track multiple detected targets in surveillance missions.
The problems in this thesis, which include 1) the spatio-temporal coordination of sensor platforms to observe AOIs or OOIs, 2) the optimal sensor geometry and path planning for localization and tracking of targets in a mobile three-dimensional (3D) space, and 3) the scheduling of sensors working in different (i.e., active and passive) modes combined with path planning to track targets in the presence of jammers, emerge from real-world demands and scenarios.
The platform path planning combined with sensor management is formulated as optimization problems with problem-dependent performance evaluation metrics and constraints.
Firstly,
to cover disjoint AOIs over an extended time horizon using multiple aerial vehicles for persistent surveillance,
a joint multi-period coverage path planning and temporal scheduling, which allows revisiting in a single-period path, is formulated as a combinatorial optimization with novel objective functions.
Secondly,
to use a group of unmanned aerial vehicles (UAVs) cooperatively carrying out search-and-track (SAT) in a mobile 3D space with a number of targets,
a joint path planning and scanning (JPPS) is formulated based on the predictive information gathered from the search space.
The optimal 3D sensor geometry for target localization is also analyzed with the objective to minimize the estimation uncertainty under constraints on sensor altitude, sensor-to-sensor and sensor-to-target distances for active or passive sensors.
At last,
to accurately track targets in the presence of jammers broadcasting wide-band noise by taking advantage of the platform path planning and the jammer's information captured by passive sensors,
a joint path planning and active-passive scheduling (JPPAPS) strategy is developed based on the predicted tracking performance at the future time steps in a 3D contested environment.
The constraints on platform kinematic, flyable area and sensing capacity are included in these optimization problems.
For these multisensor path planning and decision making, solution techniques based on the genetic algorithm are developed with specific chromosome representations and custom genetic operators using either the non-dominated sorting multiobjective optimization (MOO) architecture or the weighted-sum MOO architecture.
Simulation results illustrate the performance and advantage of the proposed strategies and methods in real-world surveillance scenarios. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/23704 |
| Date | January 2018 |
| Creators | Wang, Yinghui |
| Contributors | Kirubarajan, Thia, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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