This paper addresses a multisource localization problem with multiple unmanned aerial vehicles equipped with appropriate sensors coordinating with each other, wherein the sources are simultaneously emitting identical acoustic signals. Distributed coordinated localization algorithms based on multiple range and direction measurements are presented and performances are evaluated in different practically significant mission scenarios. Non-deterministic polynomial (NP) hardness to determine optimal number of unmanned aerial vehicles for a given mission scenario is discussed. Group coordination, tactical path, and goal replan strategies to enable efficient localization of single and multiple acoustic sources have been designed. The localization algorithm along with coordination strategy is verified in the presence of realistic error conditions through simulation.
Timely detection of intruders ensures the safety and security of high valued assets within a protected area. This problem takes on particular significance across international borders and becomes challenging when the terrain is porous, rugged and treacherous in nature. Keeping an effective vigil against intruders on large tracts of land is a tedious task; currently, it is primarily performed by security personnel with automatic detection systems in passive supporting roles. This paper discusses an alternate autonomous approach by utilizing one or more Unmanned Vehicles (UVs), aided by smart sensors on the ground, to detect and localize an intruder. To facilitate autonomous UV operations, the region is equipped with Unattended Ground Sensors (UGSs) and laser fencing. Together, these sensors provide time-stamped location information (node and edge detection) of the intruder to a UV. For security reasons, we assume that the sensors are not networked (a central node can be disabled bringing the whole system down) and so, the UVs must visit the vicinity of the sensors to gather the information therein. This makes the problem challenging in that pursuit must be done with local and likely delayed information. We discretize time and space by considering a 2D grid for the area and unit speed for the UV, i.e. it takes one time unit to travel from one node to an adjacent node. The intruder is slower and takes two time steps to complete the same move. We compute the min–max optimal, i.e. minimum number of steps to capture the intruder under worst-case intruder actions, for different number of rows and columns in the grid and for both one and two pursuers.
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