Studies on the deployment of sensors mostly involve a 2D plane or 3D volume. However, the optimal sensor deployment in field environments is actually the resource distribution on 3D surfaces. Compared with the traditional deployment environments, field environments are more complicated, owing to some interferences on the detection capability of sensors and limitations on the maneuverability of platforms. In this paper, an optimal sensor deployment algorithm in 3D complex environments is discussed. First, considering the characteristics of field environments, the maneuverability matrix of heterogeneous platforms was introduced as a constraint. Then, a non-isomorphic environment value distribution map was constructed to mark the differences among mission areas. Furthermore, the sensor detection range model was improved to better deal with the occlusion issue. Finally, based on the multi-objective particle swarm optimization (MOPSO) algorithm, a sensor deployment strategy was deployed for complex environments. Experiments demonstrated that the proposed algorithm can better deal with the sensor deployment problem in field environments, while improving the detection accuracy of the objects in mission areas.

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