Wireless Sensor Network Optimization for Constrained Environments With Limited a Priori Target Information
In target localization problems, positioning between sensor networks and targets plays a critical role in localization estimation. Because estimation performance is strongly dependent on sensor-target relative positions, many analytically optimal expressions for sensor positions have been constructe...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10855424/ |
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| Summary: | In target localization problems, positioning between sensor networks and targets plays a critical role in localization estimation. Because estimation performance is strongly dependent on sensor-target relative positions, many analytically optimal expressions for sensor positions have been constructed as a function of target locations under ideal circumstance. But as more fields have adopted wireless sensing networks, these analytically-optimal placements are not applicable under physical limitations, including placement constraints, network limitations, and sensor modalities. Similarly, these analytically optimal expressions assume the target position is known a priori, but is not a valid assumption for many practical applications. To address this limitation, this paper defines a procedure for optimally placing sensors in a heterogeneous wireless sensor network for localizing targets without an estimate of the target location a priori. We address this by defining a general region where the target may exist, and characterize the network’s sensing performance using the Fisher information matrix. We consider a heterogeneous network of arbitrary size under distance-dependent noise as we search for and continue to optimally monitor a set of unknown static target positions. We define this optimization problem to generally include physical constraints, which are much more practical for most localization applications. Extensive simulations are conducted under two different environmental monitoring example problems to corroborate our approach and demonstrate its range and flexibility. This work may find application in wireless sensor network planning and design for defense, environmental monitoring, surveillance, or autonomous system applications. |
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| ISSN: | 2169-3536 |