Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach

Abstract

In localization estimation systems, it is well known that the sensor-emitter geometry can seriously impact the accuracy of the location estimate. In this paper, time-difference-of-arrival (TDOA) localization is applied to locate the emitter using unmanned aerial vehicle (UAV) swarms equipped with TDOA-based sensors. Different from existing studies where the variance of measurement noises is assumed to be independent and changeless, we consider a more realistic model where the variance is sensor-emitter distance-dependent. First, the measurements model and variance model based on signal-to-noise ratio (SNR) are considered. Then the Cramer-Rao low bound (CRLB) is calculated and the optimal configuration is analyzed via the distance rule and angle rule. The sensor management problem of optimizing UAVs trajectories is studied by generating a sequence of waypoints based on CRLB. Simulation results show that path optimization enhances the localization accuracy and stability.

Finding the Right Place: Sensor Placement for UWB Time Difference of Arrival Localization in Cluttered Indoor Environments

“…a single location of interest, also called target point [4]. In this context, the TDOA optimal sensor geometry problem for both static and dynamic sensor setups was considered in [8], and extended to 3D in [9]. In its general form, the sensor placement problem for multiple target points has been shown to be NP-hard [10].…”

Civilian UAV Deployment Framework in Qatar

“…(1) methods for structural control and self-assembly [9,11]; (2) techniques for navigation and search functions [12,13]; (3) strategies for solving optimisation issues [14]. Despite Qatar's wealth and significant technological investments, UAV applications remain lacking compared to those in other countries.…”

Review: Using Unmanned Aerial Vehicles (UAVs) as Mobile Sensing Platforms (MSPs) for Disaster Response, Civil Security and Public Safety

“…Such collectives of cooperating devices often solve problems far more efficiently than a single device, especially in environments subject to continuous change. In [12] the authors identify 3 categories of applications which are currently attracting a lot of attention from the community: (1) formation control and self-assembly methods ( [11,[13][14][15][16][17][18][19]), (2) localization and search methods ( [20][21][22][23][24]) and (3) techniques for solving optimization problems ( [25][26][27]). Furthermore, ref [12] highlights the ability to sense as an integral basic functionality.…”