SLAM-inspired simultaneous localization of UAV and RF sources with unknown transmitted power

Abstract

In the unmanned aerial vehicle (UAV) based localization of slow-moving radio frequency (RF) sources with unknown transmitted strength of signal, such as a person with a cell phone in a search and rescue mission, the UAV navigation errors are significant sources of localization error. Although the use of a global positioning system (GPS) can reduce the UAV's localization error significantly resulting in more accurate RF source localization, if the GPS signal is lost temporarily or permanently, the accuracy of the UAV-based localization decreases rapidly. In this paper, a simultaneous localization and mapping (SLAM)-inspired approach for simultaneous localization of UAV and RF sources (SLUS) is proposed. The proposed approach solves these two connected problems, i.e. the UAV localization and RF source localization, simultaneously to decrease the error of the UAV position estimation and the error of the RF source localization. In the proposed approach, beside the UAV position prediction, the RF source position prediction is also performed. Then the predicted states are augmented and the augmented predicted state information is corrected using range-ratio and bearing observations, i.e. RF source features, considering the unknown transmitted power. The proposed approach is simulated and the results show that the normal divergence of a target localization and the divergence of the UAV navigation in latitude and longitude channels have been eliminated using this approach. In other words, simultaneous localization of the UAV and RF sources uses the RF sources, as features in the environment, to aid the navigation system. Although the approach is similar to the mapping of RF sources in the environment, the created map would be useless after finding the RF sources. That is why SLUS has been used instead of SLAM. The main contributions of this work are: 1) performing simultaneous localization of a UAV and targets using RF signals, especially in a non-line of sight (NLOS) condition; 2) using difference of signal strength, i.e. differential received strength signal indicator (DRSSI), to eliminate the impact of unknown target signal power; and 3) simultaneous multi-target localization and tracking.

Single- and Multi-UAV Trajectory Control in RF Source Localization

“…The results of the Monte Carlo simulation is based on the averages of 1000 consecutive runs. The initial position of the UAVs is specified randomly in a ring-shaped area with the center on [13,2] T km, the smaller radius of 10 km, and the larger radius of 13 km. The emitter is assumed to be stationary with unknown location for the system.…”

“…The emitter is assumed to be stationary with unknown location for the system. The potential location of the emitter is restricted to a circle-shaped area with the center on [13,2] T km and radius of 3.5 km. The EKF is initialized to s 0|−1 = [13, 2] T and P 0|−1 = diag (3,3).…”

“…Passive localization of radio sources such as mobile phones is consistently used in variety of applications, e.g., search and rescue and surveillance [1][2][3][4]. Aerial localization is highly recommended in avalanches, mountains, forests, etc.…”

Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor

“…UAVs have exhibited performance in many challenging tasks that traditionally require significant human resource such as the inspection of infrastructure [ 2 , 3 , 4 , 5 , 6 ], indoor navigation using simultaneous localization and mapping [ 7 , 8 , 9 ], obstacle avoidance [ 10 , 11 , 12 , 13 , 14 ], terrain reconstruction [ 15 , 16 ], and real-time monitoring [ 17 , 18 ]. Drones can become a valuable tool to support humanitarian actions that aims to analyze characteristic of subject in order to determine the trends for future research directions [ 19 ].…”

Towards Autonomous Modular UAV Missions: The Detection, Geo-Location and Landing Paradigm

“…The significance of UAV autonomy and processing power is demonstrated in several applications including technical infrastructure inspection [2,3,4,5,6], indoor navigation using simultaneous localization and mapping [7,8,9,10], obstacle avoidance [11,12,13,14], terrain reconstruction [15,16,17], and real-time environmental monitoring [18,19]. The flexibility, human safety, cost effectiveness and ease of use facilitates the usage of UAVs to support humanitarian actions in disaster situations [20,21].…”