Aerial localization of an RF source in NLOS condition

Transactions of the Institute of Measurement and Control

2015

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.

Section: Conclusion On the Simulation Resultsmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

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

Transactions of the Institute of Measurement and Control

2015

“…Signal attenuation due to the shadowing has a normal distribution when it is expressed by dB [18]. It should be noted that modeling of diffraction loss decreases the SD of shadowing relatively to the mentioned values in other papers [13,20]. Considering that the total signal loss in terms of dB has a Gaussian distribution, subtracting a determinate amount as path loss, does not change the distribution and its SD.…”

Section: 0mentioning

confidence: 99%

“…In all of the above type of the localization approaches, the hills and mountains between an RF source and a UAV (Fig. 1), which is also called obstacles throughout this paper, have the greatest effect on the observation model and the localization accuracy [13].…”

Section: Introductionmentioning

confidence: 99%

A geometrical approach for aerial cooperative obstacle mapping using RSSI observations

2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)

2014

The effect of an obstacle on signal strength attenuation is the most important reason that affects the performance of distance estimation based on Received Signal Strength Indication (RSSI) observations. Consequently, in this paper, a novel approach for estimation of the location and height of an obstacle between two UAVs using RSSI observations is proposed which would help to better estimate the location of a radio frequency (RF) source. The long-term goal of developing this approach is to improve the localization of an RF source by removing the effect of obstacle(s) on the signal attenuation. This approach is based on path planning of the UAVs to estimate the tip of the obstacle between them. The change in the diffraction loss observations are used to find the Line Of Sight (LOS) positions of the UAVs. These LOS lines, constructed by connecting the UAVs in LOS situation, are the locus of the tip of the obstacle and used in an iterative geometrical approach for estimation of the location of the tip of the obstacle. Due to the uncertainty in determining the LOS position which is created because of the radius of the Fresnel zone, the motion steps of UAVs, and the non-modeled dynamics in signal attenuation, an EKF filter is used to estimate the tip of the obstacle. The approach has been simulated and the results show that the approach provides better accuracy in RF source localization compared to the basic approach which does not consider the obstacles in the localization process.

“…Received Signal Strength (RSS) [1], Differential Signal Strength (DRSS) [2], Angle Of Arrival (AOA) [3], Time Of Arrival (TOA) [4], and Time Difference Of Arrival (TDOA) [5] are different measurements of the received signal which have been used to localize the RF source. And still there are efforts to improve the accuracy in different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Optimal path planning for DRSSI based localization of an RF source by multiple UAVs

2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)

Shahidian

2014

The Radio Frequency (RF) source localization accuracy depends not only on the measurement performance of sensors, but also on the relative location of the sensors and the source. This paper investigates the impact of UAVs, equipped with RSSI sensors, formation and trajectory on the aerial RF source localization performance for Differential Received Signal Strength Indication (DRSSI) based approach in None Line Of Sight (NLOS) propagation condition. To eliminate the need for knowing the power of the signal source the DRSSI approach is applied. The collected measurements in each waypoint are used to estimate the location of the source iteratively by the use of Extended Kalman Filter (EKF). The Cramer-Rao Lower Bound (CRLB), which expresses a lower bound on the variance of any unbiased estimator, is used as the objective function for the proposed algorithm. Due to the complexity of Jacobin calculation to perform global CRLB optimization, the local values of CRLBs in the current waypoint and the next probable waypoints are used to determine the best path. Maximizing the determinant of the inverse of CRLB, i.e. the Fisher Information Matrix, in each measurement instance over the next probable waypoint, minimizes the estimation uncertainty area. The effectiveness of the proposed algorithm is illustrated by Monte-Carlo simulations and compared with the basic bio-inspired approach of going toward the estimated direction of the source.