TDoA Based UAV Localization Using Dual-EKF Algorithm

Lee, S. C.; Lee, Wooram; You, Kwanho

doi:10.1007/978-3-642-10741-2_6

Cited by 26 publications

(17 citation statements)

References 7 publications

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“…On the other hand, localization has been addressed using triangulation [7], least square methods [8], and Bayesian filters [9][10][11][12]7]. In all of the above type of the localization approaches, the hills and mountains between an RF source and a UAV (Fig.…”

Section: Introductionmentioning

confidence: 98%

“…Localization of RF sources based on the different measured data include Received Signal Strength Indication (RSSI), Differential Received Signal Strength Indication (DRSSI) [1], signal strength ratio [2], Angle Of Arrival (AOA) [3][4][5], Time of Arrival (TOA), and Time Differential of Arrival (TDOA) [6] has been studied in many researches and still efforts to improve the accuracy of localization is continuing. On the other hand, localization has been addressed using triangulation [7], least square methods [8], and Bayesian filters [9][10][11][12]7].…”

Section: Introductionmentioning

confidence: 99%

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A geometrical approach for aerial cooperative obstacle mapping using RSSI observations

Dehghan

Moradi

2014

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

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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.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A geometrical approach for aerial cooperative obstacle mapping using RSSI observations

Dehghan

Moradi

2014

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

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“…Contemporary examples include locating calling mammals in the ocean 5,14,24,29 , sounds in a room via robots 6 , ships 26 , cell phones 13 , lightning 11 , wildlife radio transmitters 9 , aircraft radio emissions 10 , bistatic sonars 3 , and theoretical developments 7 . These models derive locations by translating TDOA or bistatic signal times to difference or sum of distance assuming signal speed is constant 3, [5][6][7][9][10][11]13,14,26 , the overwhelmingly-common case, or are constrained to a finite interval 24,29 , e.g. [1450 , 1500] m/s for the ocean.…”

Section: Introductionmentioning

confidence: 99%

Locating calling mammals with signal times amongst shadows and black holes in two-dimensional models

Spiesberger

2019

The Journal of the Acoustical Society of America

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Calling mammals, ships, and many other objects have been commonly located during the last century with two-dimensional (2D) models from measurements of signal time even when the objects are not on the 2D surface. The overwhelmingly common method for locating signals with 2D models takes signal speed as constant. Distance is computed by multiplying this speed by signal time. For monostatic, bistatic, and Time Differences of Arrival (TDOA) measurements, the distances constrain locations to circles, ellipses, and hyperbolas respectively, whose intersections yield location. However, the speed needed to obtain correct locations depends on the horizontal separation between object and instrument. In fact, if their horizontal separation is zero the speed needed for correct location must also be zero. In light of this singularity, methods are derived for generating extremely reliable confidence intervals for location and identifying regions of the 2D model where a 3D model is needed. Because speeds needed for correct location are spatially in-homogeneous in the extreme, isosigmachrons and isodiachrons emerge as natural geometries for interpreting location instead of ellipses and hyperbolas. These issues are caused by choice of coordinates, and the same phenomena occur in general relativity regarding the speed of light and black holes.

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“…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

Dehghan

Moradi

Shahidian

2014

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

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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.

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TDoA Based UAV Localization Using Dual-EKF Algorithm

Abstract: Most UAV (unmaned aerial vehicle)

Cited by 26 publications

References 7 publications

A geometrical approach for aerial cooperative obstacle mapping using RSSI observations

A geometrical approach for aerial cooperative obstacle mapping using RSSI observations

Locating calling mammals with signal times amongst shadows and black holes in two-dimensional models

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

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