Multipath detection in TDOA localization scenarios

Steffes, Christian; Rau, Sven

doi:10.1109/sdf.2012.6327914

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Beyond the systematic biases observed in the previous section, TDOA measurements are often corrupted by outliers caused by multi-path and NLOS signal propagation. The multi-path effect is the result of the reflection of radio waves, leading to longer ToF and wrong TDOA measurements [21]. In indoor scenarios, metal structures, walls, and obstacles are the major causes of multi-path propagation.…”

Section: Uwb Tdoa Outlier Measurementsmentioning

confidence: 99%

Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots

Zhao

Panerati

Schoellig

2021

Preprint

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Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization is a promising lightweight, low-cost solution that can scale to a large number of devices-making it especially suited for resource-constrained multi-robot applications. However, the localization accuracy of standard, commercially available UWB radios is often insufficient due to significant measurement bias and outliers. In this letter, we address these issues by proposing a robust UWB TDOA localization framework comprising of (i) learning-based bias correction and (ii) M-estimation-based robust filtering to handle outliers. The key properties of our approach are that (i) the learned biases generalize to different UWB anchor setups and (ii) the approach is computationally efficient enough to run on resource-constrained hardware. We demonstrate our approach on a Crazyflie nano-quadcopter. Experimental results show that the proposed localization framework, relying only on the onboard IMU and UWB, provides an average of 42.08% localization error reduction (in three different anchor setups) compared to the baseline approach without bias compensation. We also show autonomous trajectory tracking on a quadcopter using our UWB TDOA localization approach.

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Section: Uwb Tdoa Outlier Measurementsmentioning

confidence: 99%

Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots

Zhao

Panerati

Schoellig

2021

Preprint

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“…Existing range measurement techniques for RFID-based application include the time of arrival (TOA) technique [1][2][3][4], the time difference of arrival (TDOA) technique [5,6], the phase of arrival (POA) technique [7][8][9][10][11][12][13], and the received signal strength indicators (RSSI) technique [14,15]. The TOA technique measures the one-way propagation time, and the distance between the transmitter and the receiver can be calculated using the propagation velocity of the signal.…”

Section: Introductionmentioning

confidence: 99%

An improved k-NN algorithm for localization in multipath environments

Zhao

Liu

et al. 2014

J Wireless Com Network

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To improve the localization accuracy in multipath environments, this paper presents an effective localization approach with the utilization of reference tags. In this approach, an improved k-nearest neighbor (k-NN) algorithm is proposed based on radio-frequency (RF) phases. The traditional k-NN algorithm only focuses on the weighting factors of the coordinates of the selected reference tags, while the improved k-NN algorithm aims at the estimation of direct distance from a reader antenna to a target tag. Then, the location is estimated by linear least squares with a new reference selection scheme. Simulation results show that our approach is superior to the traditional localization approaches under multipath environments. In addition, we conclude that phase has the superiority over strength in the selection of reference tags for range estimation, and range estimation is more accurate than coordinate estimation with k-NN algorithm for localization.

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An Effective Phase-Based Localization Approach Under Multipath Environments

Zhao

Liu

et al. 2013

The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems

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Multipath detection in TDOA localization scenarios

Cited by 4 publications

References 8 publications

Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots

Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots

An improved k-NN algorithm for localization in multipath environments

An Effective Phase-Based Localization Approach Under Multipath Environments

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