3-D TDOA/AOA location based on Extended Kalman Filter

Tu, Xiaodong; Zhang, Hao; Cui, Xuerong; Gulliver, T. Aaron

doi:10.1109/isape.2010.5696504

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…Filtering is not a common feature of TDOA/AOA algorithms but is occasionally included. Filtering approaches include Kalman filtering [126] and a Kalman-like Bayesian filter [127].…”

Section: Review Of Tdoa/aoa Multilateration Approachesmentioning

confidence: 99%

A Review of Linear Multilateration Techniques and Applications

Widdison,

Long

2024

IEEE Access

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In multilateration, distances between an unknown location and several known points are used to determine the location of the unknown point. Papers on algorithms for multilateration, such as time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA), have proliferated in the literature. These use the same equations, but are often written in a way that hides the underlying geometry and make it difficult to see the similarity in approaches between different papers. This paper provides a review of TOA and AOA-based localization approaches, with a focus on linear positioning algorithms. This paper presents simple and consistent versions of TOA, TDOA, and AOA position equations with intuitive explanations for how those equations relate to the geometry of the localization problem. This perspective can make it easier to implement these algorithms in a way that produces stable results and provides insight into the underlying geometric problem. We also review the literature relating to TDOA and AOA localization algorithms and multistatic radar.

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“…Filtering is not a common feature of TDOA/AOA algorithms but is occasionally included. Filtering approaches include Kalman filtering [126] and a Kalman-like Bayesian filter [127].…”

Section: Review Of Tdoa/aoa Multilateration Approachesmentioning

confidence: 99%

A Review of Linear Multilateration Techniques and Applications

Widdison,

Long

2024

IEEE Access

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“…Other RF techniques, such as [30], [31] and [32], implement Kalman filtering to improve the accuracy of AoA estimation.…”

Section: Related Workmentioning

confidence: 99%

Angle-of-Arrival Estimation in Formation Flying Satellites: Concept and Demonstration

et al. 2019

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One of the challenges of future space missions is the deployment of instruments distributed across multiple satellites that fly in an autonomous formation. This type of mission requires that the spacecraft communicate using inter-satellite links (ISL) and collaborate to fulfil the role of a large-scale, complex scientific instrument. In this context, precise information regarding the relative positioning of the satellites in terms of distance and orientation is essential. In radio frequency (RF) based techniques, obtaining the relative orientation between the satellites starts with estimating the angle-of-arrival (AoA) of the radio signal. This paper proposes an AoA estimation technique that can be used in Orthogonal Frequency-Division Multiplexing (OFDM) communications. Our approach consists in obtaining the AoA using the ISL, specifically the preamble whose primary roles are data link synchronization and channel estimation. Our technique allows a more accurate measurement of the AoA due to an improved ambiguity resolution of phase measurements. We give theoretical results regarding the accuracy of the AoA estimator and we validate them by tests on a real-time functioning testbed consisting in multiple National Instruments Universal Software Radio Peripheral platforms. We show that the carrier frequency offset introduces only a negligible bias in the AoA estimation and we discuss the effect of multipath propagation. INDEX TERMS Angle-of-arrival, inter-satellite link, orthogonal frequency division multiplexing, universal software radio peripheral.

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“…Figures 4, 5, 6 and 7 show the MAE for S=1, 2,3,4,5,6,7,8 for the CM1 and CM2 channels, and T b =1ns and T b =4ns. The relationship is always that the MAE decreases as S increases.…”

Section: A Mae With Respect To the Normalized Thresholdmentioning

confidence: 99%

“…Positioning technologies can be classified into range based [2,3,6,7] and nonrange based [8]. For example, Time of Arrival (TOA) [7,9] and Time Difference of Arrival (TDOA) [10] are range based techniques, while Received Signal Strength (RSS) and AngleofArrival (AOA) [8] are nonrange based.…”

Section: Introductionmentioning

confidence: 99%

Threshold Selection for Ultra-Wideband TOA Estimation based on Neural Networks

Cui¹,

Zhang²,

Gulliver³

2012

JNW

Self Cite

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Because of the good penetration into many common materials and inherent fine resolution, Ultra-Wideband (UWB) signals are widely used in remote ranging and positioning applications. On the other hand, because of the high sampling rate, coherent Time of Arrival (TOA) estimation algorithms are not practical for low cost, low complexity UWB systems. In order to improve the precision of TOA estimation, an Energy Detection (ED) based non-coherent TOA estimation algorithm using Artificial Neural Networks (ANN) is presented which is based on the skewness after energy detection. The expected values of skewness and kurtosis with respect to the Signal to Noise Ratio (SNR) are investigated. It is shown that the skewness is more suitable for TOA estimation. The best threshold values for different SNRs are investigated and the effects of integration period and channel modes are examined. Comparisons with other ED based algorithms show that in CM1 and CM2 channels, the proposed algorithm provides higher precision and robustness in both high and low SNR environments

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3-D TDOA/AOA location based on Extended Kalman Filter

Cited by 9 publications

References 6 publications

A Review of Linear Multilateration Techniques and Applications

A Review of Linear Multilateration Techniques and Applications

Angle-of-Arrival Estimation in Formation Flying Satellites: Concept and Demonstration

Threshold Selection for Ultra-Wideband TOA Estimation based on Neural Networks

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