The Extended Invariance Principle for Signal Parameter Estimation in an Unknown Spatial Field

Antreich, Felix; Nossek, Josef A.; Swindlehurst, A. Lee

doi:10.1109/tsp.2011.2140107

Cited by 31 publications

(10 citation statements)

References 37 publications

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“…Thus, multi-antenna GNSS receivers became the focus of research on resilient positioning withstanding not only multipath but also interference and spoofing. In addition to beamforming approaches [4], [5], multi-dimensional parameter estimation approaches [6], [7], and other approaches as those proposed in [8] and [9], tensor-based decomposition methods showed significant improvements over matrix-based decomposition methods. A tensor-based decomposition features uniqueness, improves the identifiability of the parameters, and the tensor structure permits efficient denoising of the received signal.…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios

et al. 2020

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Global Navigation Satellite Systems (GNSS) are crucial for applications that demand very accurate positioning. Tensor-based time-delay estimation methods, such as CPD-GEVD, DoA/KRF, and SECSI, combined with the GPS3 L1C signal, are capable of, significantly, mitigating the positioning degradation caused by multipath components. However, even though these schemes require an estimated model order, they assume that the number of multipath components is constant. In GNSS applications, the number of multipath components is time-varying in dynamic scenarios. Thus, in this paper, we propose a tensor-based framework with model order selection and high accuracy factor decomposition for timedelay estimation in dynamic multipath scenarios. Our proposed approach exploits the estimates of the model order for each slice by grouping the data tensor slices into sub-tensors to provide high accuracy factor decomposition. We further enhance the proposed approach by incorporating the tensor-based Multiple Denoising (MuDe). INDEX TERMS Global Navigation Satellite Systems (GNSS), Global Positioning System (GPS), GPS3, Time-delay estimation (TDE), Multipath components, Model order selection (MOS).

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Section: Introductionmentioning

confidence: 99%

Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios

et al. 2020

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“…By combining the spatial and time (space-time) domain, the suppression capabilities can be further increased, especially for wide-band and high dynamic interferences [17]. Furthermore, high-resolution parameter estimation algorithms can jointly mitigate multipath and RFI, and provide highly accurate results [20,21]. This is achieved by separating the LOS component from reflections [20].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, high-resolution parameter estimation algorithms can jointly mitigate multipath and RFI, and provide highly accurate results [20,21]. This is achieved by separating the LOS component from reflections [20]. However, these methods entail rather high complexity in the parameter estimation as multi-dimensional nonlinear problems have to be solved.…”

Section: Introductionmentioning

confidence: 99%

Interference and multipath suppression with space-time adaptive beamforming for safety-of-life maritime applications

et al. 2019

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In this work, we present mitigation algorithms to protect GNSS receivers against malicious interference. Maritime applications with an antenna array-based receiver are considered as a use case. A two-stage mitigation algorithm, that tackles multipath and radio frequency interference (RFI), caused by personal privacy devices (PPD) or additive white Gaussian noise (AWGN) interferers is presented. Our approach consists of a pre-whitening step, followed by a space-time adaptive principle component analysis (PCA) beamformer that uses a dimensionality reduction (i.e. compression) method based on Canonical Components (CC) with a bank of signal-matched correlators. The algorithms are capable of suppressing strong RFI and separating highly correlated and even coherent multipath signals, thus achieving a reliable time delay and, therefore, pseudorange estimation performance. Finally, we evaluate and compare the proposed algorithms not only via numerical simulations but also with real data collected from a measurement campaign performed at DLR's maritime jamming testbed in the Baltic sea. A complete description of the test platform and the scenarios is provided.

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“…Knowledge of the DOA offers outstanding benefits, such as spatial filtering. We refer here to many important DOA estimation methods, such as Iterative Quadratic Maximum Likelihood (IQML) [2], Root-WSF [3] and Root-MUSIC [4], Expectation Maximization (EM) [5], [6], [7], [8], the space Alternating Generalized Expectation Maximization (SAGE) [9], [10], [11] and Estimation of Signal Parameters via Rotational Invariance (ESPRIT) [12], which can be applied.…”

Section: Introductionmentioning

confidence: 99%

Continuous Authentication via Localization Using Triangulation of Directions of Arrival of Line of Sight Components

Marinho¹,

Gondim²,

Costa³

2015

Proceedings of the the Ninth International Conference on Forensic Computer Science

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A larger number of users work from a desktop computer and use their smartphones, tablets, and home computers to communicate, buy, organize, and store sensitive information. With the growth of the adoption of the Internet for tasks such as online banking and shopping, an increased focus has been given on the development of tools that enable secure transactions. This manuscript proposes the usage of direction of arrival estimation tools to provide continuous authentication. The location of a user within the network can be estimated by using triangulation of the user's wireless signal. The location estimates can be used to track a user's movement within a wireless network. The movement pattern can then be analyzed for possible indicators of fraud.

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The Extended Invariance Principle for Signal Parameter Estimation in an Unknown Spatial Field

Cited by 31 publications

References 37 publications

Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios

Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios

Interference and multipath suppression with space-time adaptive beamforming for safety-of-life maritime applications

Continuous Authentication via Localization Using Triangulation of Directions of Arrival of Line of Sight Components

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