Constructing a continuous phase time history from TDMA signals for opportunistic navigation

Pesyna, Kenneth M.; Kassas, Zaher M.; Humphreys, Todd E.

doi:10.1109/plans.2012.6236977

Cited by 38 publications

(31 citation statements)

References 17 publications

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“…Then, the mutual information between and , which measures the expected reduction in entropy in one random vector due to the observation of another, can be shown through the Kullback-Leibler divergence to be given by [48] (11) (12) Therefore, to maximize , one can either maximize the right-hand side of (11) or (12). Note that the mapping that was applied to transform the Cartesian coordinate frame to polar, i.e., , is a bijective mapping; hence, maximizing the right-hand side of (11) also maximizes (12) in whichever coordinate frame. One can interpret as the prediction error covariance .…”

Section: E Relationship Between D-optimality and Mildmentioning

confidence: 99%

“…This paradigm, termed opportunistic navigation (OpNav), aims to extract positioning and timing information from ambient radio frequency signals of opportunity (SOPs). These signals include cellular code division multiple access (CDMA) signals [8], [9], digital television vestigial sideband (VSB) signals [10], [11], Iridium satellite time division multiple access (TDMA) signals [12], and orthogonal frequency division multiplexing (OFDM) signals [13]. In collaborative OpNav (COpNav), multiple OpNav receivers share information to construct and continuously refine a global signal landscape [14].…”

Section: Introductionmentioning

confidence: 99%

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Greedy Motion Planning for Simultaneous Signal Landscape Mapping and Receiver Localization

Kassas

Arapostathis

Humphreys

2015

IEEE J. Sel. Top. Signal Process.

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Greedy motion planning strategies to enhance situational awareness in an opportunistic navigation (OpNav) environment are considered. An OpNav environment can be thought of as a radio frequency signal landscape within which a receiver locates itself in time and space by extracting information from ambient signals of opportunity (SOPs). The receiver is assumed to draw only pseudorange observations from the SOPs. The following problem is considered. A receiver with no a priori knowledge about its own initial states nor the initial states of multiple SOPs, except for one, is dropped in an OpNav environment. Assuming that the receiver can prescribe its maneuvers, what greedy (i.e., one-step look-ahead) motion planning strategy should the receiver adopt so to optimally build a high-fidelity signal landscape map of the environment while simultaneously localizing itself within this map in time and space with high accuracy? Several information-based and innovationbased motion planning strategies are studied. It is shown that with proper reformulation, the innovation-based strategies can be cast as tractable convex programs, the solution of which is computationally efficient. Simulation results are presented comparing the various strategies and illustrating the improvements gained from adopting the proposed strategies over random and predefined receiver trajectories.

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Section: E Relationship Between D-optimality and Mildmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Greedy Motion Planning for Simultaneous Signal Landscape Mapping and Receiver Localization

Kassas

Arapostathis

Humphreys

2015

IEEE J. Sel. Top. Signal Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…An alternative approach to these map‐based and sensor‐fusion–based approaches has emerged over the past decade. It exploits ambient signals of opportunity (SOPs), such as cellular, digital television, AM/FM, WiFi, and iridium satellite signals . Among the different SOPs, cellular signals are particularly attractive due to their ubiquity, geometric diversity, high received power, and large bandwidth .…”

Section: Introductionmentioning

confidence: 99%

LTE receiver design and multipath analysis for navigation in urban environments

2018

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Mitigating multipath of cellular long‐term evolution (LTE) signals for robust positioning in urban environments is considered. A computationally efficient receiver, which uses a phase‐locked loop (PLL)–aided delay‐locked loop (DLL) to track the received LTE signals, is presented. The PLL‐aided DLL uses orthogonal frequency division multiplexing (OFDM)–based discriminator functions to estimate and track the time‐of‐arrival. The code phase and carrier phase performances in an additive white Gaussian noise (AWGN) channel are evaluated numerically. The effects of multipath on the code phase and carrier phase are analyzed, demonstrating robust multipath mitigation for high transmission LTE bandwidths. The average of the DLL discriminator functions over multiple LTE symbols is presented to reduce the pseudorange error. The proposed receiver is evaluated on a ground vehicle in an urban environment. Experimental results show a root mean square error (RMSE) of 3.17 m, a standard deviation of 1.06 m, and a maximum error of 6.58 m between the proposed LTE receiver and the GPS navigation solution over a 1.44 km trajectory. The accuracy of the obtained pseudoranges with the proposed receiver is compared against two algorithms: estimation of signal parameters by rotational invariance techniques (ESPRIT) and EKAT (ESPRIT and Kalman filter).

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“…Most of the SoOP navigation uses signals with stable properties that are well known a priori. Examples of electromagnetic spectrum-based SoOP include AM/FM radio, [9][10][11] cellular phone, [12][13][14][15] digital television, [16][17][18] iridium 19 or other communication satellites, 20 WiFi, [21][22][23][24] VLF signals, 25,26 or combinations of signals. [27][28][29] These SoOP approaches make use of a priori information about their signals in order to derive navigation information.…”

Section: Introductionmentioning

confidence: 99%

Navigation using VLF signals with artificial neural networks

2018

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This examines the use of very low frequency (VLF) electromagnetic signals with artificial neural networks (ANN) to estimate two‐dimensional location. The VLF source positions and type of signals are not known in advance. VLF signals were collected from the environment using a mobile antenna while the user manually tagged the true location. Data were collected over several months. The signals were divided into time segments, and features were generated for the ANN from the spectral power of the signals over time. The ANN uses long short‐term memory (LSTM) layers and fully connected layers and was trained using the time‐series spectral features of the VLF signal and the true location data. The position estimates are an indoor locale‐classification problem. Our task was to determine which of 50 rooms or hallway segments the sensor was in for a given building. The system achieved an average accuracy over 76% in determining discrete rooms or hallways. The approach achieved these exceptional accuracies with very little a priori knowledge of the VLF signals using only supervised learning.

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Constructing a continuous phase time history from TDMA signals for opportunistic navigation

Cited by 38 publications

References 17 publications

Greedy Motion Planning for Simultaneous Signal Landscape Mapping and Receiver Localization

Greedy Motion Planning for Simultaneous Signal Landscape Mapping and Receiver Localization

LTE receiver design and multipath analysis for navigation in urban environments

Navigation using VLF signals with artificial neural networks

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