Performance Bounds for Finite Moving Average Tests in Transient Change Detection

Egea-Roca, Daniel; López-Salcedo, José A.; Seco-Granados, Gonzalo; Poor, H. Vincent

doi:10.1109/tsp.2017.2788416

Cited by 28 publications

(15 citation statements)

References 37 publications

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“…Moreover, the position and rotation angle tracking accuracies are significantly reduced when the LOS link is blocked [43], [55]. So, the LOS link is used for tracking position and rotation angle and either LOS detection methods or online blockage identification approaches can be adopted depending on the applications 17 [57]- [61]. This allows to simply switch to the BS that is in the LOS thanks to the ultra dense networks.…”

Section: B Conversion To Position and Orientationmentioning

confidence: 99%

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Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

Shahmansoori

Uguen

Destino

et al. 2019

IEEE Signal Process. Lett.

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Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. Despite their benefits for achieving high data rate communications, their potential advantages for tracking of the location of the user terminals are largely undiscovered. In this paper, we propose a novel support detection-based channel training method for frequency selective millimeter-wave (mm-wave) multiple-input-multiple-output system with lens antenna arrays. We show that accurate position and orientation estimation and tracking is possible using signals from a single transmitter with lens antenna arrays. Particularly, the beamspace channel estimation is formulated as two sparse signal recovery problems in the downlink and uplink for the estimation of angle-of-arrival, angle-of-departure, and timeof-arrival. The proposed method offers a higher sparse detection probability compared to the compressed sensing based solutions. Finally, a joint heuristic beamformer design and user position and orientation tracking approach are proposed based on initial estimation of channel parameters obtained in the training phase.

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Section: B Conversion To Position and Orientationmentioning

confidence: 99%

“…As an example, blockage can be modeled based on 2-D knife edge diffraction[56]. This allows the receiver to apply a transient change detection algorithm based on the mean change of a complex Gaussian distribution as the signal integrity metric for the LOS blockage identification[57].September 18, 2018 DRAFT…”

mentioning

confidence: 99%

Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

Shahmansoori

Uguen

Destino

et al. 2019

IEEE Signal Process. Lett.

Self Cite

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“…Features for multipath classification were also extracted from the correlation sequences in [18]. Authors in [28] used the code discriminator output and the mean and variance of the slope of the correlation curve to monitor the signal quality. In a more recent paper [29], the authors reported the indoor LOS/MP signal classification performance using a deep leaning approach based on the tracking outputs of a pseudolite signal.…”

Section: Introductionmentioning

confidence: 99%

Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX- and NMEA-Level Measurements

Jia

Luo

et al. 2019

Remote Sensing

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This paper proposes to use a correlator-level global positioning system (GPS) line-of-sight/multipath/non-line-of-sight (LOS/MP/NLOS) signal reception classifier to improve positioning performance in an urban environment. Conventional LOS/MP/NLOS classifiers, referred to as national marine electronics association (NMEA)-level and receiver independent exchange format (RINEX)-level classifiers, are usually performed using attributes extracted from basic observables or measurements such as received signal strength, satellite elevation angle, code pseudorange, etc. The NMEA/RINEX-level classification rate is limited because the complex signal propagation in urban environment is not fully manifested in these end attributes. In this paper, LOS/MP/NLOS features were extracted at the baseband signal processing stage. Multicorrelator is implemented in a GPS software-defined receiver (SDR) and exploited to generate features from the autocorrelation function (ACF). A robust LOS/MP/NLOS classifier using a supervised machine learning algorithm, support vector machine (SVM), is then trained. It is also proposed that the Skymask and code pseudorange double difference observable are used to label the real signal type. Raw GPS intermediate frequency data were collected in urban areas in Hong Kong and were postprocessed using a self-developed SDR, which can easily output correlator-level LOS/MP/NLOS features. The SDR measurements were saved in the file with the format of NMEA and RINEX. A fair comparison among NMEA-, RINEX-, and correlator-level classifiers was then carried out on a common ground. Results show that the correlator-level classifier improves the metric of F1 score by about 25% over the conventional NMEA-and RINEX-level classifiers for testing data collected at different places to that of training data. In addition to this finding, correlator-level classifier is found to be more feasible in practical applications due to its less dependency on surrounding scenarios compared with the NMEA/RINEX-level classifiers.The former, like the conventional GNSS positioning algorithm, still makes use of pseudorange measurements, meanwhile it is aided with the information of satellite visibility or additional path delay of reflected signals obtained using 3D city models. For shadow matching, the basic idea is to compare the measured signal availability and strength with predictions obtained using 3D city models over a range of candidate positions. For detailed implementation of 3DMA GNSS positioning, readers are referred to [5][6][7][8][9][10]. For the 3DMA GNSS positioning, the accuracy of line-of-sight/non-line-of-sight (LOS/NLOS) signal reception classification directly affects its performance [5]. In addition to the 3DMA GNSS positioning, conventional ranging-based least-squares GNSS positioning can also benefit from an accurate signal classification by excluding or down-weighting the identified multipath (MP)/NLOS measurements [11].Various approaches to classifying LOS/NLOS signal have been proposed. As mentioned above, the 3D ...

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“…A type of detection problem closely related to anomaly detection is transient detection [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. In many signal processing applications, it is of interest to detect short-duration statistical changes in observed data.…”

Section: Introductionmentioning

confidence: 99%

Data Discovery and Anomaly Detection Using Atypicality for Real-Valued Data

Sabeti

Høst-Madsen

2019

Entropy

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The aim of using atypicality is to extract small, rare, unusual and interesting pieces out of big data. This complements statistics about typical data to give insight into data. In order to find such “interesting” parts of data, universal approaches are required, since it is not known in advance what we are looking for. We therefore base the atypicality criterion on codelength. In a prior paper we developed the methodology for discrete-valued data, and the current paper extends this to real-valued data. This is done by using minimum description length (MDL). We develop the information-theoretic methodology for a number of “universal” signal processing models, and finally apply them to recorded hydrophone data and heart rate variability (HRV) signal.

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Performance Bounds for Finite Moving Average Tests in Transient Change Detection

Cited by 28 publications

References 37 publications

Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX- and NMEA-Level Measurements

Data Discovery and Anomaly Detection Using Atypicality for Real-Valued Data

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