Context-Aware Adaptive Multipath Compensation Based on Channel Pattern Recognition for GNSS Receivers

Sokhandan, Negin; Ziedan, Nesreen I.; Broumandan, Ali

doi:10.1017/s0373463317000121

Cited by 14 publications

(9 citation statements)

References 40 publications

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“…Ziedan (2012) proposed a Principal Components Analysis (PCA) and probabilistic neural network-based method to deal with multipath effects in adaptive tracking. Sokhandan et al (2017), meanwhile, proposed a Support Vector Machine (SVM)-based adaptive multipath compensation and tracking strategy to extract context information about the type of multipath environment and the state of motion of a GNSS receiver. Vector tracking, however, requires altering the traditional architecture of the GNSS receiver, which is not currently possible for low-cost GNSS receivers.…”

Section: Introductionmentioning

confidence: 99%

GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System

et al. 2018

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The multipath effect and non-line-of-sight (NLOS) reception of global positioning system (GPS) signals both serve to degrade performance, particularly in urban areas. Although, receiver design continues to evolve, residual multipath errors and NLOS signals remain a challenge in built-up areas. It is therefore desirable to identify the direct, multipath-affected and NLOS GPS measurements in order improve ranging based position solutions. The traditional signal strength-based methods to achieve this, however, use a single variable (e.g. C/N0) as the classifier. Because the single variable does not completely represent the multipath and NLOS characteristics of the signals, the traditional methods are not robust in the classification of signal reception. This paper uses a set of variables derived from the raw GPS measurements together with an algorithm based on an adaptive neuro fuzzy inference system (ANFIS) to classify direct, multipath-affected and NLOS measurements from GPS. Results from real data show that the proposed method could achieve rates of correct classification of 100%, 91% and 84%, respectively, for the LOS, Multipath and NLOS based on a static test with special conditions results that are superior to the other three state-of-the-art signal reception classification methods.

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Section: Introductionmentioning

confidence: 99%

GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System

et al. 2018

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“…Hence, this paper proposes to improve signal type classification rate by using correlator-level classifier, and compares it with conventional NMEA/RINEX-level classifiers. To make a fair comparison between different level classifiers, it is important to make sure that all features come from the same receiver, which has been rarely explored in existing researches, although some researches have explored the deeper-level features in different applications [18,26,29]. The two findings in this paper and the interpretation in perspective of previous studies are described as follows.…”

Section: Discussionmentioning

confidence: 86%

“…The basic principle behind this is that the NLOS reception distorts the autocorrelation function (ACF) of the direct LOS signal. 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.…”

Section: Introductionmentioning

confidence: 99%

“…The effectiveness of these additional aiding-based techniques has been verified. However, the issue of large physical size and high cost needs to be addressed before being applied to low-cost receivers, which occupy a large portion of mass-market GNSS capable devices.Machine learning algorithms with nonlinear kernel functions can classify nonlinearly separable data instances by projecting the data features into a higher-dimensional space and maximizing the geometric distance between the decision boundary and the nearest data on each side of the decision boundary [18]. It has been used extensively in classification applications, e.g., the GNSS receiver context and channel classification [18,19] and GNSS signal type classification [20].…”

mentioning

confidence: 99%

“…Machine learning algorithms with nonlinear kernel functions can classify nonlinearly separable data instances by projecting the data features into a higher-dimensional space and maximizing the geometric distance between the decision boundary and the nearest data on each side of the decision boundary [18]. It has been used extensively in classification applications, e.g., the GNSS receiver context and channel classification [18,19] and GNSS signal type classification [20]. This method requires no additional aiding, making it more feasible in practice.…”

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confidence: 99%

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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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