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 ...

Velocity updates have been proven to be important for constraining motion-sensor-based dead-reckoning (DR) solutions in indoor unmanned aerial vehicle (UAV) applications. The forward velocity from a mass flow sensor and the lateral and vertical non-holonomic constraints (NHC) can be utilized for three-dimensional (3D) velocity updates. However, it is observed that (a) the quadrotor UAV may have a vertical velocity trend when it is controlled to move horizontally; (b) the quadrotor may have a pitch angle when moving horizontally; and (c) the mass flow sensor may suffer from sensor errors, especially the scale factor error. Such phenomenons degrade the performance of velocity updates. Thus, this paper presents a multi-sensor integrated localization system that has more effective sensor interactions. Specifically, (a) the barometer data are utilized to detect height changes and thus determine the weight of vertical velocity update; (b) the pitch angle from the inertial measurement unit (IMU) and magnetometer data fusion is used to set the weight of forward velocity update; and (c) an extra mass flow sensor calibration module is introduced. Indoor flight tests have indicated the effectiveness of the proposed sensor interaction strategies in enhancing indoor quadrotor DR solutions, which can also be used for detecting outliers in external localization technologies such as ultrasonics.

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