Integrating GLONASS with GPS for Drone Orientation Tracking B Mahanth Gowda1( ), Justin Manweiler2, Ashutosh Dhekne1, Romit Roy Choudhury1, and Justin D. Weisz2 1 University of Illinois at Urbana-Champaign, Urbana, IL, USA [email protected] 2 IBM Research, Yorktown Heights, NY, USA Abstract. In addition to position sensing, GPS receivers can be lever- aged for orientation sensing too. We place multiple GPS receivers on drones and translate their relative positions into orientation. Such an orthogonal mode of orientation sensing provides failsafe under Inertial sensorfailures–aprimarycauseofdronecrashestoday.Thispaper integrates GLONASS satellite measurements with GPS for enhancing the orientation accuracy. Accurate estimate of orientation depends upon high precision rela- tive positioning of the GPS receivers. While GPS carrier phases pro- vide high precision ranging data, the phases are noisy and wrap after every wavelength which introduces ambiguity. Moreover, GPS signals experience poor SNR and loss of satellite locks under aggressive flights. This can severely limit both the accuracy and the amount of carrier phase data available. Fortunately, integrating the ubiquitously avail- able Russian GLONASS satellites with GPS can double the amount of observations and substantially improve the robustness of orientation esti- mates. However, the fusion is non-trivial because of the operational dif- ference between FDMA based GLONASS and CDMA based GPS. This paper proposes a temporal differencing scheme for fusion of GLONASS and GPS measurements, through a system called SafetyNet. Results from 11 sessions of 5–7 min flights report median orientation accuracies of 2◦ even under overcast weather conditions. 1 Introduction Multiple GPS receivers can be used for tracking drone orientation. A rigid body is formed by placing 3 or more GPS receivers on the body of a drone. The orientation of the drone is a simple function of relative positions of the GPS receivers on the rigid body. While inertial sensors (IMU) like accelerometers, gyroscopes and compasses are conventionally used for orientation tracking, they can fail – leading to crashes [1,2,4,8,33]. Many of the failures are correlated from common sources of engine vibration and on-board electromagnetic interference [18,21,22,26,31]. Hence, it is not possible to cope with such failures by having redundant sensors. Since GPS provides a completely orthogonal modality of orientation sensing, it can serve as a failsafe for IMU failures. c Springer International Publishing AG 2017 N. Sastry and S. Chakraborty (Eds.): COMSNETS 2017, LNCS 10340, pp. 77–92, 2017. DOI: 10.1007/978-3-319-67235-9 7 78 M. Gowda et al. Achieving an orientation accuracy of 2◦ with GPS requires the relative posi- tions between the GPS receivers to be known to an accuracy of 2 cm. Moreover, the accuracy needs to be upheld constantly over time, especially during periods of aggressive maneuvers, where the SNR is poor, locks with satellites break and other errors manifest. This is a challenging task since a basic GPS location is only 3 m accurate. Even the best differential GPS technique only provides a relative positioning accuracy of 15 cm [14,15]. They leverage carrier phase data, which is a pre- cise estimate of range from satellites, but polluted by ambiguity due to phase wrapping. Our recent Mobicom paper [10] shows how carrier phase data can be differenced over time and space and fused using an augmented particle filter framework. This results in accurate orientation estimate with a median accuracy of 2◦ and 95th percentile accuracy of 8◦. In this paper, we expand the GPS/GLONASS integration component of Safe- tyNet, which is a critical factor in achieving a high accuracy. Under aggressive flights, the carrier phase data is not only noisy, but also sparse in number because of poor SNR and loss of satellite locks. Fortunately, integrating the ubiquitously available GLONASS (GLObal NAvigation Satellite System – [19]) satellite mea- surements with GPS doubles the number of measurements and enhances the robustness of orientation estimates. However, the fusion is non trivial since GLONASS operates on FDMA whereas GPS uses CDMA. In the rest of the paper, we first provide a background of GPS, GLONASS and the basics of satellite positioning systems. We then revisit the math behind orientation sensing from [10]. We derive orientation as a function of various dif- ferences of carrier phases over, receivers, satellites and time. Finally, we integrate GLONASS measurements into our model. Evaluation results show substantial gains from the integration, especially in eliminating errors in the tail. 2 GPS Foundations We present GPS foundations from first principles and end with discussions on modern techniques. As a result, this section is long. However, given that this paper builds over core GPS algorithms, the material is necessary. We also believe the material is easy to follow. 2.1 Global Navigation Satellite System (GNSS) GNSS is the generic name given to satellite systems that provide localization services to receiver’s on earth. The Global Positioning System (GPS) [24]is one example of a GNSS, developed by the US Government during 1970–1980s. GPS consists of a constellation of 31 satellites orbiting the earth at a height of 20,000 km. The satellites are continuously transmitting unique pseudo-random noise (PRN) sequences using CDMA at 2 different frequencies –1575.42 (L1) and 1227.60 MHz (L2). They also broadcast ephemeris data using which the (satellite) position and time of transmission can be calculated. A GPS receiver Integrating GLONASS with GPS for Drone Orientation Tracking 79 on the ground localizes itself by trilateration, i.e., measuring and combining the time-of-flight (ToF) from different satellites. Velocity is computed from the doppler shifts from various satellites. GLObal NAvigation Satellite System (GLONASS) is another GNSS system launched by Russia in the 1980’s [19]. GLONASS has 24 operational satellites. Unlike GPS which uses CDMA for multiplexing, GLONASS uses a 15 channel FDMA with a center frequency of 1602 MHz and an inter-channel separation of 0.5625 MHz. Since, there are only 15 channels, but 24 operational satellites, those satellites that are antipodal (on opposite side of earth), reuse the same frequency, such that they are not simultaneously visible to a GPS receiver. Sim- ilarly, GALILEO [3] is an European GNSS system currently under development. Many GNSS receivers are capable of decoding signals from multiple GNSS sys- tems, providing increased accuracy. This paper will also use such receivers and exploit the advantages of satellite diversity. 2.2 GPS Localization and Error Sources A GNSS receiver on the ground can compute its 3D location, time, and velocity. The key idea is to measure various attributes of the arriving signal (e.g., time of flight, phase, etc.), and then apply statistical algorithms to estimate the errors and ambiguities in measurements. We give an overview of the techniques that underpin GPS; other GNSS systems are similar. Pseudorange When a satellite transmits a signal, it includes the starting time of the trans- mission (obtained from its atomic clock). The ground receiver records the time of reception also using its less accurate local clock. The time-of-flight (ToF) is the difference between these timestamps. When multiplied by the speed of light, the result gives the rough distance to the satellite, called pseudorange. Pseudorange = ToF * (speed of light) Of course, the measured ToF is inaccurate because the clock of a typical GPS receiver is not synchronized to the GPS satellites. The resulting error can be up to 300 km. In addition, when the GPS signal enters the Earth’s atmosphere, it can get delayed due to refractions in the Ionosphere and Troposphere. A signal also passes through a multipath channel, adding more errors. Assuming the true s s range between a satellite s and receiver i is ρi , the measured Pseudorange Pi , inclusive of all error sources, can be modeled as s s s s Pi = ρi + cti − ct + A + Mi + i (1) s Here, ti and t are receiver and satellite clock biases, respectively, with respect to true time. A represents the range error due to refractions in the atmosphere. s s Mi denotes Multipath, i is receiver’s hardware noise and c is the speed of light. s 1 In today’s systems, the satellite clock error t is small ,andcti proves to be the 1 Satellites estimate the errors themselves from mutually exchanged signals as well as from ground sources. 80 M. Gowda et al. s major source of error. Hence, cti is modeled as an unknown, and ρi is written as a function of the unknown 3D receiver location ρi and the known 3D satellite position ρs. This results in a total of 4 unknowns. Once we have a lock with 4 satellites, the time bias and the 3D locations can be jointly estimated resulting in a position fix. The ignored error sources, i.e., ts,A,M,and, could contribute to an error of 1–4 m, depending upon the environmental conditions. GPS receivers on phones and car dashboards use the above techniques. How- ever, higher accuracy applications such as 3D orientation tracking require better performance. To this end, modern GPS research has leveraged the phase of the arriving signals, as detailed next. Carrier Phase s Once a satellite lock is acquired, the phase of the arriving signal, φi , is constantly tracked by a phase lock loop (PLL). The true range between the satellite and the s receiver, ρi , can be expressed in terms of wavelength λ. s s s ρi = λNi + λφi (2) s s Ni is an unknown integer, meaning that the PLL measurement of φi only captures the fractional part of the range. However, due to atmospheric effects, multipath, and clock issues, the above equation can be updated: s s s s λφi = ρi + cti − ct + A + Mi + i − λNi (3) s Estimating Ni is non-trivial and several algorithms exist [20,32].