A tracking loop and associated method for tracking a satellite signal in a GNSS receiver and for determining a line-of-sight (LOS) signal from a plurality of satellite signals received by the GNSS receiver from a satellite. One or more first correlators perform a correlation between a code signal derived from one of the received satellite signals and a plurality of corresponding replica code signals to determine a plurality of code correlation sums comprising a prompt code correlation sum, one or more early code correlation sums and one or more late code correlation sums. One or more second correlators correlate the plurality of code correlation sums with a plurality of replica carrier signals, each having a different Doppler frequency offset, to determine, for each of the plurality of code correlation sums, a set of correlation magnitudes at frequencies of the plurality of replica carrier signals. An LOS identification module identifies the LOS signal based on a signal propagation delay corresponding to one or more local maxima within the sets of correlation magnitudes.

Methods, devices, systems, media, and receivers for processing GNSS signals are described. One aspect of the present disclosure provides a method for processing satellite signals of a Global Navigation Satellite System (GNSS), the method comprising: receiving a first GNSS signal transmitted in a first GNSS operational band by a satellite of the GNSS and a second GNSS signal transmitted in a second GNSS operational band by the satellite; tracking the first GNSS signal; generating, from the tracking of the first GNSS signal, tracking parameters for the first GNSS signal; and decoding, at least based on the tracking parameters for the first GNSS signal, the second GNSS signal, wherein the first GNSS operational band is one of L1 band, L2 band or L5 band, and the second GNSS operational band is L6 band.

A device that includes a receiver that receives multiple positioning signals from a satellite, including a positioning signal and remaining positioning signals, and a processor communicatively coupled to the receiver. The processor determines a speed value of the device based on a Doppler shift of the positioning signal. The speed value is a magnitude of a velocity of the device in a direction. The processor also determines that the speed value is not consistent with at least one other measurement and determines the position of the device using the remaining positioning signals.

An assisted satellite positioning system based on detecting signals from a number of satellites includes: (a) a mobile receiver; and (b) a base station communicating with the receiver over a low-power wireless communication network, the base station providing ephemeris data of a selected number of the satellites, but not all, using a compressed data format. The ephemeris data may include data concerning doppler frequency variations or elevation variations of the selected satellites over a predetermined time interval. The doppler frequency variations and the elevation variations may be represented in the compressed format by coefficients of a polynomial function of time. The polynomial function may be weighted to have lesser relative errors in larger doppler frequencies than lesser doppler frequencies, or to have lesser relative errors in lesser elevations than larger elevations. In one implementation, the low-power wireless communication network—such as a LoRa network—that has a range of at least 10 miles.

An electromagnetic transmission carrying a bauded signal, such as a transmission from an orbiting satellite, is processed for Doppler shift analysis. The electromagnetic transmission is captured and a non-linear operation is performed to expose a cyclostationary feature of the captured transmission that defines a rate-line having a rate-line frequency that is related to the bauded signal and to the motion of the transmitter relative to the receiver. The rate-line frequency is tracked in time to generate data indicative of Doppler shift associated with the satellite. The data are then supplied to a tracking receiver.

An apparatus and a method for providing a global localization output are provided. When the apparatus receives navigation signals, the apparatus processes the signals to determine, based on a fixed earth-centered, earth-fixed (ECEF) reference pose of a reference point in an ECEF coordinate, a new ECEF pose, and to convert the fixed ECEF reference pose to an east-north-up (ENU) reference pose in an ENU coordinate. When the apparatus determines that a jump occurs in the new ECEF pose based on a pose change between the new ECEF pose and a previous ECEF pose, the apparatus calculates a reference shift of the ENU reference pose based on the pose change to absorb the jump in the ENU coordinate, and updates the ENU reference pose based on the reference shift. Thus, a new ENU local pose may be obtained using the ENU reference pose.

A position detection method to be executed by a computer, the position detection method includes transmitting, by a sensor terminal, a signal obtained by performing capture processing on a satellite signal from a satellite of a search target according to an order of the satellites of the search targets; calculating, by a calculation device, a position of the sensor terminal based on a signal transmitted by the sensor terminal; and determining a satellite having a highest discovery probability based on a specific estimation method for second and subsequent search targets, using an index which is reflected larger as the discovery probability of other satellites is higher or lower, in a case where the first satellite is captured when a first search target is determined.

Method, systems, and computer-readable media containing instructions which, when executed by a computing device, cause it to receive data from an inertial measurement unit, including GPS data, velocity data, and bearing data, receive data from a digital magnetic compass, including bearing data, receive data from a Doppler sensor, including velocity data and distance data, determining whether GPS location data is in consensus with a previous derived multi-source reckoning system location, determining a consensus distance value from a weighted average of data from the inertial measurement unit and the Doppler sensor, determine a consensus heading value from a weighted average of data from the inertial measurement unit and the digital magnetic compass, determine a consensus geolocation value from a weighted average of data from the inertial measurement unit and the previous derived multi-source reckoning system location, and determine a derived multi-source reckoning system location.

An error model calibration method can analyze discrete distribution situations of a pseudo-range measurement error and a Doppler measurement error under different carrier-to-noise ratios and altitude helping to calibrate and improve the universality of error model calibration. Observation data is received and satellite data is acquired based on the observation data. A pseudo-range error array and a Doppler error array are calibrated based on the observation data, geometric parameters, and the satellite data. The pseudo range error array describes errors of two terminals under a carrier-to-noise ratio and altitude angle of a satellite. The Doppler error array describes a discrete distribution of the two terminals. The pseudo-range error models respectively corresponding to the at least two terminals are fit using the pseudo-range error array. The Doppler error models respectively corresponding to the at least two terminals are fit using the Doppler error array.

A system and method for detection of an aerial drone in an environment includes a baseline of geo-mapped sensor data in a temporal and location indexed database formed by (i) using at least one sensor to receive signals from the environment and converting into digital signals for further processing; (ii) deriving time delays, object signatures, Doppler shifts, reflectivity, and/or optical characteristics from the received signals; (iii) geo-mapping the environment using GNSS and the sensor data; and (iv) logging sensor data over a time interval, for example 24 hours to 7 days. Live sensor data can be then be monitored and signature data can be derived by computing at least one parameter such as direction and signal strength. The live data is continuously or periodically compared to the baseline data to identify a variance, if any, which may be indicative of a detection event.