# US 2023/0057518 A1 · Region-Adapted Neural Network for Location Determination > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0057518 A1 - Document ID: US20230057518A1 - Status: Published application - Kind: A1 - Application number: 17/851,924 - Filed: 2022-06-28 - Published: 2023-02-23 - Earliest claimed: 2021-08-20 - Assignees: Mediatek Inc. - Applicants: MediaTek Inc. - Inventors: Po-Yu Chen; Hao Chen; Tsung-Yu Chiou - Web page: https://patentgrep.com/patent/US20230057518A1 ## CPC - G01S 19/25: involving aiding data received from a cooperating element, e.g. assisted GPS - G01S 19/396: {Determining accuracy or reliability of position or pseudorange measurements} - H04B 7/18556: {using a location database} - H04W 4/029: Location-based management or tracking services - G01S 19/22: Multipath-related issues - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - H04B: TRANSMISSION - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract A wireless device includes a satellite receiver to receive data from multiple satellites. The wireless device also includes processing circuitry and memory. The memory stores one or more neural network models. The processing circuitry is operative to identify a neural network model that has been trained to adapt to a region in which the wireless device operates, classify satellite raw measurements from each satellite at a given time into a corresponding quality level using the neural network model, and identify satellites raw measurements with a quality level higher than a threshold. The location of the wireless device is calculated using the identified satellite raw measurements. ## Claims ### Claim 1 A method for determining a location of a wireless device that receives data from a plurality of satellites, comprising: identifying a neural network model that has been trained to adapt to a region in which the wireless device operates; classifying satellite raw measurements from each satellite at a given time into a corresponding quality level using the neural network model; identifying satellites raw measurements with a quality level higher than a threshold; and calculating the location of the wireless device using the identified satellite raw measurements. ### Claim 2 (depends on 1) The method of claim 1, wherein the neural network model has been trained to classify the satellite raw measurements according to a pseudo-range error computed during a training phase, the pseudo-range error including at least a multipath propagation error. ### Claim 3 (depends on 1) The method of claim 1, wherein the neural network model is a multi-layer perceptron network. ### Claim 4 (depends on 1) The method of claim 1, wherein the satellite raw measurements include some or all of satellite raw measurement items: a satellite system identifier, signal-to-noise ratio (SNR), Doppler information, Elevation, Azimuth, and clock information. ### Claim 5 (depends on 4) The method of claim 4, wherein the neural network model includes a plurality of input nodes at an input layer, each input node receiving a respective one of the satellite raw measurement items. ### Claim 6 (depends on 1) The method of claim 1, further comprising: disregarding the satellite raw measurements having the quality level lower than the threshold. ### Claim 7 (depends on 1) The method of claim 1, further comprising: estimating the location of the wireless device to determine whether the neural network model stored in a device memory corresponds to the estimated location; and downloading, from a server, a new neural network model that corresponds to the estimated location when the neural network model stored in the device memory does not correspond to the estimated location. ### Claim 8 (depends on 1) The method of claim 1, further comprising: incorporating building information to estimate multipath effects on signals from a given satellite at a given time; and classifying the satellite raw measurements based, at least in part, on the building information. ### Claim 9 (depends on 1) The method of claim 1, further comprising: incorporating building information to estimate multipath effects on signals from a given satellite at a given time; and calculating the location of the wireless device using the identified satellite raw measurements and the building information. ### Claim 10 (depends on 1) The method of claim 1, further comprising: incorporating information from one or more additional wireless systems in the calculation of the location of the wireless device, the one or more additional wireless systems include one or more of: a cellular system, a Wi-Fi system, and a Bluetooth system. ### Claim 11 A wireless device, comprising: a satellite receiver operative to receive data from a plurality of satellites; processing circuitry; and memory to store one or more neural network models, wherein the processing circuitry is operative to: identify a neural network model that has been trained to adapt to a region in which the wireless device operates; classify satellite raw measurements from each satellite at a given time into a corresponding quality level using the neural network model; identify satellites raw measurements with a quality level higher than a threshold; and calculate the location of the wireless device using the identified satellite raw measurements. ### Claim 12 (depends on 11) The wireless device of claim 11, wherein the neural network model has been trained to classify the satellite raw measurements according to a pseudo-range error computed during a training phase, the pseudo-range error including at least a multipath propagation error. ### Claim 13 (depends on 11) The wireless device of claim 11, wherein the neural network model is a multi-layer perceptron network. ### Claim 14 (depends on 11) The wireless device of claim 11, wherein the satellite raw measurements include some or all of satellite raw measurement items: a satellite system identifier, signal-to-noise ratio (SNR), Doppler information, Elevation, Azimuth, and clock information. ### Claim 15 (depends on 14) The wireless device of claim 14, wherein the neural network model includes a plurality of input nodes at an input layer, each input node receiving a respective one of the satellite raw measurement items. ### Claim 16 (depends on 11) The wireless device of claim 11, wherein when identifying the satellite raw measurements, the processing circuitry is further operative to: disregard the satellite raw measurements having the quality level lower than the threshold. ### Claim 17 (depends on 11) The wireless device of claim 11, wherein the processing circuitry is further operative to: estimate the location of the wireless device to determine whether the neural network model stored in the memory corresponds to the estimated location; and download, from a server, a new neural network model that corresponds to the estimated location when the neural network model stored in the memory does not correspond to the estimated location. ### Claim 18 (depends on 11) The wireless device of claim 11, wherein when classifying the satellite raw measurements, the processing circuitry is further operative to: incorporate building information to estimate multipath effects on signals from a given satellite at a given time; and classify the satellite raw measurements based, at least in part, on the building information. ### Claim 19 (depends on 11) The wireless device of claim 11, wherein when calculating the location of the wireless device, the processing circuitry is further operative to: incorporate building information to estimate multipath effects on signals from a given satellite at a given time; and calculate the location of the wireless device using the identified satellite raw measurements and the building information. ### Claim 20 (depends on 11) The wireless device of claim 11, wherein when calculating the location of the wireless device, the processing circuitry is further operative to: incorporate information from one or more additional wireless systems in the calculation of the location of the wireless device, the one or more additional wireless systems include one or more of: a cellular system, a Wi-Fi system, and a Bluetooth system.