# US 2018/0024248 A1 · Systems and methods for nequick modeling using neural networks

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- Number: US 2018/0024248 A1
- Document ID: US20180024248A1
- Status: Published application
- Kind: A1
- Application number: 15/215,288
- Filed: 2016-07-20
- Published: 2018-01-25
- Earliest claimed: 2016-07-20
- Assignees: Honeywell International Inc.
- Applicants: Honeywell International Inc.
- Inventors: Martin Orejas; Zbynek Fedra; Jussi Raasakka
- Web page: https://patentgrep.com/patent/US20180024248A1

## CPC

- G01S 19/072: {Ionosphere corrections}
- G01S 19/40: Correcting position, velocity or attitude
- G06N 3/0499: Feedforward networks
- G06N 3/09: Supervised learning
- 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
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Systems and methods of determining ionosphere delay for a GNSS system are provided. In one embodiment, a GNSS system includes an antenna configured to receive GNSS signals from one or more GNSS satellites. The system further includes a signal processing circuit coupled to the antenna and configured to down convert the GNSS signals from RF to IF. The system further includes a processing device coupled to a memory, the memory including a database of a plurality of weights and an activation function for a neural network, the neural network trained to output an approximation of an output of a NeQuick model. The processing device configured to: apply the plurality of weights and the activation function for the neural network to a plurality of inputs generated from the GNSS signals; and estimate an indication of ionosphere delay based on an output of the neural network.

## Claims

### Claim 1

A Global Navigation Satellite System (GNSS) system comprising:
an antenna configured to receive Global Navigation Satellite System (GNSS) signals from one or more GNSS satellites;
a signal processing circuit coupled to the antenna, wherein the signal processing circuit is configured to down convert the GNSS signals from a radio frequency (RF) to an intermediate frequency (IF); and
a processing device coupled to a memory, wherein the memory includes a database of a plurality of weights and an activation function for a neural network, wherein the neural network is trained to output an approximation of an output of a NeQuick model, wherein the processing device is configured to:
apply the plurality of weights and the activation function for the neural network to a plurality of inputs generated from the GNSS signals;
estimate an indication of ionosphere delay based on an output of the neural network.

### Claim 2 (depends on 1)

The GNSS system of claim 1, wherein the plurality of inputs comprises nine inputs, wherein each input of the plurality of inputs corresponds to an input of the NeQuick model.

### Claim 3 (depends on 2)

The GNSS system of claim 2, wherein the neural network is directed to a specific subset of one or more values for at least one input of the NeQuick model.

### Claim 4 (depends on 2)

The GNSS system of claim 2, wherein the neural network is selected from a plurality of neural networks, wherein each neural network of the plurality of neural networks is directed to a specific subset of one or more values for at least one input of the NeQuick model.

### Claim 5 (depends on 1)

The GNSS system of claim 1, wherein the plurality of inputs comprises eight inputs, wherein each of the eight inputs corresponds to an input of the NeQuick model, wherein the neural network is directed to a specific value for an input of the NeQuick model that does not correspond to the eight inputs.

### Claim 6 (depends on 1)

The GNSS system of claim 1, wherein the processing device is further configured to generate the plurality of inputs from the GNSS signals.

### Claim 7 (depends on 1)

The GNSS system of claim 1, wherein the GNSS system is incorporated into a multi-mode radio onboard a vehicle.

### Claim 8 (depends on 1)

The GNSS system of claim 1, wherein the GNSS system is configured to process Galileo specific GNSS signals.

### Claim 9 (depends on 1)

The GNSS system of claim 1, wherein the processing device is further configured to determine at least an estimated position of the GNSS system using the indication of ionosphere delay and the received GNSS signals.

### Claim 10 (depends on 1)

The GNSS system of claim 1, wherein a structure of the neural network is stored in the memory.

### Claim 11 (depends on 1)

The GNSS system of claim 1, wherein the processing device is further configured to provide an estimation of expected error between the output of the neural network and the output of the NeQuick model.

### Claim 12

A method of determining the ionosphere delay for a Global Navigation Satellite System (GNSS) receiver, comprising:
receiving GNSS signals from one or more satellites;
converting the GNSS signals from a radio frequency (RF) to an intermediate frequency (IF);
generating a plurality of inputs of a neural network from the GNSS signals, wherein the neural network is trained to output an approximation of an output of a NeQuick model; and
estimating an indication of ionosphere delay based on an output of the neural network.

### Claim 13 (depends on 12)

The method of claim 12, wherein estimating an indication of ionosphere delay based on an output of the neural network comprises:
providing a respective weighted input from each input of the plurality of inputs to each node of a hidden layer of the neural network;
determining a respective sum, for each respective node of the hidden layer, by summing the weighted inputs provided to the respective node of the hidden layer and applying the activation function;
applying a weight to each respective sum to produce a respective weighted sum for each respective sum;
providing each respective weighted sum to a node of an output layer of the neural network; and
determining the output of the neural network by summing the respective weighted sums provided to the node of the output layer.

### Claim 14 (depends on 12)

The method of claim 12, further comprising determining a position of the GNSS receiver using the received GNSS signals and the indication of ionosphere delay.

### Claim 15 (depends on 12)

The method of claim 12, wherein the plurality of inputs of the neural network comprises nine inputs, wherein each input of the plurality of inputs corresponds to an input of the NeQuick model.

### Claim 16 (depends on 15)

The method of claim 15, wherein the neural network is directed to a specific subset of one or more values for at least one input of the NeQuick model.

### Claim 17 (depends on 12)

The method of claim 12, wherein the plurality of inputs of the neural network comprises eight inputs, wherein each of the eight inputs corresponds to an input of the NeQuick model, wherein the neural network is directed to a specific value for an input of the NeQuick model that does not correspond to the eight inputs.

### Claim 18

A non-transitory computer readable medium having computer-executable instructions stored thereon which, when executed by one or more processing devices, cause the one or more processing devices to:
generate a plurality of inputs of a neural network based on received GNSS signals, wherein the neural network is trained to output an approximation of an output of a NeQuick model, wherein the neural network is stored on a memory;
apply a plurality of weights and an activation function of the neural network to the plurality of inputs; and
estimate an indication of ionosphere delay using the neural network.

### Claim 19 (depends on 18)

The non-transitory computer readable medium of claim 18, wherein the plurality of inputs comprises nine inputs, wherein each input of the plurality of inputs corresponds to an input of the NeQuick model.

### Claim 20 (depends on 18)

The non-transitory computer readable medium of claim 18, wherein the plurality of inputs comprises eight inputs, wherein each of the eight inputs corresponds to an input of the NeQuick model, wherein the neural network is directed to a specific value for an input of the NeQuick model that does not correspond to the eight inputs.