Mission planning for weapons systems

Patent metadata

Field	Value
Publication number	US-11029130-B2
Application number	US-201816614857-A
Country	US
Kind code	B2
Filing date	May 17, 2018
Priority date	May 25, 2017
Publication date	Jun 8, 2021
Grant date	Jun 8, 2021

Abstract

A mission planning method for use with a weapon is disclosed. The method comprises: obtaining a first training data set describing the performance of the weapon; using the first training data set and a Gaussian Process (GP) or Neural Network to obtain a first surrogate model giving a functional approximation of the performance of the weapon; and providing the first surrogate model to a weapons system for use in calculating a performance characteristic of the weapon during combat operations.

First claim

The invention claimed is:

1. A mission planning method for use with a weapon, the method comprising the steps of:
obtaining a first training data set describing the performance of the weapon;
using the first training data set and a Gaussian Process (GP) to obtain a first surrogate model giving a functional approximation of the performance of the weapon, the Gaussian process comprising using a covariance function to calculate a set of hyper-parameters and a set of weighted values; and
providing the first surrogate model to a weapons system for use in calculating a performance characteristic of the weapon during combat operations.

2. A mission planning method according to claim 1 , wherein the surrogate model further comprises a set of inducing points.

3. A mission planning method according to claim 1 , wherein the Gaussian Process algorithm used is the Fully Independent Training Conditional (FITC) algorithm.

4. A mission planning method according to claim 1 , further comprising calculating a performance characteristic of the weapon during combat operations using the surrogate model.

5. A mission planning method according to claim 4 , further comprising initiating launch of the weapon in dependence on the performance characteristic so calculated.

6. A mission planning method according to claim 1 , the method comprising the steps of:
obtaining a second training data set describing the performance of the weapon in a second, different, parameter space to the first training data set;
using the second training data set and a Gaussian Process (GP) or Neural Network to obtain a second, different, surrogate model giving a functional approximation of the performance of the weapon in the second parameter space;
providing the first and second surrogate models to a weapons system for use in calculating a performance characteristic of the weapon during combat operations.

7. A mission planning method according to claim 6 , further comprising, during combat operations, selecting the first or second surrogate model in dependence on the current parameters and using the surrogate model so selected to calculate a performance characteristic of the weapon.

8. A mission planning method according to claim 1 , wherein the weapon is a missile.

9. A mission planning method according to claim 1 , wherein the weapon system comprises a weapons platform and the weapons platform is an aircraft, ship or land vehicle.

10. A mission planning method according to claim 1 , wherein the performance characteristic is the Launch Success Zone (LSZ), the Launch Acceptable Region (LAR), the footprint, the aerodynamic drag of the weapon and/or the trajectory of an enemy weapon.

11. A weapons system comprising a processor programmed with software configured to calculate a performance characteristic of a weapon of the weapons system during combat operations using a functional approximation of the performance of the weapon, said functional approximation comprising a surrogate model produced using a Gaussian Process, the Gaussian process comprising using a covariance function to calculate a set of hyper-parameters and a set of weighted values.

12. A weapons system according to claim 11 , further comprising a launcher, and wherein the launcher comprises the processor.

13. A weapon configured for use as the weapon of the weapons system of claim 11 , wherein the weapon comprises the processor.

14. A weapons system according to claim 11 , further comprising a weapons platform, wherein the processor is part of the command and control system of the weapons platform.

15. A computer software product for loading onto a processor associated with a weapons system, wherein the software product is configured to calculate a performance characteristic of a weapon of the weapons system during combat operations using a functional approximation of the performance of the weapon, said functional approximation comprising a surrogate model produced using a Gaussian Process, the Gaussian process comprising using a covariance function to calculate a set of hyper-parameters and a set of weighted values.

16. A computer software product according to claim 15 , wherein the surrogate model is produced using a Gaussian Process and the surrogate model comprises a covariance function, a set of hyper-parameters and a set of weighted values.

Assignees

• Mbda Uk Ltd

Inventors

• Rees Gareth Stanley
• Walls Andrew Philip
• Robinson Alex Martin
• Sargent Stephen Vincent
• Potter Nathan Rees
• Moody Leigh

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/08

  Learning methods · CPC title

• F41G7/006

  Guided missiles training or simulation devices · CPC title

• F41G7/002

  target simulators · CPC title

• F41G9/002

  for guiding a craft to a correct firing position (for bombing control F41G9/02) · CPC title