Systems and methods for efficient targeting

Patent metadata

Field	Value
Publication number	US-11726197-B2
Application number	US-201916653201-A
Country	US
Kind code	B2
Filing date	Oct 15, 2019
Priority date	Jan 2, 2015
Publication date	Aug 15, 2023
Grant date	Aug 15, 2023

Abstract

A system for determining the physical path of an object can map several candidate paths to a suitable path space that can be explored using a convex optimization technique. The optimization technique may take advantage of the typical sparsity of the path space and can identify a likely physical path using a function of sensor observation as constraints. A track of an object can also be determined using a track model and a convex optimization technique.

First claim

What is claimed is:

1. A method for analyzing motions of objects, the method comprising performing by a processor the steps of:
generating a set of constraints comprising: (i) a set of observations at a receiving antenna, and (ii) a plurality of sets of phase shifts at the receiving antenna, each set of phase shifts comprising phase shifts observable at the receiving antenna corresponding to a point scatterer associated with a respective path point in a set of path points, each path point representing in a path space a respective expected path of motion of a point scatterer in a physical space;
determining intensity of each path point in the set of path points: (i) by exploring a set of fields, each field corresponding to a respective path point in the set of path points, (ii) while satisfying the set of constraints; and
identifying a first path, in the physical space, of a first point scatterer, based on the respective intensities of the path points.

2. The method of claim 1 , wherein:
a number K of the path points in the set of path points is greater than a number N of the observations at the receiving antenna in the set of observations.

3. The method of claim 2 , wherein exploring the set of fields comprises selecting the set of path points such that a number of near-zero fields in the set of fields is maximized.

4. The method of claim 3 , wherein exploring the set of fields comprises second order cone programming (SOCP).

5. The method of claim 1 , wherein:
the path space comprises a parametric space; and
at least one parameter of the parametric space is selected from the group consisting of: a position, a linear velocity, and an angular velocity.

6. The method of claim 1 , wherein the representation of a path point in the path space comprises one of:
a three dimensional position vector;
a six dimensional vector comprising a three dimensional position vector and a three dimensional velocity vector; and
a vector comprising a six dimensional vector representing rigid body motion and a position vector.

7. The method of claim 1 , wherein:
the receiving antenna comprises N E elements, each element being associated with up to N F frequencies and up to N T pulses; and
each phase shift observable at the receiving antenna is associated with an antenna element, one of the N F frequencies, and one pulse.

8. The method of claim 7 , wherein a number N of the observations at the receiving antenna in the set of observations is less than N E *N E *N T .

9. The method of claim 7 , wherein each expected path of motion of the point scatterer in the physical space corresponds to a single dwell of the antenna, the single dwell corresponding to N E elements, N F frequencies, and N T pulses.

10. The method of claim 1 , further comprising:
identifying a second path, in the physical space, of a second point scatterer, based on the respective intensities of the path points.

11. The method of claim 10 , further comprising:
determining via a comparison of the second path with the first path that both the first and second point scatterers are associated with a rigid body; and
determining a path of the rigid body according to at least one of the first and second paths.

12. A processor-implemented method for analyzing motions of objects, the method comprising
performing by a processor the steps of:
receiving a track model representing a plurality of candidate tracks of motion of an object in a physical space, the track model comprising a time variable and a parameter set, each unique value of the parameter set representing a distinct candidate track in the physical space, the track model being valid for a plurality of data collection intervals;
receiving observed data from a sensor, the observed data being collected from a start time up to an end time;
selecting a plurality of candidate tracks, each candidate track being represented by the track model and corresponding to a respective selected value of the parameter set;
generating, using a functional, a plurality of sets of expected sensor data from the track model, each set corresponding to both a respective one of the selected candidate tracks and a respective value of the parameter set;
identifying a candidate track as an expected track by optimizing a relation of the plurality of sets of expected sensor data and the observed data; and
determining the candidate track as a physical path of the object in the physical space.

13. The method of claim 12 , wherein the track model comprises a Kepler gravity model.

14. The method of claim 12 , wherein:
the track model comprises a ballistic track model; and
the parameter set of the track model comprises:
a parameter representing total energy of the object;
a parameter representing orbital angular momentum magnitude of the object;
a subset of parameters representing orientation of an orbit of the object in space; and
a parameter representing time of periapsis of the object.

15. The method of claim 12 , wherein the sensor is selected from the group consisting of a radar, a lidar, and a camera.

16. The method of claim 12 , wherein the sensor comprises: a multi-element radar; and
the data collection interval comprises a dwell time of the radar.

17. The method of claim 12 , wherein optimizing the relation comprises: assigning a probability to each candidate track represented by a respective parameter value such that:
across all selected parameter values a summation of a function of the candidate track probabilities and respective sets of expected sensor data is equal to the observed data within a specified threshold; and
entropy of the assigned probabilities is maximized.

18. The method of claim 12 , wherein the optimizing step comprises:
selecting a parameter value such that a function of the set of expected sensor data corresponding to that parameter value and the observed data is minimized across all selected parameter values.

19. The method of claim 12 , wherein the:
the start time corresponds to a time at which the motion of the object started; and
the end time corresponds to a time at which a track of the object is to be determined.

20. The method of claim 19 , wherein a time difference between the end time and the start time comprises an integer multiple of a data collection interval.

21. A method for analyzing characteristics of objects, the method comprising, performing at a first station in a plurality of stations, the steps of:
collecting observations using a sensor;
via a communication module, at least one of: (i) receiving observations collected by a second station in the plurality of stations, and (ii) exchanging data with a third station in the plurality of stations; and
solving jointly by a processor and with the third station, an optimization task having constraints that are based on at least one of: (i) observations collected by the sensor, and (ii) the received observations, to determine a characteristic of an object, the solving jointly comprising distributed processing to identify a first path, in a physical space, of a first point scatterer, based on respective intensities of path points in a set of point paths, the constraints comprising a set of observations and a plurality of sets of phase shifts corresponding to a point scatterer associated with a respective path point in the set of path points, each path point representing in a path space a respective expected path of motion of a point scatterer in the physical space.

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Assignees

• Qualcomm Technologies Inc

Inventors

• Baskaran Muthu M
• Henretty Thomas
• Johnson Ann
• Konstantinidis Athanasios
• Langston M H
• Mcmahon Janice O
• Meister Benoit J
• Mountcastle Paul D
• Naqvi Aale
• Pradelle Benoit
• Ramananandro Tahina
• Tavarageri Sanket
• Lethin Richard A

Classifications

• B64U2101/20

  for use as communications relays, e.g. high-altitude platforms · CPC title

• G01S13/723 Primary

  by using numerical data · CPC title

• G01S7/411

  Identification of targets based on measurements of radar reflectivity (G01S7/415 takes precedence) · CPC title

• G01S7/4802

  using analysis of echo signal for target characterisation; Target signature; Target cross-section · CPC title

• B64C39/024

  of the remote controlled vehicle type, i.e. RPV · CPC title