Back to results
US 2014/0111372 A1Published application
Sensor system and method for determining target location using sparsity-based processing
AssigneeSaab Sensis Corporation
InventorRyan Haoyun Wu
Life of this patent
Estimating the term…
Abstract
A system and method for an arrayed sensor to resolve ambiguity in received signals, improve direction of arrival accuracy and estimate a location of one or more targets in an environment including signal interference.
Claims23
A sensor system for estimating a location of a target using a sparse representation framework comprising:
- a plurality of receiving elements for receiving energy signals including an energy signal-of-interest, other transmitted energy signals and coherent interference;
- and
- at least one receiver comprising at least one processor using the sparse representation framework for processing the received energy signals;
- and a sparsity solver for estimating the location of the target,
- wherein the at least one processor using a sparse representation framework:
- receives received energy signals and separates the energy signal-of-interest present in each snapshot collection window in at least one of a spatial domain, a temporal domain and a frequency domain, so that only the energy signal-of-interest and associated coherent interference are present in a snapshot;
- converts the energy signal-of-interest and associated coherent interference present in each snapshot from an energy signal in a time domain to an energy signal in a frequency domain;
- divides the energy signal in the frequency domain in each snapshot with a reference signal which is not equal to 0;
- arranges a search grid around an area defined an angle θ and a delay τ on an at least 2-D grid;
- forms matrix vector equations using a signal model matrix;
- and
- applies the sparsity solver to solve the matrix vector equations to estimate a location of the target.
9 more elements
A sensor system for estimating a location of a target using a sparse representation framework where at least one interfering transmitter is transmitting an interfering energy signal, comprising:
- at least one transmitter for transmitting an energy signal in at least one direction, the energy signal reflecting off of at least the target;
- a plurality of receiving elements for receiving energy signals including an energy signal-of-interest, the energy signal reflecting off of at least the target, transmitted interfering energy signals and coherent interference;
- and
- at least one receiver comprising at least one processor using the sparse representation framework for processing the received energy signals;
- and a sparsity solver for estimating the location of the interfering transmitter transmitting the interfering signal passively and the location of the target actively,
- wherein to passively estimate the location of the interfering transmitter transmitting the interfering signal the at least one processor using a sparse representation framework:
- receives energy signals and separates the interfering energy signal present in each snapshot collection window in at least one of a spatial domain, a temporal domain and a frequency domain, so that only the interfering energy signal and associated coherent interference are present in a snapshot;
- converts the interfering energy signal and associated coherent interference present in each snapshot from an energy signal in a time domain to an energy signal in a frequency domain;
- divides the energy signal in the frequency domain in each snapshot with a reference signal which is not equal to 0;
- arranges a search grid around an area defined an angle θ and a delay τ on the at least 2-D grid, where a frequency domain measurement ratio term for angle θ is represented as follows:
- r
- n
- ,
- m
- =
- ∑
- k
- =
- 1
- K
-
-
- ∑
- l
- =
- 1
- L
-
-
- α
- k
- ,
- l
-
-
- -
- j
-
-
- 2
-
-
- π
-
-
- f
- m
-
- (
- τ
- k
- -
- Δ
-
-
- t
- n
- ,
- l
- )
-
- ∑
- k
- =
- 1
- K
-
-
- ∑
- l
- =
- 1
- L
-
-
- α
- k
- ,
- l
-
-
- -
- j
-
-
- 2
-
-
- π
-
-
- f
- m
-
- (
- τ
- k
- -
- Δ
-
-
- t
- n
-
-
- 0
- ,
- l
- )
-
- calculates all possible values for term (τk−Δtn,1) based on the search space defined by values of angle θ and values of delay τ, sorts the calculated values for the term (τk−Δtn,1) from small to large and denotes the sorted calculated values for the term (τk−Δtn,1) from small to large as follows:
- {εn,1,εn,2, . . . εn,V}
- where:
- V=KL and the corresponding amplitudes are denoted as {β1, ε2, . . . , βV}, which belong to the set of amplitudes α, and the terms corresponding to (τk−Δtn0,1) are now denoted as {εn0,1, εn0,2, . . . εn0,V}, and are arranged based on the sorting sequence of the calculated values for (τk−Δn,1) and are not necessarily in an ascending order;
- rearranges the spectrum ratio equation using the new notations to obtain the following homogeneous spectrum ratio equation in matrix vector form;
- [
- ϕ
- 11
- …
- ϕ
- V
-
-
- 1
- ⋮
- ⋱
- ⋮
- ϕ
- 1
-
-
- M
- …
- ϕ
- VM
- ]
-
- ϕ
-
- [
- β
- 1
- ⋮
- β
- V
- ]
-
- β
- =
- [
- 0
- ⋮
- 0
- ]
- and the sparsity solver solves for β, and estimates a location of the interfering transmitter transmitting the interfering signal where the solution of β is spanned by the null space of φ, a correct β is sparse, and β cannot be zero;
- and
- wherein to actively estimate the location of the target the at least one processor using a sparse representation framework:
- applies spatial nulling to suppress the interfering energy signal transmitted from the estimated location of the target transmitting the interfering energy signal;
- detects the target in an azimuth-elevation-range resolution cell and establishes a three-dimension (3-D) grid covering an area including the azimuth-elevation-range resolution cell containing the one or more targets;
- reforms the target location estimation problem into a linear angle-excess range estimation problem for the target represented in the general form:
- Ax=y
- where:
- x is a solution vector,
- A is a matrix of known functions, and
- y is a vector from observation;
- forms a number of measurement equations, the number of measurement equation being equal to N×M, where N is the number of outputs from receiving elements of the sensor array, and M is the number of frequency sampling points available;
- forms a matrix based on the N×M measurement equations for the solution vector y and a K×L search space in the 3-D grid;
- forms a 3-D image of the K×L search space, where the solution vector x for the target on the 3-D image is sparse when a number of grid points within the K×L search space is much greater than a number of grid points P having a value above a noise floor of the K×L search space (KL>>P);
- and
- applies the sparsity solver to solve the matrix for the solution vector x to estimate a location of the target.
167 more elements
A method for estimating a location of a target using a sparse representation framework comprising:
- receiving energy signals including an energy signal-of-interest, other transmitted energy signals and coherent interference;
- and
- processing the received energy signals in at least one processor using a sparse representation framework the processing comprising:
- receiving received energy signals and separating the energy signal-of-interest present in each snapshot collection window in at least one of a spatial domain, a temporal domain and a frequency domain, so that only the energy signal-of-interest and associated coherent interference are present in a snapshot;
- converting the energy signal-of-interest and associated coherent interference present in each snapshot from an energy signal in a time domain to an energy signal in a frequency domain;
- dividing the energy signal in the frequency domain in each snapshot with a reference signal which is not equal to 0;
- arranging a search grid around an area defined an angle θ and a delay τ on an at least 2-D grid;
- forming matrix vector equations using a signal model matrix;
- and
- applying a sparsity solver to solve the matrix vector equations to estimate a location of the target.
7 more elements
A method for estimating a location of a target using a sparse representation framework wherein at least one interfering transmitter is transmitting an interfering energy signal, the method comprising:
- receiving energy signals including an energy signal-of-interest, the interfering transmitted energy signal and coherent interference;
- and
- processing the received energy signals in at least one processor using a sparse representation framework comprising:
- receiving received energy signals and separating the interfering energy signal present in each snapshot collection window in at least one of a spatial domain, a temporal domain and a frequency domain, so that only the interfering energy signal and associated coherent interference are present in a snapshot;
- wherein passively estimating the location of the interfering transmitter transmitting the interfering signal of interest further comprises:
- converting the interfering energy signal and associated coherent interference present in each snapshot from an energy signal in a time domain to an energy signal in a frequency domain;
- dividing the energy signal in the frequency domain in each snapshot with a reference signal which is not equal to 0;
- arranging a search grid around an area defined an angle θ and a delay τ on the at least 2-D grid, where a frequency domain measurement ratio term for angle θ is represented as follows:
- r
- n
- ,
- m
- =
- ∑
- k
- =
- 1
- K
-
-
- ∑
- l
- =
- 1
- L
-
-
- α
- k
- ,
- l
-
-
- -
- j
-
-
- 2
-
-
- π
-
-
- f
- m
-
- (
- τ
- k
- -
- Δ
-
-
- t
- n
- ,
- l
- )
- ∑
- k
- =
- 1
- K
-
-
- ∑
- l
- =
- 1
- L
-
-
- α
- k
- ,
- l
-
-
- -
- j
-
-
- 2
-
-
- π
-
-
- f
- m
-
- (
- τ
- k
- -
- Δ
-
-
- t
- n
-
-
- 0
- ,
- l
- )
- calculating all possible values for term (τk−Δtn,1) based on the search space defined by values of angle θ and values of delay τ, sorts the calculated values for the term (τk−Δtn,1) from small to large and denotes the sorted calculated values for the term (τk−Δtn,1) from small to large as follows:
- {εn,1,εn,2, . . . , εn,V}
- where:
- V=KL and the corresponding amplitudes are denoted as {β1, β2, . . . , βV}, which belong to the set of amplitudes α, and the terms corresponding to (τk−Δtn0,1) are now denoted as {εn0,1, εn0,2, . . . εn0,V}, and are arranged based on the sorting sequence of the calculated values for (τk−Δtn,1) and are not necessarily in an ascending order;
- rearranging the spectrum ratio equation using the new notations to obtain the following homogeneous spectrum ratio equation in matrix vector form;
- [
- ϕ
- 11
- …
- ϕ
- V
-
-
- 1
- ⋮
- ⋱
- ⋮
- ϕ
- 1
-
-
- M
- …
- ϕ
- VM
- ]
-
- ϕ
-
- [
- β
- 1
- ⋮
- β
- V
- ]
-
- β
- =
- [
- 0
- ⋮
- 0
- ]
- and solving for β using the sparsity solver to estimate a location of the interfering transmitter transmitting the interfering signal where the solution of β is spanned by the null space of φ, a correct β is sparse, and β cannot be zero, and
- wherein actively estimating the location of the target further comprises:
- applying spatial nulling to suppress the interfering signal transmitted from the estimated location of the interfering transmitter target transmitting the interfering signal;
- transmitting an energy signal in at least one direction, the energy signal reflecting off of at least the target;
- detecting the target in an azimuth-elevation-range resolution cell and establishing a three-dimension (3-D) grid covering an area including the azimuth-elevation-range resolution cell containing the one or more targets;
- reforming the target location estimation problem into a linear angle-excess range estimation problem for the target represented in the general form:
- Ax=y
- where:
- x is a solution vector,
- A is a matrix of known functions, and
- y is a vector from observation;
- forming a number of measurement equations, the number of measurement equation being equal to N×M, where N is the number of outputs from receiving elements of the sensor array, and M is the number of frequency sampling points available;
- forming a matrix based on the N×M measurement equations for the solution vector y and a K×L search space in the 3-D grid;
- and
- forming a 3-D image of the K×L search space, where the solution vector x for the target on the 3-D image is sparse when a number of grid points within the K×L search space is much greater than a number of grid points P having a value above a noise floor of the K×L search space (KL>>P);
- and
- solving the matrix for the solution vector x using the sparsity solver to estimate the location of the target.
164 more elements
Dependencies are inferred from the claim text.