Patent Application
20130031046
The present disclosure relates to devices, methods, and systems for model based degree-of-angle localization.
Localization detection (e.g., determining the location of a transmitter) can be an important part of rescue operations. For example, firefighters entering a dwelling that is on fire can become disoriented. If a disoriented firefighter wears a transmitter that emits a signal, localization detection can aid other firefighters in locating the disoriented firefighter.
Typical localization detection techniques with an array of antennas focus on transmitters as points in space. However, multipath dispersion effects (e.g., scattered signals due to reflection of the signals off objects in the environment) can cause an array to inaccurately detect the transmitter emitting the signals, in some instances. Arrays can, for example, be a uniform linear array (ULA) or a uniform circular array (UCA). An individual ULA or UCA can sample signals in one dimension. These devices can be helpful for localization, but have some limitations, such as their one dimensional nature.
Devices, methods, and systems for model based degree-of-angle localization are described herein. For example, one or more device embodiments include a memory and a processor.
Benefits of embodiments of the present disclosure include, but are not limited to, generic modeling of degree-of-angle (DOA) localization, that can be applied via a number of rings in a multi-ring array and a number of receiving elements in each ring of the multi-ring array. Such modeling embodiments can provide the benefit of utilizing the same modeling process across different multi-ring array devices.
Benefits of such embodiments include, but are not limited to, simplifying the modeling method and/or procedure of a multi-ring array device. Embodiments of the present disclosure can, for example, provide models capable of DOA based localization in environments with multiple obstacles between a transmitting device and a receiving device. That is, embodiments can, for example, provide the benefit of DOA localization in environments where a direct line of sight between the transmitter and receive is lacking.
In some embodiments, the processor can be configured to execute executable instructions stored in the memory to construct a model of a number of signals, where the model includes a number of parameters. The processor can be utilized to execute the executable instructions to estimate the number of parameters and calculate range information of the number of signals. Range information can be calculated via a number of techniques including, but not limited to, a matrix pencil method. Range information can include, but is not limited to, the distance from the computing device or ring array location to the transmitter transmitting the signals. In some such embodiments, the processor executes the executable instructions to estimate a location of a transmitter transmitting the number of signals.
Devices, methods, and/or systems in accordance with one or more embodiments of the present disclosure can be utilized to localize signals. Some embodiments of the present disclosure can be utilized to localize signals in dense multipath environments, such as an indoor environment. Benefits of localizing signals in dense multi-path environments with a number of obstacles between the signal transmitter and the receiver can include more accurate signal localization, faster signal localization, and/or capability to localize a number of different transmitters.
Further, embodiments of the present disclosure can be utilized to construct models that are independent of the number of rings in a multi-ring array. Models that are independent of the number of rings can increase the versatility of the modeling method and/or procedure. For example, the same modeling procedure and/or method can be used on a multi-ring array of two rings and/or ten rings.
Various embodiments of the present disclosure can increase sampling resolution, for example, by adjusting the number of rings and receiving elements of the multi-ring array, without having to alter the distributed source model. This increasing of sampling resolution can provide the benefit of greater signal localization accuracy, in some instances. Further, increasing the number of receivers can increase the total number of signals received and therefore, increase the accuracy of signal localization.
In the following detailed description, reference is made to the accompanying drawings that form a part hereof. The drawings show by way of illustration how one or more embodiments of the disclosure may be practiced. These embodiments are described in sufficient detail to enable those of ordinary skill in the art to practice one or more embodiments of this disclosure. It is to be understood that other embodiments may be utilized and that process, electrical, and/or structural changes may be made without departing from the scope of the present disclosure.
The figures herein follow a numbering convention in which the first digit or digits correspond to the drawing figure number and the remaining digits identify an element or component in the drawing. Similar elements or components between different figures may be identified by the use of similar digits. For example, 102 may reference element “02” in
As will be appreciated, elements shown in the various embodiments herein can be added, exchanged, combined, and/or eliminated so as to provide a number of additional embodiments of the present disclosure. The proportion and the relative scale of the elements provided in the figures are intended to illustrate the embodiments of the present disclosure, and should not be taken in a limiting sense.
As used herein, “a” or “a number of” something can refer to one or more such things. For example, “a number of radio sensors” can refer to one or more radio sensors.
Indoor environments include, but are not limited to, dwellings, offices, buildings, warehouses, mines, sewers, etc. Outdoor environments include, but are not limited to, parks, forests, parking lots, construction zones, war zones, etc.
As shown in
As shown in
With respect to the present disclosure, a transmitter is a device that can emit (e.g., transmit) signals within an environment. As defined herein, signals can include, but are not limited to, electromagnetic (e.g., radio) waves that are modulated or continuous.
In the embodiment of
In the embodiment illustrated in
Signal 108-1, as illustrated by
Signals 108-1 and 108-2 are received by computing device 102 as reflected signals 110-1 and 110-2, respectively. Reflected signals as used herein are referred to as scattered signals. In one or more embodiments, computing device 102 can receive direct line signals, scattered signals, and/or combinations thereof. For example, a computing device may receive both, direct line signals and scattered signals that have been reflected off of objects within the environment.
Such embodiments of the present disclosure can provide the benefit of not requiring direct line signals from a transmitter for signal localization. This allows for localization in dense multipath environments such as in a house or a mine. Further, embodiments of the present disclosure can, for example, construct a model that is independent of the source of the signal. That is, the constructed model can aid in signal localization with direct line signals, scattered signals, and/or combinations thereof.
A uniform circular array (UCA) is a circular array that has a number of uniform (e.g., evenly spaced) receiving elements. A receiving element is capable of intercepting and collecting a number of signals transmitted by a transmitter. One example of a receiving element is an antenna. A stack of UCAs can, for example, include a number of UCAs a known distance apart from one another. In one or more example, the UCAs can be vertically in line, stacked at an angle, stacked different vertical distances from one another, stacked different angles from one another, and/or combinations thereof, etc. For example, each UCA in a stack can be perpendicular to one another. In another example, Each UCA in a stack can be a distance above and/or below other UCAs in the stack and a known angle from the UCA directly above and/or below.
A model of the number of signals is constructed, 284, where the model includes a number of parameters. In one or more embodiments of the present disclosure, the model is constructed and positioned within the environment to receive signals that include a number of scattered signals and a number of direct line of sight signals.
In one or more embodiments of the present disclosure, the number of parameters can include an elevation angle parameter θ, an azimuth angle parameter φ, an azimuth angular spread parameter σθ, and/or an elevation angular spread parameter σφ. The constructed model can, for example, aid in identifying a location of a transmitter that is transmitting the signals.
In one or more embodiments, the constructed model is independent of the number of UCAs in the multi-ring array. That is, a method and/or procedure of constructing a model to aid in identifying a location of a transmitter according to embodiments of the present disclosure can, for example, be the same regardless of the number of rings and/or receiving elements in a multi-ring array.
Embodiments of the present disclosure can, for example, construct a distributed source model based a number of scattered signals. An example of a distributed source model, includes, but is not limited to:
For example, a(θ) represents a steering vector, x(t) represents a point source. s(θ, ψ, t)represents an angular signal density, θ represents a direction of arrival, ψ characterizes a spatial distribution of the source signal, and n(t) represents noise in the system.
As used herein, a steering vector represents a set of phase delays a plane wave experiences, evaluated at a receiving element. A point source is the source from which the signals are being transmitted. An angular signal density represents the number of signals received by the receiving elements within a certain arch angle (e.g., from 0 to 45 degrees of the x-axis).
A direction of arrival is the elevation angle at which the signal is received by the receiving element. Noise is the summation of random fluctuations in electrical signals and unwanted/disturbing energy from natural and/or man-made sources.
At 286, the method 280 estimates the number of parameters. In one or more embodiments, the number of parameters can be estimated via a weighted least square technique as discussed in connection with
The multi-ring model can, for example, include symmetrical spatial sampling in both the azimuth and elevation planes to achieve a more accurate angle of arrival computation. As discussed below, elevation angular accuracy can be increased, for example, by increasing the elevation aperture. Elevation aperture can be increase by increasing the number of elements in vertical plane (e.g., stacking UCAs on top of each other
Embodiments of the present disclosure, can, for example, estimate an angle of arrival (AOA) of the received signals as one of the elements. AOA measurement is a method for identifying the direction of a signal transmitted by a transmitter. AOA can consider the time difference of arrival (TDOA) of a number of signals at the number of elements of each ring of the multi-ring array. The AOA can include an azimuth AOA and/or an elevation AOA.
Range information can be calculated at 288. Range information can include, for example, the distance to the transmitter that is transmitting the signals. Range information can be calculated via a number of techniques including, but not limited to, a matrix pencil method.
In one or more embodiments, the matrix pencil method can include a covariance matrix, as discussed in connection with
In one or more embodiments, a desired accuracy can be achieved by altering the number of receiving elements per UCA and/or altering the number of UCAs in the multi-ring array. For example, increasing the number of receiving elements can increase the accuracy of the location determination of the transmitter.
As illustrated in
For example, when a location of the source of the scattered signals does not change temporally (e.g., the shape of the angular distribution does not change temporally) and the scattered signals received from that source at different angles are fully correlated, the distributed source can be said to be a coherently distributed source. That is, for a coherently distributed source, the signal components arriving form different directions can be modeled as the delayed and attenuated replicas of the same signals. For example, a coherently distributed source can include:
x(t)=∫∫a(θ,φ)s(t)ρ(←,φ;μ)dθdφ+n(t);
where x(t) is an array output vector, ρ(θ, φ, μ) is a deterministic angular weighting function of θ and φ but not of t, and is parameterized by the vector μ=(θ, σθ, φ, σφ) denoting the nominal elevation direction of arrival (DOA) θ, angular extension νθ of the elevation DOA, the nominal azimuth DOA φ, and angular extension σφ of the azimuth DOA, and a(θ,φ)=[ejη sin θ cos(φ−γ
The coherently distributed source model can be represented by:
x(t)=s(t)b(θ,σθ,φ,σφ)+n(t);
where b(θ, σ74, φ, σφ) is the steering vector. For example, the coherently distributed source model above can have a deterministic angular weighting function ρ(θ, φ; μ) of Gaussian shape:
Then, the steering vector b(θ, σθ, φ, σφ) for the distributed source model can be written as:
[b(θ,σθ,φ,σφ)]k≈[a(θ,φ)]k·e−η
The steering vector can account for a nominal elevation angle-of-arrival (θ), spread in elevation angle-of-arrival (σB), a nominal azimuth angle-of-arrival (φ) and a spread in azimuth angle-of-arrival (σφ). The steering vector can, for example, can account for receiving signals from both azimuth and elevation planes (3-dimensional) from the target radio. That is, it is a mathematical model of the multi-ring array which accounts for the distributed source model in 3-dimension.
The constructed model can resemble an environment (e.g., indoor/outdoor) and a statistically optimum estimation technique (e.g., maximum-likelihood) or semi-optimal technique (e.g., weighted least squares) can be applied to estimate the angle-of-arrival in both azimuth and elevation planes, and a matrix pencil method can aid in extracting the range parameters, and the fusion of all three parameters used for identifying the location of a transmitter that is transmitting the signal
Although the embodiment illustrated in
The spherical coordinate system is a three-dimensional graphical representation of an environment. Three numbers can represent any point in space: the radial distance from a fixed point (e.g., R, 426); the elevation angle from a fixed zenith direction (θ, 424); and an azimuth angle (φ, 430) measured from a reference plane (e.g., x-y plane). For example, the three numbers can represent the location of a transmitter relative to a computing device (e.g., receiver).
Receiving elements of the UCAs are displaced by the
angle
for k=1, 2, . . . , L, from the x axis. That is, receiving elements can, for example, be numbered starting in the positive direction from the x-axis, with receiving element 1 (e.g., 402-1). The position vector of each location is pN=(r cos γk, r sin γk, −(N−1)d), respectively.
When a signal with a wave value k0=2π/λ, for example, propagates in direction −r, the phase difference between the received signal at the origin and the received signal at element k of array 420-N is ψk1=ej·k
The received signal vector in UCA 420-1 can be expressed as y(t)=s(t)c(θ, σθ, φ, σφ)+v(t), for example, when
for small angular extensions, which in the matrix form can be written as C≈B.φp where:
The total array output vector z(t)=[x1(t), x2(t), . . . xN(t)]T can be written as: z(t)=γs(t)+u(t); where γ=[B, BΦ1, . . . , B.ΦN]T and u(t) is the noise vector. This will provide a constructed covariance matrix as:
R
z
=E{z(t)ZH(t)}
In one or more embodiments, the number of parameters can be estimated via a maximum likelihood technique. For example, using the sampled array covariance matrix R and the constructed covariance matrix RZ, the log-likelihood function can be:
L(θ,σθ,φ,σφ)=log(RZ)+Tr{RZ−1·R}.
Finding the minima of the L can give the value of the desired parameters. Finding a value of the parameters can be beneficial, for example, to provide a three-dimensional location of a transmitter transmitting a number of signals relative to the receiver receiving the signals.
In one or more embodiments, the number of parameters can be estimated via a weighted least squares technique. For example, the weighted least squares criterion can be written as:
L=Tr{(RZR−1−l)2}
Minimizing the parameter L can give the value of the parameters using the least squares criterion. As discussed above, finding a value of the parameters can provide a three-dimensional location of a transmitter transmitting a number of signals relative to the receiver receiving the signals.
Embodiments of the present disclosure provide devices, methods, systems for model based degree-of-angle localization. Embodiments can construct models that are independent of the type of signal received (e.g., direct line or scattered). Benefits can include, but are not limited to, signal localization in dense multipath environments, unified model construction in a number of different arrays, etc.
Further, embodiments of the present disclosure can provide an added benefit of constructing models independent of the number of ring arrays in the multi-ring array and/or the number of receiving elements per ring array. Therefore, distributed source models of the present disclosure can be applicable to a number of different types of multi-ring arrays.
Although specific embodiments have been illustrated and described herein, those of ordinary skill in the art will appreciate that any arrangement calculated to achieve the same techniques can be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments of the disclosure.
It is to be understood that the above description has been made in an illustrative fashion, and not a restrictive one. Combination of the above embodiments, and other embodiments not specifically described herein will be apparent to those of skill in the art upon reviewing the above description.
The scope of the various embodiments of the disclosure includes any other applications in which the above structures and methods are used. Therefore, the scope of various embodiments of the disclosure should be determined with reference to the appended claims, along with the full range of equivalents to which such claims are entitled.
In the foregoing Detailed Description, various features are grouped together in example embodiments illustrated in the figures for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the embodiments of the disclosure require more features than are expressly recited in each claim.
Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.
