Patent Application
20120322385
The present invention relates to magnetic communication systems, and more specifically magnetic communication receivers and methods for interference suppression and signal enhancement.
Underground communication systems, such as radio-frequency (RF) and magnetic signal based communication systems, are subject to noise and other types of interference which hinder their intended operation. For example, the orientation of the transmitter and/or the relative positions of the transmitter and the receiver can affect the direction and strength of the transmitted field with respect to the receiver. Likewise, the intervening medium, for example, the atmosphere, earth, as well as man-made obstructions, can affect the transmitted magnetic field at the receiver in ways that are difficult or impossible to predict.
These characteristics may be especially detrimental to the performance of magnetic communication systems which utilize directional antennas, such as systems used in direction finding operations. Direction finding is the process of determining the location of a transmission source (usually radio or magnetic-based). Critical applications for direction finding include emergency rescue operations in harsh operating conditions, for example, on or around mountains or in underground mines. As RF transmissions are typically less effective through materials such as earth and rock, magnetic field based communications are often preferred. However, as set forth above, magnetic communication systems are subject to inaccuracies and signal power reductions which make direction finding operations difficult.
Improved magnetic communication systems are desired.
In one embodiment of the present invention, a method for enhancing a magnetic communication signal while simultaneously suppressing noise and interference is provided. The method includes receiving a magnetic communication signal with a plurality of antenna elements. The relative amplitudes of a plurality of received signals are measured, by, for example, generating a first covariance matrix corresponding to the communication signal as received by each of the antenna elements. A second covariance matrix corresponding to the noise and interference as received by each of the antenna elements may also be generated, inverted, and combined with the first covariance matrix. Principal components analysis may be applied to this combined matrix to calculate a set of weights that are used to generate a single output waveform comprising a weighted sum of the plurality of received input waveforms.
In a second embodiment of the present invention, a method for enhancing a magnetic communication signal is provided. The method includes receiving a magnetic communication signal with a plurality of antenna elements. The relative amplitudes of a plurality of received signals are measured, by, for example, generating a covariance matrix corresponding to the communication signal as received by each of the antenna elements. Principal components analysis may be applied to this matrix to calculate a set of weights that are used to generate a single output waveform comprising a weighted sum of the plurality of received input waveforms.
In a third embodiment of the present invention, a method for suppressing noise and interference in a magnetic communication system is provided. The method includes receiving noise and interference with a plurality of antenna elements in the absence of a transmitted magnetic communication signal. The relative amplitudes of a plurality of received waveforms are measured, by, for example, generating a covariance matrix corresponding to the noise and interference as received by each of the antenna elements. Principal components analysis may be applied to the inverse of this matrix to calculate a set of weights that are used to generate a single output waveform comprising a weighted sum of the plurality of received input waveforms.
In a fourth embodiment of the present invention, a method for finding the direction of a magnetic transmitter relative to a magnetic receiver is provided. The method includes receiving a magnetic communication signal with three mutually perpendicular antenna elements. The relative amplitudes of a plurality of received signals are measured, by, for example, generating a covariance matrix corresponding to the communication signal as received by each of the antenna elements. Principal components analysis may be applied to this matrix to estimate the magnitude and orientation of the magnetic field at the receiver antenna's position. The direction to the transmitter can then be determined from the field's orientation.
It is to be understood that the figures and descriptions of the present invention have been simplified to illustrate elements that are relevant for a clear understanding of the present invention, while eliminating, for purposes of clarity, many other elements found in typical magnetic communication systems, such as underground magnetic communication systems. However, because such elements are well known in the art, and because they do not facilitate a better understanding of the present invention, a discussion of such elements is not provided herein. The disclosure herein is directed to all such variations and modifications known to those skilled in the art.
In the following detailed description, reference is made to the accompanying drawings that show, by way of illustration, specific embodiments in which the invention may be practiced. It is to be understood that the various embodiments of the invention, although different, are not necessarily mutually exclusive. Furthermore, a particular feature, structure, or characteristic described herein in connection with one embodiment may be implemented within other embodiments without departing from the scope of the invention. In addition, it is to be understood that the location or arrangement of individual elements within each disclosed embodiment may be modified without departing from the scope of the invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims, appropriately interpreted, along with the full range of equivalents to which the claims are entitled. In the drawings, like numerals refer to the same or similar functionality throughout several views.
Multi-element antennas may be used in magnetic communication systems in order to improve received signal quality. However, current signal processing methods used with these antennas achieve only marginal improvements, as they only utilize data from the antenna element that outputs the strongest signal. These methods ignore benefits that may be realized by optimally combining the received signals from each antenna element, such as signal enhancement and noise cancellation, as will be set forth with respect to embodiments of the present invention.
Embodiments of the present invention may utilize a linear combination of waveforms received from a plurality of antenna elements to decrease noise and interference, increase signal strength, and improve the signal to noise ratio (SNR). In one embodiment, a received waveform from each antenna element may be associated with a weight determined through principal component analysis of waveforms from all antenna elements. The received waveforms contain communication signals as well as noise and interference. The associated weights are used to create a single waveform comprising the weighted sum of the individual received waveforms. The weights operate to improve the signal-to-noise ratio by canceling out much of the noise by correlating noise components from each of the antenna elements while preserving or enhancing the desired communication signal.
In another embodiment, weights are calculated through principal component analysis of received waveforms containing communication signals. By applying these weights, a single waveform comprising the weighted sum of the individual received waveforms may be generated. The communication signal in this waveform may be stronger than any individual signal received by any antenna element.
In another embodiment, weights are calculated through principal component analysis of received waveforms containing only noise and interference. By applying these weights, a single waveform comprising the weighted sum of the individual received waveforms may be generated. The noise and interference in this waveform may be weaker than in any individual waveform received by any antenna element.
In another embodiment of the present invention, a three-element (e.g. tri-axis) receiver can be used in direction finding operations to estimate the location of a transmitter, thus aiding, for example, rescue efforts. By using a tri-axis receiver in conjunction with the above-described signal processing techniques, estimates of the magnitude and orientation of the magnetic field at the receiver's position may be calculated. The direction to the transmitter may then be determined from the azimuth angle of the field's orientation.
Referring generally to
Each antenna element 12,14,16 has a directional response that is maximal when the magnetic field to be received is aligned with its axis and minimal when the field is perpendicular to its axis. Response to a signal, such as that from an emergency transponder or beacon, varies significantly depending on the orientation of antenna elements with respect to the magnetic field of the transmitted signal. Because elements 12,14,16 may not be optimally oriented with respect to a signal, their reception abilities may be adversely affected.
A first embodiment of the present invention may remedy these drawbacks by combining waveforms received from each of elements 12,14,16 and processing them jointly to derive a weighted signal representative of the signal source. Specifically, using digital signal processing algorithms, signals from each of elements 12,14,16 may be combined to effectively rotate the antenna electronically. This process maximizes the signal strength for further processing and analysis by the remaining portions of the communication system. As will be set forth in detail below, resulting weights from this technique may strike a balance between enhancing the signal and suppressing noise and interference, thus improving the resulting signal to noise ratio relative to any single antenna element. With respect to these embodiments as shown, for example, in
Referring generally to
These waveforms are convolved with a filter 32, having a response h (Eq. 2), which is matched to the transmitted signal in order to eliminate noise and interference residing outside the frequency band of interest. A resulting vector {right arrow over (y)} is produced, representing the filtered waveforms from each of the antenna elements 1 through N:
{right arrow over (y)}=h*{right arrow over (x)} Eq. 2
Input vector {right arrow over (y)} is assumed to be a combination of a desired signal {right arrow over (s)} and undesired interference and noise {right arrow over (n)}:
{right arrow over (y)}={right arrow over (s)}+{right arrow over (n)} Eq. 3
It may be assumed that signal {right arrow over (s)} and noise {right arrow over (n)} are zero-mean and independent of each other so that a covariance matrix of the input Ry is simply the sum of a signal covariance matrix Rs and a noise covariance matrix Rn:
Where E(x) is the expected value of x and {right arrow over (y)}H is the conjugate transpose of the column vector {right arrow over (y)}. Embodiments of the present invention are operative to produce a set of weights {right arrow over (w)} that maximizes the signal-to-noise ratio (SNR) in the scalar received signal r, which is a weighted sum of the elements of input vector {right arrow over (y)}:
r={right arrow over (w)}
H
{right arrow over (y)} Eq. 5
The power of the received signal is:
Where r* is the complex conjugate of the scalar r. The signal-to-noise ratio is then:
Maximizing the SNR is equivalent to maximizing the signal power while keeping the noise power constant. A Lagrange multiplier may be used to find weights {right arrow over (w)} that maximize a function ƒ({right arrow over (w)})=Ps subject to a constraint g({right arrow over (w)})=Pn−c=0:
∇f({right arrow over (w)})=λ∇g({right arrow over (w)}) Eq. 8a
∇({right arrow over (w)}HRs{right arrow over (w)})=λ∇({right arrow over (w)}HRn{right arrow over (w)}−c) Eq. 8b
R
s
{right arrow over (w)}=λR
n
{right arrow over (w)} Eq. 8c
R
n
−1
R
s
{right arrow over (w)}=λ{right arrow over (w)} Eq. 8d
Where ∇ is the gradient operator.
It should be noted that the form of the last expression comprises the definition of an eigenvector of the matrix Rn−1Rs. Thus, the SNR is maximized when weights {right arrow over (w)} are chosen that are proportional to eigenvector {right arrow over (q)}1 corresponding to the largest eigenvalue λ1 of matrix Rn−1Rs:
As set forth above, and still referring to
As previously set forth in Eq. 4a, covariance matrix Ry is defined as follows:
This covariance matrix may be computed at step 34 and/or step 40 of
{circumflex over (R)}
k
={right arrow over (y)}
k
{right arrow over (y)}
k
H Eq. 11
Alternatively, Ry may be approximated using an exponentially weighted moving average taking the form:
{circumflex over (R)}
k
=α{right arrow over (y)}
k
{right arrow over (y)}
k
H+(1−α){circumflex over (R)}k-1 Eq. 12
In yet another embodiment, Ry may be approximated using an average over a block of M samples:
In order to achieve noise and interference suppression, the covariance matrix of the received noise and interference Rn is estimated in step 40 by averaging the input {right arrow over (y)} when the desired communication data is known to be absent from the received signal. It is envisioned that several methods may be employed to ensure that the desired signal is absent when making this estimation.
In one embodiment, the input {right arrow over (y)} may be sampled when the desired signal is known to be absent, such as during a pre-determined silent interval or a silent interval that is part of a collision avoidance protocol. A second method of estimating the covariance of the interference Rn uses only samples of the input {right arrow over (y)} with amplitude exceeding a predetermined threshold. This method assumes that the amplitude of the interference significantly exceeds that of the desired signal. A third method of estimating the covariance of interference Rn detects the presence of the desired signal, estimates a waveform representative thereof, and subtracts the waveform from the input {right arrow over (y)}. The result of this subtraction will be comprised primarily of interference. A fourth method of estimating the covariance of interference Rn includes using a frequency-selective filter to attenuate the desired signal. The resulting filtered waveform will be out of band interference used to model the in-band interference. This method assumes that the covariance of out of band interference is similar to the covariance of the in-band interference.
Still referring to step 34, the covariance matrix of the desired signal Rs may be estimated by averaging the filtered input {right arrow over (y)} when the signal is present. As a result of the accompanying noise and interference, Ry≠Rs. However, if the noise and interference are independent of the desired signal, then:
R
s
=R
y
−R
n. Eq. 14
Thus, if the interference is slowly varying, an estimate of R, can be obtained by subtracting a previous estimate of Rn from the current estimate of Ry.
In another embodiment of the present invention, Rs may be estimated by providing a long-duration single-frequency pulse. A filter matched to the pulse may be provided to improve the SNR of the received signal. As the SNR rises, the received signal covariance Ry approaches the ideal signal-only covariance Rs. The use of the sinusoid as a training signal is acceptable as the system is only responsive to spatial information regarding the transmitter, and not details of the signal.
In another embodiment, each communication system transmits a periodic training signal (e.g., with a period on the order of one minute, or at a rate consistent with changes to the interference environment) that would consist of one second, for example, of a sinusoid followed by one second of silence. The training signal would be match filtered to generate Rs and the silent period would be sampled to generate Rn. Various communication systems may utilize synchronized clocks so that every system would agree on the absolute times at which each system transmits its training pulses. If the training pulses occur at a low enough rate there would be no significant impact on normal communication.
Once signal covariance Rs and noise and interference covariance Rn have been estimated in step 34 and step 40 of
A=R
n
−1
R
s Eq. 15
and the eigenvector {right arrow over (q)}1 corresponding to the largest eigenvalue λ1 of matrix A may be calculated in step 36.
A{right arrow over (q)}
1
=λ{right arrow over (q)}
1 Eq. 16
From matrix A, an eigenvector {right arrow over (q)}1 corresponding to the largest eigenvalue λ1 of the matrix (e.g. the 1st principal eigenvector) may be calculated. As will be understood by one of ordinary skill in the art, computing eigenvector {right arrow over (q)}1 can be accomplished by any number of techniques, such as principal components analysis or the power method.
Principal component analysis may include finding the largest diagonal element of A and selecting the corresponding column as an approximation of the eigenvector {right arrow over (q)}1. This is equivalent to a single step of the power method with an initial unit vector. Alternatively, a variation of the power method using an exponentially weighted moving average taking the following form may be utilized:
{circumflex over (q)}
k
=αA{circumflex over (q)}
k-1+(1−α){circumflex over (q)}k-1 Eq. 17
This vector may be normalized periodically to prevent numeric overflow or underflow in the processing system.
A weight vector proportional to the resulting eigenvector {right arrow over (q)}1 is applied to the input signal to produce a scalar output in step 38:
The resulting scalar waveform r will have an improved SNR relative to the original received signal and can be used as the input to a demodulator of a communication system receiver for further processing and/or analysis.
Referring generally to
Similarly, referring to
Referring generally to
A=R
s Eq. 20
with weights calculated and applied (step 38) as described in the first embodiment of the present invention.
A third embodiment of the present invention minimizes the noise and interference instead of maximizing the SNR. This is accomplished by performing principal components analysis to find the principal eigenvector {right arrow over (q)}1 of the matrix
A=R
n
−1 Eq. 21
with weights calculated and applied as described in the first embodiment of the present invention.
Referring generally to
Direction finding operations may include the steps described above with respect to
From the weights, the general bearing 30 of the transmitted signal can be estimated. In particular, the x, y, and z components of the weight vector {right arrow over (w)} are converted from Cartesian coordinates to spherical coordinates to obtain azimuth and elevation angles θ and φ, with:
The source of the magnetic transmission lies along a line of bearing indicated by the estimated azimuth angle θ. In one embodiment of the present invention, measurements may be made by multiple receivers at varying locations, or multiple measurements by a single receiver at multiple positions. In this way, triangulation can be used to estimate the position of the transmitter.
In any of the above-described embodiments, it should be understood that the signal processing described herein may be implemented by known digital signal processing (DSP) techniques and associated hardware. For example, a processor 51 (
While the foregoing invention has been described with reference to the above-described embodiment, various modifications and changes can be made without departing from the spirit of the invention. Accordingly, all such modifications and changes are considered to be within the scope of the appended claims. Accordingly, the specification and the drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.
Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations of variations of various embodiments. Combinations 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.
