This invention concerns a method for estimating the frequency of a single frequency complex exponential tone in additive Gaussian noise. In another aspect the invention is a frequency estimation program for estimating the frequency of a single frequency complex exponential tone in additive Gaussian noise. In further aspects, the invention is computer hardware programmed to perform the method.
Earlier work to estimate the frequency of a single frequency complex exponential tone in additive Gaussian noise uses the fast Fourier transform (FFT) algorithm. The initial work on this topic was introduced by Rife and Boorstyn [1-3]. This paper introduces an algorithm employing the FFT, which produces an estimate of the frequency with extremely low variance of the error. The variance of the frequency estimate is independent of the frequency of the signal. The algorithm has a low computational complexity implementation.
The received signal, r[n], is given by,
r[n]=s[n]+η[n], for n=0,1,2, . . . , N−1 (1)
where:
Rife and Boorstyn [1-4] suggest a method of estimating f by using a FFT. It is assumed that 0≦f≦fs. First, a coarse search is performed. Under noiseless conditions, the absolute value of the FFT output coefficient corresponding to the bin centre frequency closest to f will be maximum over the set of absolute values of the FFT output coefficients. The coarse search, performed by the FFT, narrows the frequency uncertainty, to
where an N point FFT is used. Then, a fine search method is used to further reduce the frequency uncertainty. A secant method is used to compute the estimate of f by successful approximates.
Define,
where, Y=FFT(r) and FFT(.) is the Fast Fourier Transform Operator.
Then the Rife and Boorstyn coarse search is,
where,
{circumflex over (f)}0 is the coarse frequency estimate in Hz.
Numerous other frequency estimation approaches have been suggested in the literature [5-10].
In a first aspect the invention is a method for estimating the frequency of a single frequency complex exponential tone in additive Gaussian noise, comprising the steps of:
performing the fast Fourier transform (FFT) on the tone;
estimating the frequency as the frequency corresponding to the largest FFT output coefficient magnitude;
computing a discriminant which is proportional to the frequency error in the initial frequency estimate using modified coefficients of the discrete Fourier transform (DFT) with center frequencies plus one half and minus one half of the FFT bin spacing relative to the initial frequency estimate;
mapping the value of the discriminant into the estimate of the frequency error in the initial frequency estimate using a mathematically derived function;
adding the estimate of the frequency error to the initial frequency estimate to get a first interpolated frequency estimate;
computing a further discriminant which is proportional to the frequency error in the first interpolated frequency estimate using modified coefficients of the discrete Fourier transform (DFT) with center frequencies plus one half and minus one half of the FFT bin spacing relative to the first interpolated frequency estimate;
mapping the value of the further discriminant into the estimate of the frequency error in the first interpolated frequency estimate using the mathematically derived function; and
adding the estimate of the frequency error in the first interpolated frequency estimate to the first interpolated frequency estimate to get a second interpolated frequency estimate.
The first interpolated frequency estimate is quite accurate because it is in a region of relatively low noise induced frequency error. The method generates an unbiased, low error variance estimate of the frequency. The performance of the method, above the signal to noise ratio threshold, is about 0.06 dB above the Cramer-Rao lower bound. The method is ideally suited to be utilised in a number of communications, signal processing and biomedical applications. The method is easily implemented in hardware or software with low computational overhead.
In theory, this technique of iteratively deriving an interpolated frequency estimate and then, using the frequency discriminant, a more precise frequency estimate can be continued infinitely times until a fixed point (or solution) occurs. At this fixed point, the discriminant function has zero value.
Several functions have been identified to compute the discriminant. In practice, different functions may require a different number of iterations to essentially converge to a fixed-point solution. However, discriminant functions defined by a wide class of functions using two DFT coefficients as the input converge to the same solution and therefore exhibit identical noise performance.
A first example of the discriminant, or distance metric, of frequency estimation error is:
So for the initial frequency estimate using the FFT,
and {circumflex over (ε)}=0.
Other examples of the discriminant having the properties required for the algorithm include:
and in particular,
where Re[.] is the real part and * denotes the complex conjugate.
In these equations, β and α are the modified DFT coefficients defined by,
It is also possible to define discriminant functions which use more than two DFT coefficients to obtain further improvements in frequency estimation performance in additive Gaussian noise relative to discriminants that use only two DFT coefficients. An example where 2M+2 coefficients are used, where 0≦M≦N/2−1 and the FFT coefficients are used in the discriminant with optimal weighting coefficients obtained by using the concept of matched filtering is,
where,
mod N indicates modulo N.
and, where, denotes complex conjugate.
are the modified DFT coefficients given by,
The discriminant using more than two DFT coefficients may be used in the last iteration to obtain additional frequency accuracy. In a similar manner, discriminant functions may be formulated which use more than two DFT coefficients and less or equal to all N FFT coefficients.
Additional frequency accuracy may be obtained by computing the frequency discriminant recursively until convergence for the frequency estimate is reached.
Convergence for the frequency estimate may be reached after zero to three iterations, depending upon the specific discriminant used and the signal to noise ratio.
In any iteration, the frequency discriminant may be computed using any one of the functional forms:
γ may vary on each iteration.
where, Re[.] denotes the real part and denotes the complex conjugate
In general, the frequency incremental shift Δfm(r) is related to the previously defined frequency discriminant, D, by,
The frequency discriminant may be driven to zero input and output values by either modifying the frequency of the DFT coefficients or frequency translating the signal. Signal frequency translation may be achieved by multiplication of the signal by a locally generated complex exponential signal. The advantage of frequency multiplication of the signal is that the algorithm may be implemented with a standard hardware, software, or combination hardware/software FFT. This FFT may be highly optimized for one or a multiplicity of processors operating as a system.
The process for obtaining additional frequency accuracy may be scaled to save multiplies by scaling the frequency estimate during recursion. The process may involve a final step of multiplying the scaled frequency estimate {circumflex over (f)}m+1Ts with the sampling frequency fs to remove the scaling from the frequency estimate.
In a second aspect, the invention is a frequency estimation program for estimating the frequency of a single frequency complex exponential tone in additive Gaussian noise, wherein the frequency estimation program has functionality to perform the method.
In a third aspect, the invention is computer hardware programmed to perform the method. The hardware may comprise a DSP processor chip, or any other programmed hardware.
Examples of the invention will now be described with reference to the accompanying drawings, in which:
Simulations of the invention show the rms frequency error performance of the algorithm vs SNR in dB, for different values of N.
The two-interpolation case essentially achieves the performance of the infinite interpolation case.
Referring first to FIGS. 1 to 4, the received signal r[n] is given by:
r[n]=s[n]+η[n], for n=0,1,2, . . . , N−1 (1)
Then, an initial frequency estimate {circumflex over (f)}0 is taken as the frequency corresponding to the largest FFT output coefficient magnitude. A discriminant which is proportional to the frequency error in the initial frequency estimate {circumflex over (f)}0 is computed using modified coefficients α0, β0 of the discrete Fourier transform (DFT) with center frequencies plus one half and minus one half of the FFT bin spacing relative to the initial frequency estimate {circumflex over (f)}0.
The value of the discriminant is then mapped into the estimate of the frequency error in the initial frequency estimate {circumflex over (f)}0 using a mathematically derived function.
The estimate of the frequency error is added to the initial frequency estimate {circumflex over (f)}0 to get a next interpolated frequency estimate {circumflex over (f)}1.
The process is then repeated, using the next interpolated frequency estimate {circumflex over (f)}1 and computing a new frequency discriminant to produce a next, more precise, frequency estimate {circumflex over (f)}2.
It is assumed that the signal to noise ratio (SNR) is sufficiently high such that the largest magnitude FFT coefficient corresponds to a frequency closest to the signal frequency. This assumes that the signal to noise ratio is sufficiently high that the probability of the statistical outlier event of a noise only FFT bin magnitude being larger than a FFT bin containing both signal and noise is negligible.
Define,
Then the discriminant, or distance metric, of frequency estimation error is defined as,
For the initial frequency estimate using the FFT,
and {circumflex over (ε)}=0.
In the noiseless case,
D(ε,{circumflex over (ε)}) is a monotonically increasing function of ε−{circumflex over (ε)}. Therefore, each D(ε,{circumflex over (ε)}), there is a unique inverse mapping to ε−{circumflex over (ε)}. Clearly, D(ε,{circumflex over (ε)})) may be used as a discriminant for fine frequency interpolation between FFT bin centre frequencies.
There exists some functional relationship such that,
where, ψ(.) is a monotone increasing function.
Assume that,
Without loss of generality, assume that {circumflex over (ε)}=0. Also assume the noise free case.
The FFT output coefficients are given by,
The discriminant can be expressed as,
After some trigonometric simplification,
This inverse mapping from D(ε,{circumflex over (ε)}) to ε−{circumflex over (ε)} can be obtained as,
Then the first interpolated frequency estimate, {circumflex over (f)}1, may be obtained, where,
The implication is that a two point (N=2) FFT is sufficient to obtain zero error frequency determination in the noiseless case. However, the Cramer-Rao lower bound is relatively large for N=2 and the SNR threshold is relatively large. There is a motivation to use larger N to reduce the rms frequency estimation error. However, the implication of larger N is increased computational complexity and longer delay time to obtain the transform results. It is desirable to obtain low frequency estimation error with the smallest possible N.
Define,
For large N or for any N and small |D|, the function ψ1(D) closely approximates ψ(D).
For the case of r[n] consisting of signal plus noise, the noise will cause a perturbation of D(ε,{circumflex over (ε)}), and some error in the frequency estimate will result. Although an algorithm for the exact frequency determination in the noiseless case has been presented, it will be shown that the noise performance of this algorithm improves substantially when |ε−{circumflex over (ε)}| is close to zero. Since the discriminant D(ε,{circumflex over (ε)}) can be used to get an interpolated frequency estimate with less than one half FFT bin size error, it then follows that the algorithm can be iterated to move |ε−{circumflex over (ε)}| towards zero and |D(ε,{circumflex over (ε)})| towards zero. In this way the variance of the frequency estimator output can be reduced.
The iterative algorithm is defined below.
Define,
Define a monotone increasing function of
D, φ(D), such that φ(0)=0,
The iterative algorithm is defined by,
which implies that {circumflex over (ε)}0=0.
Then,
{circumflex over (f)}1Ts={circumflex over (f)}0Ts+φ[D(ε,{circumflex over (ε)}0]
{circumflex over (f)}2Ts={circumflex over (f)}1Ts+φ[D(ε,{circumflex over (ε)}1]
* * *
{circumflex over (f)}mTs={circumflex over (f)}m−1Tsφ[D(ε,{circumflex over (ε)}m−1]
and,
{circumflex over (f)}∞=lim {circumflex over (f)}m
The steady state frequency estimate at the end of the iteration is {circumflex over (f)}∞.
Define,
for k=0,1,2,3
Then,
and, the normalized frequency error is,
{circumflex over (ε)}28 −ε=({circumflex over (f)}∞f)Ts
The iteration may be viewed as a convergence to a fixed point of the equation,
Theorem 1 below is referenced, [21];
Let g(x) be a continuous function on
Furthermore, assume that there is a constant 0<λ<1, with,
|g(x)−g(y)|≦λ|x−y|, for all x,y∈[a,b],
Then,
The two previously defined functions ψ(D) and ψ1(D) fulfil the requirements of φ(D) and may be used in the iteration to obtain {circumflex over (f)}∞. While ψ(D) iteration will tend to converge more rapidly than ψ1(D) iteration, both will yield identical values of {circumflex over (f)}∞. However, evaluation of ψ1(D) has lower computational complexity than evaluation of ψ(D). There is performance advantage in using ψ(D) when the computation is limited to a few iterations.
Assuming that the SNR is sufficiently high, it is highly probable that
This is an above threshold condition [1]. A fine interpolation is obtained to improve the frequency accuracy.
The normalized frequency estimate {circumflex over (f)}Ts is computed recursively in order to save computational complexity.
After computation of {circumflex over (f)}Ts, {circumflex over (f)} is obtained by ({circumflex over (f)}Ts)fs={circumflex over (f)}
First Algorithm
Referring to
At step 2, the peak search to find kmax is: kmax=max−1[|Y[k]|:0≦k≦N−1].
At step 3, the initial frequency estimate is computed by:
At step 4, recursion is started at m=0.
At step 5, the DFT coefficients for the m:th frequency estimate are computed:
The frequency discriminant is then computed:
The m+1:th frequency estimate, {circumflex over (f)}m+1, is computed:
{circumflex over (f)}m+1={circumflex over (f)}m+Δfm(r)
At step 6, convergence for the frequency estimate is reached if |{circumflex over (f)}m+1−{circumflex over (f)}m| is sufficiently small. If there is convergence, then the frequency estimate is {circumflex over (f)}m+1. If convergence for the frequency estimate has not been reached, then m is incremented by 1 and step 5 is repeated. In practice, the algorithm will converge for m=2. Therefore, only {circumflex over (f)}m for m=0, 1, and 2 need to be computed (two iterations) which means that the frequency estimate is {circumflex over (f)}f2.
Second Algorithm
To handle large N, a modification to the first algorithm is made. The modification is made to the step of computing the frequency discriminant.
Third Algorithm
A third algorithm is provided to improve the frequency accuracy. At step 1, the the N point complex FFT is computed. The FFT output coefficients are Y(k), 0≦k≦N−1.
At step 2, the peak search to find kmax is: kmax=max−1[|Y[k]|:0≦k≦N−1]
At step 3, the initial frequency estimate is computed by:
At step 4, the DFT coefficients for the initial frequency estimate are computed:
The frequency discriminant is then computed:
The first interpolated frequency estimate is computed:
{circumflex over (f)}1={circumflex over (f)}0+Δ{circumflex over (f)}0(r)
At step 5, recursion is started at m=1
At step 6, the DFT coefficients for the m:th frequency estimate are computed:
The frequency discriminant is then computed:
The m+1:th frequency estimate, {circumflex over (f)}m+1, is computed:
{circumflex over (f)}m+1={circumflex over (f)}m+Δfm(r)
At step 7, convergence for the frequency estimate is reached if |{circumflex over (f)}m+1−{circumflex over (f)}m| is sufficiently small. If convergence has been reached, then the frequency estimate is {circumflex over (f)}m+1. If convergence for the frequency estimate has not been reached, then m is incremented by 1 and step 6 is repeated. In practice, the algorithm will converge after {circumflex over (f)}2 is evaluated. Therefore, only {circumflex over (f)}m for m=0, 1, and 2 need to be computed. This algorithm is less computationally complex than the, first algorithm and has essentially the same convergence properties in the recursion.
Fourth Algorithm
A fourth algorithm is provided to improve the frequency accuracy. At step 1, the N point complex FFT is computed. The FFT output coefficients are Y(k), 0≦k≦N−1.
At step 2, the peak search to find kmax is: kmax=max−1[|Y[k]|:0≦k≦N−1]
At step 3, the initial frequency estimate is computed by:
At step 4, the DFT coefficients for the initial frequency estimate are computed:
The frequency discriminant is then computed:
The first interpolated frequency estimate is computed:
{circumflex over (f)}1={circumflex over (f)}0+Δf0
At step 5, recursion is started at m=1
At step 6, the DFT coefficients for the m:th frequency estimate are computed:
The frequency discriminant is then computed:
The m+1:th frequency estimate, {circumflex over (f)}m+1, is computed:
{circumflex over (f)}m+1={circumflex over (f)}m+Δfm(r)
At step 7, convergence for the frequency estimate is reached if |{circumflex over (f)}m+1−{circumflex over (f)}m| is sufficiently small. If convergence has been reached, then the frequency estimate is {circumflex over (f)}m+1. If convergence for the frequency estimate has not been reached, then m is incremented by 1 and step 6 is repeated. In practice, the algorithm will converge after {circumflex over (f)}2 is evaluated. Therefore, only {circumflex over (f)}m for m=0, 1, and 2 need to be computed. This algorithm is less computationally complex than the first, second or third algorithms and has essentially the same convergence properties in the recursion. There is reduced computational complexity in the computation of Δf1(r) because of the elimination of the need to compute square roots in the evaluation of the absolute value of complex variables.
Fifth Algorithm
A fifth algorithm is provided to improve the frequency accuracy. At step 1, the N point complex FFT is computed. The FFT output coefficients are Y(k), 0≦k≦N−1.
At step 2, the peak search to find kmax is: kmax=max−1[|Y[k]|:0≦k≦N−1]
At step 3, the initial frequency estimate is computed by:
At step 4, recursion is started at m=0
At step 5, the DFT coefficients for the m:th frequency estimate are computed:
The frequency discriminant is then computed:
where .γm is a set of non-negative constants.
γm is a constant, γm>0
The m+1:th frequency estimate, {circumflex over (f)}m+1, is computed:
{circumflex over (f)}m+1={circumflex over (f)}m+Δfm(r)
At step 6, convergence for the frequency estimate is reached if |{circumflex over (f)}m+1−{circumflex over (f)}m| is sufficiently small. If convergence has been reached, then the frequency estimate is {circumflex over (f)}m+1. If convergence for the frequency estimate has not been reached, then m is incremented by 1 and step 5 is repeated.
Scaled Computational Version of an Algorithm
The frequency scaled frequency estimate can be computed and then multiplied by fs. This process is described using the example of the first algorithm but can similarly be done for all the algorithms. The scaled computational version of the first algorithm is more computationally efficient as it saves multiplies.
At step 1, the N point complex FFT is computed. The FFT output coefficients are Y(k), 0≦k≦N−1.
At step 2, the peak search to find kmax is: kmax=max−1[|Y[k]|:0≦k≦N−1]
At step 3, the initial frequency estimate is computed by:
At step 4, recursion is started at m=0
At step 5, the DFT coefficients for the m:th frequency estimate are computed:
The frequency discriminant is then computed:
The m+1:th frequency estimate, {circumflex over (f)}m+1, is computed:
{circumflex over (f)}m+1Ts={circumflex over (f)}mTs+Δfm(r)Ts
At step 6, convergence for the frequency estimate is reached if |{circumflex over (f)}m+1−{circumflex over (f)}m| is sufficiently small. If convergence has been reached then the scaled frequency estimate is {circumflex over (f)}m+1Ts. If convergence for the frequency estimate has not been reached, then m is incremented by 1 and step 5 is repeated.
As a final operation, one additional multiply is require to remove the scaling from the frequency estimate.
({circumflex over (f)}m+1Ts)fs={circumflex over (f)}m+1 since Tsfs=1, where {circumflex over (f)}m+1 is the m+1:th frequency estimate.
Sixth Algorithm
A sixth algorithm is provided to improve the frequency accuracy. The sixth algorithm uses any of the previously defined functional forms for Δfm(r) for any step. The difference between the sixth algorithm and the other algorithms types is that the frequencies of the two modified DFT coefficients are not changed. Instead, the centre frequency of the signal is modified by multiplying the previously defined signal by e−j2πnΔf
There are situations where standard FFT or DFT functions are available in hardware, software, or combined hardware and software configurations. These FFT and DFT functions are highly optimised for their respective signal processors and are run at very high computational efficiency. Often, parallel processing for multiple processors is utilised extremely effectively. In these cases, the technique of frequency translation of the signal is of considerable implementation benefit. Very efficient computation is achievable. Frequently, an optimised large N point FFT runs faster on a parallel processor than the computation of two DFT coefficient.
For the sixth algorithm, z0(n)=r(n), n=0,1,2,3, . . . is initialised.
At step 1, the N point complex FFT is computed. The FFT output coefficients are Y(k), 0≦k≦N−1.
At step 2, the peak search to find kmax is: kmax=max−1[|Y[k]|:0≦k≦N−1]
At step 3, the initial frequency estimate is computed by:
At step 4, recursion is started at m=0
At step 5, the DFT coefficients for the m:th frequency estimate are computed:
The frequency discriminant, Δfm(r), is then computed for any of the functional forms as a function of αm and βm.
The m+1:th frequency estimate, {circumflex over (f)}m+1, is computed:
{circumflex over (f)}m+1={circumflex over (f)}m+Δfm(r)
At step 6, convergence for the frequency estimate is reached if |{circumflex over (f)}m+1−{circumflex over (f)}m| is sufficiently small. If convergence has been reached, then the frequency estimate is {circumflex over (f)}m+1. If convergence for the frequency estimate has not been reached, then the signal is frequency translated (by complex time domain multiplication):
zm+1(n)=zm(n)e−j2πnΔf
then m is incremented by 1 and step 5 is repeated.
For convergence, all algorithms have the same steady state performance. In the noiseless case, the first algorithm gives the exact frequency for one iteration, which is of great benefit in some applications. The exact frequency is obtained for any N, including N=2. The reason is that an exact functional mapping from the magnitudes of the two DFT coefficients to the frequency was analytically derived and used in the algorithm. This is a new analytical result and forms the basis of the algorithm.
For the case of signal in additive noise, D∞ is a random variable.
var[{circumflex over (f)}∞Ts]=var[ε∞]=var[φD∞)]
In general, |α| and |β↑ are both Rician distributed random variables. However, under high signal to noise ratio, both |α| and |β| are essentially Gaussian distributed random variables.
ε∞, which is a random variable, is found by the constraint,
{circumflex over (ε)}∞ will be perturbed, by the noise component in D∞. Even though D∞ is constrained to be zero, the constraint and noise induce randomness in ε∞. The noise perturbation in
D∞ induces the perturbation in ε∞.
The approach taken is the computation of the variance of D from the point of view of the creation of D from noisy observations and then to find the corresponding perturbation of {circumflex over (ε)}∞−ε.
For high signal to noise ratios, D(ε,{circumflex over (ε)}∞) may be represented by the first three terms of the Taylor Series expansion about, μα and μβ, which are the means of |α| and |β|, respectively.
Assuming {circumflex over (ε)}∞=ε
and for high signal to noise ratio,
Then,
E[D]=0,
It then follows that,
Then, for high signal to noise ratio, the normalized frequency error may be computed. The largest part of the probability density function of D is in the region of where the atan(x)≈x. Therefore,
The Cramer-Rao Lower Bound on the variance of the frequency error of any unbiased frequency estimator is given by,
The performance of the DFT based estimator may be compared to the Cramer-Rao Lower Bound.
For high SNR and large N, the performance frequency estimation variance is
above the Cramer-Rao Lower Bound.
in dB verses N, where N is the length of the FFT.
The reason for the performance improvement of the proposed class of algorithms relative to prior algorithms is the first frequency interpolation allows the computation of two DFT coefficients, which are ½ DFT bin spacing above the first interpolated frequency and ½ DFT bin space below the first interpolated frequency. While the first interpolation may still have significant error, which is dependent on the relationship of the true frequency relative to the FFT coefficient frequencies, the error discriminant evaluated for the first interpolated frequency will have a value close to zero. The variance of the frequency error is relatively low in the region of small values of the frequency discriminant. Therefore, the second interpolated frequency will have small error variance. There is significant noise performance advantage in using the first interpolation to allow a low error variance second interpolation. The interpolation may be iterated, with diminishing improvements of estimation accuracy, until convergence to a fixed point solution is obtained.
Simulation results have verified a single FFT and two iterations involving the computation of the discriminant function D(ε,{circumflex over (ε)}0) and D(ε,{circumflex over (ε)}1) are sufficient to obtain RMS frequency error performance close to the computation of {circumflex over (f)}∞. The first iteration moves the discriminant towards zero and decreases of |ε−{circumflex over (ε)}1|. The estimate resulting from the second iteration therefore results in small error variance of ε−{circumflex over (ε)}2.
The algorithm has complexity of O(N log2(N)+O(8N)=O[N log2(N)]
The algorithm has the same order of complexity as the original FFT for performance, which is very close to the Cramer-Rao Lower Bound.
The algorithm has the same order of complexity as the original FFT for performance, which is very close to the Cramer-Rao Lower Bound.
Iteration with ψ1(D)=D yield results very close to ψ(D) and saves considerable computational complexity. The fixed point solution for the two cases is identical.
Since the algorithm comes very close to convergence with two iterations, two iterations are sufficient for most applications. The performance improvement with additional iterations is small.
There are a number of discriminants, which have the same performance, when used iteratively to obtain the fixed point solution, as the previously introduced discriminants. The noise performance is identical, for iteration, because the fixed point solution is identical.
This class of discriminants includes functional forms,
and in particular,
and,
where * denotes complex conjugate.
And where Re[.] is the real part.
FIGS. 7 to 12 show the rms frequency error performance of the algorithm vs SNR in dB, for N=2,4,16,64,246, and 1024, respectively. Both the cases of one interpolation and two iterative interpolations are shown. The two interpolation case is essentially achieves the performance of the infinite interpolation case.
In summary, a new, low computational complexity, class of algorithms, which interpolates the result of a FFT, has been presented for the precise estimation of frequency of a complex exponential function in additive Gaussian noise. The performance of the algorithm, above the threshold in additive Gaussian noise, is about 0.06 dB above the Cramer-Rao lower bound. The algorithm is ideally suited to be utilized in a number of communications, signal processing and biomedical applications. The algorithm also has ideal characteristics for digital signal processor implementation.
There are a large number of applications for this invention, including:
It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the invention as shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.
Number | Date | Country | Kind |
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2002950040 | Jul 2002 | AU | national |
2002950296 | Jul 2002 | AU | national |
Filing Document | Filing Date | Country | Kind | 371c Date |
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PCT/AU03/00862 | 7/4/2003 | WO | 8/8/2005 |