MULTIPATH REDUCTION SYSTEM
TECHNICAL FIELD The present invention relates to communication receivers and is particularly directed to adaptively suppressing interference due multipath and other sources.
BACKGROUND ART To get high quality reception, communication systems, which include radio and television, require a strong signal that is not corrupted by noise or interference. One form of interference that can severely degrade reception is multipath. Multipath occurs when the transmitted signal arrives at the receiver simultaneously from more than one direction. The multiple paths are generally due to reflections of the transmitted signal from hills, buildings, etc.; they can also be the result of atmospheric phenomena. This causes distortion in both the phase and the amplitude of the received signal. This can result in deep signal strength fades, overlapping data, clicking, etc.
One of the better approaches for reducing multipath distortion is to design the antenna pattern gain characteristics to reject the indirect paths by placing a null in their direction of arrival. This eliminates the indirect paths altogether. It is easy to accomplish when conditions are known and do not change. But in most communication situations, conditions do change. The adaptive array has been used to automatically change the antenna pattern as the conditions change.
In applying an adaptive array to the general communications problem where the direction of arrival (DOA) and the time of arrival (TOA) of the signal of interest are unknown, the least means squared error algorithm (LMS) is well suited. For optimal results, the LMS adaptive array requires a reference signal which is a replica of the signal of interest.
Generation of the reference signal can pose a problem. In practice, a replica of the transmitted signal is not available at the receiver. The reference signal must be derived from the adaptive array output signal. Robert Riegler and Ralph Compton (Proceedings of the IEEE, Vol.61, No.6, June 1973, p.748) have discussed the application of the adaptive array to amplitude modulated communications signals, where the adaptive array output signal is processed to generate a representation of the carrier of the transmitted signal for use as the reference signal. But this approach addresses interference signals,
not the multipath problem.
R.T. Compton and D.M. DiCarlo (IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-14, NO.4, July 1978, p.599) and Y.Bar-Ness (IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-18, No.l, January 1982, p.115) analyze another adaptive array which uses the array output to generate the reference signal. But their system was designed to address a signal environment in which the signal of interest is received along with a wideband interference signal. They do not address the multipath problem. Ralph Compton (Proceedings of the IEEE, Vol. 66, No.3, March 1978, p.289) discusses an adaptive array for communication signals using a spread spectrum technique. The adaptive array uses knowledge of the spreading code to generate a reference signal. August McGuffin (U.S. Patent 4,217,586) has extended this approach by utilizing the multipath in the reference signal generation. The pseudo random (PN) code based reference signal generator can keep lock even in severe multipath fading. But both these approaches require that a known PN code be present in the transmitted signal to generate a reference signal.
G.H. Persinger (1977 International Conference on Communications, IEEE, Pt. Ill, Chicago, I11., 12-15 June, 1977, Pp. 259-262) has used a low level PN code placed in quadrature (90 degrees out of phase) with a transmitted AM signal. It is used to generate the reference signal at the receiver. The reference generation is dependent on the injection of this special signal with a known code. Peder Hansen (IEEE Transactions on Antennas and Propagation, Vol. AP-29, No.6 November 1981, p.836) has placed a special modulated pilot signal in the transmitted signal to be used to generate the reference signal. This technique was used specifically to discriminate against multipath. But it does not work without the special pilot signal. Gayle Martin (U.S. Patent 4,255,791) uses noise decorrelation to generate a reference signal for an adaptive array. This method addresses an environment where there is a large interfering signal, not the multipath environment.
In a related technology, transversal filters (single input adaptive filters) which reduce TV ghosts by signal processing (not by using the antenna pattern) use the known portions of the transmitted TV signal structure to generate the reference signal (Shri Goyal, others, IEEE Transactions on Consumer Electronics, Vol. CE-26, February 1980).
Transversal filters remove the ghosts after the received signal has been demodulated. But, they require a large number of loops, and they are generally microprocessor or computer based. Consequently, they are quite complicated and expensive. An alternative to deriving the reference signal, is the elimination of the reference signal altogether by changing the feedback equations. Work along this line has been performed by John Treichler in a related technology with a single input adaptive filter for constant modulus (amplitude) signals (John R. Treichler and Brian G. Agee, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-31, No.2, 1983, P.459; M.G. Larimore and J.R. Treichler, International Conference of Acoustics, Speech, and Signal Processing 1983, Boston, P.13). The Constant Modulus Algorithm (CMA) can be used to remove unwanted multipath for constant amplitude signals because it exploits the amplitude fluctuations induced by multipath. The CMA approach has limitations: 1) It only applies to wideband signals; it can not handle narrowband signals or an unmodulated carrier. 2) It requires a relatively large number of adaptive loops.
To summarize, the prior art is limited. It either does not address the multipath problem, it applies to a very limited range of signal classifications, its approach to the problem is complex, or it requires special tones or codes in the transmitted signal. And consequently, there is no effective and inexpensive method of removing multipath interference at the communication receiver.
SUMMARY OF INVENTION
The object of this invention is to reduce distortions such as fading, data overlap, multiple images, clicking, etc. caused by multipath in communication receivers. A second object of this invention is to reduce the negative effects of other types of noise and interference signals with amplitudes less than the amplitude of the desired signal by rejecting them also. The invention provides an inexpensive means of reducing the effects of multipath and interference by using adaptive techniques to reject the unwanted signals. The invention does this for a signal environment in which the TOA and the DOA of the desired signal and indirect path/interference signals are unknown and for which the transmitted desired signal contains no known codes, pilot signals, or signal waveform structures. This is
accomplished by changing the feedback equation for the LMS adaptive array/filter so that a reference signal is no longer required, and bygeneralizing the special case in which the reference signal is generated by amplitude limiting the adaptive array output signal.
DESCRIPTION OF FIGURES
LIST OF REFERENCES
Ref . Function Ref . Function
10 antenna element 60 analogy to digital converter
12 bandpass filter 62 computer/microprocessor/DSP
14 mixer 64 splitter/delayer
16 local oscillator 66 weighter
18 mixer 68 adder
20 tapped delay line 70 feedback function
20' 90 degree phase shift 72 multiplier
22 multiplier 74 integrator
24 integrator 76 biased envelope detector
26 weight 78 delay
26' weight 80 multiplier
26" weight 82 amplitude limiter
28 biased envelope detector 84 time shifter/delay
30 adder 86 phase shifter
32 amplitude limiter 88 divider
34 subtractor 90 DC source
36 weight adjustment 92 logarithm device
38 phase shifter 94 clamper
40 adder 96 digital filter
42 multiplier 98 subtractor
44 envelope detector 100 weight calculator
46 divider 102 delay
48 biaser 104 feedback function
50 multiplier
Figure 1 is a block diagram of a two element array for the suppression of multipath and interference: prior art.
Figure 2 is a block diagram of a two element adaptive array using an LMS analog implementation: prior art.
Figure 3 is a block diagram of an N element adaptive array with tapped delay lines having M output signals respectively: prior art.
Figure 4 is a block diagram showing the reference signal generator.
Figure 5 is a block diagram of the reference signal generator in an LMS configuration which contains N antenna elements each having a tapped delay line with M output signals.
Figure 6 is a graph of an example spectrum of the adaptive array output signal: prior art.
Figure 7 is a graph of the reference signal spectrum derived from the adaptive array output signal in Figure 6.
Figure 8 is a block diagram showing phase shifters.
Figure 9 is a block diagram of the invention where a subset of the weighted signals are summed for the generation of the reference signal.
Figure 10 is a block diagram of an N element CMA adaptive array with tapped delay lines having M output signals respectively.
Figure 11 is a block diagram of a phase shifter added to the N element CMA adaptive array and CMA filter in Figure 10.
Figure 12 is a block diagram of a first implementation of the feedback function for p=1 and q=2. Figure 13 is a block diagram of a second implementation of the feedback function for p=1 and q=2.
Figure 14 is a block diagram of a first implementation of the feedback function for p=2 and q=2.
Figure 15 is a block diagram of a second implementation of the feedback function for p=2 and q=2.
Figure 16 is a block diagram of a first implementation of the approximate feedback function for p=1 and q=2.
Figure 17 is a block diagram of a second implementation of the approximate feedback function implementation for p=1 and q=2. Figure 18 is a block diagram of the implementation of the feedback function for p=3 and q=1 when the range of β is restricted.
Figure 19 is a block diagram of the implementation of the feedback function for q=1 and q=1 when the range of β is restricted.
Figure 20 is a block diagram of the implementation of the feedback function for p=2 and q=1 when the range of P is restricted.
Figure 21 is a block diagram of an implementation of the logarithmic feedback function. Figure 22 is a block diagram of a computer/microprocessor/DSP implementation of the invention.
Figure 23 is a flow chart of a software LMS adaptive array implementation of the invention.
Figure 24 is a flow chart of a software CMA adaptive array and filter implementation of the invention.
Figure 25 flow chart of the approximate feedback function for a software implementation for q=1 and q=2.
Figure 26 is a flow chart of the feedback function for the software implementation for q=1 and q=2.
DETAILED DESCRIPTION
Before describing the preferred embodiment of the invention in detail, a discussion of multipath theory, adaptive arrays, CMA filter, and the new feedback equation theory will be presented to facilitate understanding.
NATURE OF MULTIPATH
In a multipath environment the transmitted signal arrives at the receiver simultaneously from more than one direction, where there is a direct path and one or more indirect paths. The indirect paths are longer than the direct path, so the signals traveling these paths arrive at the receiver at a later time than the direct path signal. It is this difference in the time of arrival that causes distortion in both the amplitude and the phase of the received signal. For example, consider angle modulation (FM, PM, etc.); the direct path signal, in real notation, is
X1(t)=B1 sin[w(t-R1/c)+αf(t-R1/c)3+n1(t) (1)
where w is the angular frequency, t is the time, f(t) is the modulation, B1 is a constant amplitude, R1 Is the path length, c is the speed of light, α is the phase deviation, and n1(t) is a random noise term. The indirect path signal has the form
Xi(t) =Bi sin[w(t-R1/c )+ αf ( t-Ri/c ) ]+ni(t) (2)
where the Xi(t) indicates the "i " th path signal, Bi is a constant signal amplitude for the "i" th path, Ri is the distance traveled by the "i" th path signal, and ni(t) is a random noise term. The ni(t) and n1(t) are all independent. The Xi(t) s are all delayed versions of the direct path signal. The total signal present at a given point in space is the sum of the direct and indirect path signals. Using equations (1) and (2), the total received signal can be written as
(3)
In equation (3), for convenience, the term X1(t) has subscript one and refers to the direct path signal, the Xi(t) in the summation, where i=2 to i=N, refers to the indirect paths signals (or the interference signals). Summing over sinusoids, and for convenience, assume that the noise terms are small and can be neglected, equation (3) can be written as
E(t)=A(t) sin[wt+a(t) ] (4)
where
and
Pi=-(wRi/c ) +αf ( t-Ri/c ) .
It should be noted that if equation (4) represents the net signal present at an antenna array phase center, it can be immediately seen that the net signal received at each antenna element is different because the distance traveled, Ri, for the received signals is different for each antenna element.
ADAPTIVE ARRAY Multipath occurs when the transmitted signal of interest arrives at the receiver simultaneously from more than one direction. Define an interference source as a signal source unrelated to the specific communication system, such as a signal from another transmitter, that may or may not have the same frequency as the signal of interest. Historically, adaptive arrays were developed to reject interference signals. More recently, adaptive arrays have been shown capable of rejecting multipath. An adaptive array is an antenna array that has adjustable weights in each of the antenna elements which automatically adjusts the weights so that the multipath or interference signals are rejected.
To demonstrate the way in which an array with adjustable weights can reject an indirect multipath signal or an interference signal, consider the two element array in Figure 1. Let antenna elements 10 be omni-directional and let the spacing between them be a half-wave length of the desired signal.
The signal of interest, P(t), arrives from the normal direction, 0 degrees, and the multipath or interference signal, I(t), arrives from 30 degrees displaced from the desired signal. To simplify the calculation, let both P(t) and I(t) have zero phase at the array phase center, PC, which is located midway between the antenna elements. The output signal of each antenna element 10 goes to a variable complex weight 26", where W1+jW2 and W3+jW4 correspond to elements E1 and E2 respectively. The output signals of the complex weights are summed in adder 30, the output of which is the array output signal. The signal of interest, in complex notation, is
P(t)=P0exp(jwt), (5)
where P0 is the signal amplitude, t is time, and w is the signal angular frequency. The array output signal due to the signal of interest is
SI(t)=P0{(W1+W3)+j(W2+W4)}exp(jwt). (6)
The desired array output signal is an unaltered copy of the signal of interest. By equating equations (5) and (6), and collecting the real
and imaginary terms, the required weight relationships to get the desired output signal are
W1+W3=1 (7) and
W2+W4=0. (8)
The unwanted indirect path signal is
I(t)=I0exp(jw't) (9)
where I0 is the signal amplitude and w' is the angular frequency of the unwanted signal. When the unwanted signal is multipath, w'=w. The distance traveled by the received signal is different for each antenna element. I(t), which is incidenting the antenna array from an angle of 30 degrees, will arrive at antenna element E2 with a phase lead relative to the antenna array phase center of
(10)
radians and, similarly, it will arrive at antenna element E1 with a phase lag of radians. Therefore, the array output signal due
to I(t) is
SM(t)=I0 [W1+jW2]exp[j(w't-π/4)]
+[W3+jW4]exp[j(w't+π/4)] . (11)
Since it is desired to reject the unwanted multipath signal, equation (11) must equal zero. By using the relationships
(12)
and (13)
and collecting the real and imaginary terms, equation (11) gives
W1+W2+W3-W4=0 (14)
and
-W1+W2+W3+W4=0. (15)
The weights must satisfy equations (14) and (15) to reject the multipath signal.
Equations (7), (8), (14), and (15) give 4 equations and 4 unknowns. Solving for the weights gives
W1=.5, W2=-.5, W3=.5, W4=.5. (16)
With these weight values the antenna array will accept the signal of interest, P(t), and reject the unwanted signal, I(t). The array is functioning as a spatial filter. In an adaptive array the weights are changed automatically to the correct values that reject the unwanted multipath/interference signals and accept the signal of interest. As the signal environment changes, the weights adapt to continue rejecting the multipath/interference. To be an adaptive array, the simple array in Figure 1 requires a means for automatically changing the weights.
There are a number of approaches for changing the array weights automatically. Many examples of adaptive arrays can be found in: Robert A. Monzingo and Thomas W. Miller, Introduction to Adaptive Arrays, John Wiley & Sons, New York, 1980; Bernard Widrow and Samuel D. Stearns, Adaptive Signal Processing, Prentice-Hall, 1985; and C.F.N. Cowan and P.M. Grant Eds., Adaptive Filters, Prentice-Hall, Inc., 1985. The Least Means Square (LMS) adaptive array, which requires a reference signal, is the best known and the best understood approach to automatically adjust the weights. It is also the simplest to implement.
In the LMS adaptive array, the difference between the array output signal and the reference signal is called the error signal, , and is used as a measure of merit in a least means squares sense to adapt the weights by minimizing ∈ 2. The basic theory and technology for the LMS adaptive array has been presented by Bernard Widrow, Proceedings of the IEEE, Vol.55, No.12, December 1967, p.2143 and by Ralph Compton, Proceedings of the IEEE, Vol.61, No.6, June 1973, P.748. The books cited in the previous paragraph also present much theory about the LMS
adaptive array.
Figure 2 shows a two element adaptive array using an LMS implementation. After the received signals, which include the signal of interest and multipath/interference, enter the antenna elements 10, each element splits the signal into two components; one component is phase shifted 90 degrees by 20', and the other component's phase is unshifted. Each signal then goes to its respective amplitude weight 26, which are W1, W2, W3, and W4 respectively. Because the signals going to each of the respective antenna element weight pairs are 90 degrees out of phase, they adjust the signal in the element in both amplitude and phase. For element E1, the amplitude weighting is
(17a)
and the phase shift weighting is
ά (17b)
Element E2 has a similar result for weights W3 and W4. The weighted signals from weights W1, W2, W3, and W4 go to adder 30 where they are summed. The output signal of the adder 30 is the adaptive array output signal and it goes to subtractor 34. The second input signal to subtractor 34 is the reference signal, which, ideally, is a replica of the desired signal. The array output signal is subtracted from the reference signal by subtractor 34. It is this resulting difference ∈ between the array output signal and the reference signal that is used in the LMS adaptive arrays to automatically adjust the weights. It can be shown that
(18a)
where W^ is the "i" th weight, k is a constant, ∇wi(< ∈ 2 > ) is the component of the gradient of < ∈2> with respect to Wi and < > denotes the time average of the function contained therein. In the case shown in Figure 2, N=4. This gives for the value of the "i" th weight,
Wi=W0i-2k ∫ <∈Xi>dt i=1,...,N (18b)
where W0i is the value of the "i" th weight at time zero, and Xi is the input signal to the "i" th weight. Equations (18b) are the feedback equations for the weights in the analog implementation. The error signal 6 from subtractor 34 and the weight input signals X1, X2, X3, X4 are multiplied by multipliers 22 respectively. The output signals from multipliers 22 go to integrators 24 respectively. The output signals of each of the integrators 24 is applied to its associated weight circuit 26, where that signal is weighted. The output signal from each weight circuit is then applied to adder 30 where they are summed. Each set of multiplier, integrator, weight circuit and input signal together with the error signal, subtractor, and adder constitute an adaptive loop.
The equivalent feedback equations for a discrete/digital implementation of the LMS adaptive array is
Wi(j+1)=Wi(j)-2k∇Wi(<∈(j)2>) i=1,...N (19a)
and
Wi(j+1)=Wi(j)-2k∈(j)Xi(j) 1=1,...,N (19b)
where the antenna element input signals are discrete time samples with Xi (j) being the "i" th weight input signal at the "j" th time sample, ∈ (j) is the error signal at the "j" th time sample, Wi(j) is the "i" th weight at the "j" th time sample, and Wi(j+1) is the weight value update at the "j+1" time sample for the "i" th weight.
The adaptive array is not restricted to two antenna elements and a 90 degree phase delay. It can have many antenna elements. And it can have many time (phase) delays in each antenna element.
REFERENCE
A class of adaptive arrays of interest in this invention use a reference signal which, ideally, is a replica of the desired signal to adjust the system weights. The difficult task is obtaining the reference signal. The unique aspect of this invention that makes it different from all the prior art is the way in which the reference
signal problem is solved and its application to removing unwanted multipath and interference signals when the DOA and TOA of the signals are unknown. By generating the required reference signal through the amplitude limiting of the adaptive array output signal, this invention can reduce the negative effects of multipath and interference signals by rejecting the unwanted signals, as will be shown below. In contrast to the prior art, it does this without using special codes, tones, or waveform structures in the transmitted signal of interest. As is shown below, this is possible because of the unique characteristics resulting from the reference signal being obtained by amplitude limiting the adaptive array output signal.
The array output signal is the weighted sum of the antenna elements input signals. Each antenna element will have an input signal similar to equations (3) and (4), being made up of a direct path and indirect path signals. Using Figure 3, let
En(t)=An(t) sin[wt+an(t)] (20)
be the equation of the resulting input signal for the sum of all the signals arriving at the "n" th antenna element 10, where An(t) is the signal amplitude and an(t) is the signal phase. Each antenna element 10 goes to an M output signal delay line 20 which forms M adaptive loops. The equation of the "m"th delay line output signal for the "n"th antenna element is
En(t-dm)=An(t-dm) sin[w(t-dm)+an(t-dm)], (21)
where dm is the time delay of the delay line's "m" th output signal. Since there are N antenna elements 10 and M output signals for each tapped delay line 20, there is a total of NxM weights 26'. The adder 30 sums the weighted tapped delay line 20 output signals. The output signal of adder 30 is the array output signal and is given by
where the nm subscript of Wnm refers to the weight 26' of the "m" th delay line 20 output signal of the "n" th antenna element 10. Summing over sinusoids, equation (22) can be written as
Sar(t)=G(t)sin[wt+q(t)] (23)
where
and
Znm-dm+an(t-dm)'
Figure 4 shows the reference signal generated from the array output signal of the adaptive array in Figure 3. The array output signal represented by equation (23) goes to the amplitude limiter 32 to generate the reference signal which is represented mathematically as
R(t)=F sin[wt+q(t)] (24)
where F is a constant signal amplitude determined by the limiter. The amplitude limiter 32 keeps its output signal amplitude at a constant value even when the amplitude of the input signal changes. The amplitude limiter 32 output signal goes to the weight adjustment 36 where it is used to compute values to change the weights.
In Figure 5, the LMS adaptive array implementation is used to compute the weight values. The amplitude limiter 32 output signal, which is the reference signal, goes to the subtractor 34 which subtracts the adaptive array output signal from it. The resulting error signal goes to the adaptive loop multipliers 22. The multipliers 22 output signals are each integrated by integrators 24 respectively, the output signals of which adjust the values of the respective weights 26. As described In the references given above, the adaptive array will adjust the weights to make the array output signal match the reference signal in amplitude and phase by minimizing the means square of the difference between the reference signal and the array output
signal. It does this by correlating the signal of interest with the reference signal, as described below, and tries to null out the unwanted signals.
Each antenna element signal, as expressed by equation (20), is made up of the sum of the received input signals, and consequently, the received input signal which is most significant is the one with the largest amplitude. Since each antenna element signal, and therefore each weighted antenna element signal, contain time shifted versions of the same received input signals, they all are dominated by the same signal. The array output signal, as expressed by equation (22), is the sum of the weighted antenna element signals, and therefore, it is also most influenced by the largest received input signal. And, consequently, both the amplitude G(t) and phase q(t) of equation (23) are also most influenced by the largest input signal. And the reference signal represented by equation (24) is also most influenced by the largest received input signal via q(t).
The adaptive array tries to receive the input signal which is most correlated with the reference signal and reject the other input signals. Since the reference signal is most influenced by the largest input signal, it is most correlated to the largest input signal. So the adaptive array uses the largest input signal as the signal of interest while rejecting the other input signals by placing antenna pattern nulls in their DOA.
A direct path signal between a communications transmitter and receiver may not always exist due to buildings, hills, etc. blocking the path. In that event this invention will take the received signal with the largest amplitude as the signal of interest. It will reduce all the other multipath/interference signals just as in the direct path signal environment case. More insight into the reference signal can be gained by comparing its spectrum to that of the array output signal. In the array output signal equation (23), the sinusoid factor can be expanded into its spectral components (Fourier expansion) and has the form
(25)
where Bn and ɤn are the Fourier coefficient and phase respectively. The amplitude factor G(t) multiplies each spectral
component which it essentially modulates and splits. A simple example of a possible adaptive array output signal spectrum is shown in Figure 6. By amplitude limiting equation (23) to generate the ref eren ce signal, as represented by equation (24), G(t) is removed and replaced by the constant amplitude F. This changes the signal amplitude and removes the spectral splitting which makes the referenc e signal spectrum different from that of the adaptive array output signal spectrum. Figure 7 shows the spectrum of the reference signal obtained from the array output signal. The adaptive array changes the weights in such a way that the amplitudes and spectra of the reference signal and the adaptive array output signal are the same. It does this by placing antenna pattern, nulls in the DOA of the unwanted signals. This in turn reduces the distortions caused b y the unwanted sig nals . Ideally, when the adaptive array has fully adapted, the array output signal is the same as the sig nal of interest.
As given in the references cited above for the traditional LMS adaptive array theory, the error signal ∈ , which is the difference between the reference signal and the array output signal, is used to adjust the weight values. Let R(t) be a reference signal that is a replica of the desired signal and X i (t) be the input signal to "i" th weight W i . Th e error signal is rep res ented by
( 26)
Squaring the error in equation (26) and then performing a time average gives the mean s quare error,
( 27)
where < > is the time average. Equation (27) is qu ad ratic in the weights Wi in an "L" dimensional space (N antenna elements times M delay line output signals). Consequently, it has a single extremum which is a minimum. At this minimum the error b etween the reference signal and the array output signal is a minimum (in the mean least squares sense). The minimum occurs when antenna pattern nulls are placed in the DOA of the unwanted signals. Applying the method of
steepest decent to equation (27) to obtain the minimum yields the feedback equation already presented in equation (18b).
To derive the traditional reference LMS adaptive array feedback equation for an unmodulated carrier, let the "i" th weight input signal be
(28)
where Ai and
are constants. The reference signal provided to the adaptive array is the sine wave
R(t) = F sin(wt+δ) (29)
where F is a constant chosen to have the same value as the amplitude of the reference signal generated in this invention (as represented in equation (24)). And δ is an arbitrary constant. The error signal is
∈ = F sin(wt+δ )-Sar. (30)
Using equation (18b), the feedback equation for the "i" th weight of the traditional reference LMS adaptive array is
(31).
Using equation (28) and the relationships
<cos2(wt)>=<sin2(wt)>=1/2 (32)
and
<sin(wt)cos(wt)>=0 (33)
gives
(34)
It is shown below that this invention in an LMS implementation reduces to the traditional reference LMS adaptive array and equation (34) at
equilibrium.
In this invention the reference signal is obtained by amplitude limiting the array output signal. In this case the error signal becomes, using equations (23) and (24),
∈ = F sin[wt + q(t)]-Sar (35)
where Sar is the array output signal. Substituting equation (35) into equation (18b), the feedback equation for each weight is
(36)
where Xi is the antenna element input signal for the "i" th weight. Substituting equation (28) into equation (36) and using equations (32) and (33) gives
(37)
The difference between equation (37) and the weight value for the traditional reference LMS adaptive array in equation (34) is the phase of the first term under the integral,
Δ-q(t)-δ (38)
The traditional reference LMS adaptive array aligns the array output signal phase with the reference signal phase δ . The actual value of the reference signal phase constant in terms of the array performance is arbitrary. But in equation (37), the corresponding feedback equation for this invention, the value of q(t) depends on the weight values. As the weights change, q(t) changes. But, experimentally, this invention goes to an equilibrium condition where the weights are constant. This means q(t) is constant. Since the phase constant a of the traditional reference LMS adaptive array is an arbitrary constant, equation (34) is functionally the same as equation (37) when q(t) is constant. This implies that this invention operates in a manner similar to the traditional reference LMS adaptive array and that the traditional reference LMS adaptive array results can be used to predict the behavior of this invention. More specifically, since the
traditional reference LMS adaptive array rejects unwanted multipath signals by placing an antenna pattern null in their DOA, this invention rejects unwanted multipath signals by placing an antenna pattern null in their DOA. As a further example, consider a multipath environment for a signal of interest that is angle modulated (FM, PM, etc.). The external reference signal for the traditional LMS adaptive array has the form
(39)
where F, ύ)0, andΛare constants, and f(t) is the modulation.
The input signal to the "nm" th weight is given by equation (21).
The array output signal is given by equations (22) and (23).
Substituting equations (21) and (39) into equation (18b) and using the relationships in equations (32) and (33) gives as the feedback equation for the "nm"th weight for the traditional LMS adaptive array
The reference signal for this invention is given by equation (24). Using equations (18b), (21), (24), (32), and (33) gives as the "nm" th weight feedback equation for this invention
(41)
For a signal with a narrow enough bandwidth, A
n ( ) 0
n where
< ~and A0 are constants and $ Then equation (40) becomes for
narrowband signals
(42)
and equation (41) becomes * (43)
At equilibrium is a constant, and consequently,
equation (43), the feedback equation for this invention in an LMS implementation, Is functionally the same as equation (42), the feedback equation for the traditional LMS adaptive array. This is the same as the case presented above for the unmodulated carrier.
It can be seen for the wider bandwidth angle modulated signals that placing an antenna pattern null in the direction of arrival of the unwanted multipath signals causes the feedback equations of this invention to approach the traditional LMS adaptive array feedback equations. The array output signal for this invention after adapting is
Sar^B0sin(wt-αf(t)+δ.') (44)
where B0, and δ.' are constants. The reference signal becomes
R(t) = F sin(wt-αf(t) +δ.') (45)
Substituting equations (44) and (21) into equation (18b) gives
(46)
Comparing equation (46) to equation (40) shows that they are functionally the same. The traditional LMS adaptive array is a good model for performance of this invention in an LMS implementation for angle modulated signals and the unmodulated carrier in a multipath environment. This relationship between the traditional LMS adaptive array and the LMS implementation of the present invention can be extended to other modulation types, such as AM, with a similar approach.
Figure 9 shows another implementation of the invention. By comparing Figure 9 to Figures 3 and 4 it can be seen that the first adder 30 in Figure 9 corresponds to adder 30 in Figures 3 and 4. But in Figure 9 selected weight signals are also applied to the second adder 40. The adaptive array output signal is obtained, as in Figures 3 and 4, from the first adder 30. However, the input signal to the amplitude limiter 32 in Figure 9 is obtained from second adder 40. The output signal from the the second adder 40 also is applied to weight
adjustment 36. So in Figure 9 the input signal to the weight adjustment means and the amplitude limiter is obtained from the sum of a subset of the weight signals. The weight adjustment 36 output signal is applied to all the amplitude weights in the array just as in Figures 3 and 4.
The phase of the reference signal can be an important factor for system stability. In a hardware analog implementation, the amplitude limiter 32 can shift the reference signal phase excessively when the array output level changes too much. The greater the phase shift of the limiter due to the change of the amplitude of the array output signal, the smaller the dynamic range the adaptive array system can have. Ideally, to maximize the system dynamic range, the limiter should show no phase shift with a change in the array output signal level. The Avantek, Inc. UTL-1002 is a good example of a limiter that has a small phase shift with a change in the input signal amplitude.
The ideal reference signal generator will have a phase shift that is zero or an integral multiple of the desired signal wave length. Then the reference signal phase will be the same as the array output signal. It is also convenient in an analog hardware implementation to have a phase shift device 38 in the reference generator signal loop, the error signal path and the subtractor branch of the adaptive array output signal respectively. Figure 8 shows the phase shift devices 38 for the adaptive array in Figure 5. The adaptive array output signal comes from adder 30 and goes to the first phase shift device 38 and amplitude limiter 32. The output signal from the first phase shift device 38 goes to subtractor 34. The output signal from amplitude limiter 32 goes to the second phase shift device 38. The output signal from the second phase shift device 38 is the second input signal to subtractor 34. The output signal from subtractor 34, which is the error signal, goes to a third phase shift device 38. The third phase shift device 38 output signal goes the the multipliers 22. This permits optimal alignment of the system phase constants to maximize performance.
CMA ADAPTIVE ARRAYS The LMS adaptive array minimizes the mean square error between the array output signal and a reference signal. The CMA filter developed by Treichler minimizes a positive definite measure of the signal
modulus variation given by < (47)
where "p" and "q" are constants, β is a positive constant, and Y(t) is the adaptive filter output signal at time t. The feedback equation for the "i" th weight is
. (48)
where k and WOi are constants and ∇Wi[Jpq(t)] is the component of the gradient of Jpq(t) with respect to Wi. It can be shown that
where X-^(t) is the input signal to the "i" th weight and
The feedback equations can be rewritten in the form
(50)
where ∈ is determined from equations (48) and (49).
Table I shows ∈ for different values of p and q. Equations for values of "p" and "q" other than those shown in Table I have similar but more complicated form.
Feedback equation (52) is mathematically the same, within a sign and scale factor, as the equation (35) obtained for. the error signal in an LMS adaptive array that generates its reference signal by amplitude limiting the adaptive array output signal.
The adaptive array implementation of equation (52) results in a means for removing multipath that is very different from the CMA filter implementation:
1) The CMA filter exploits the fact that for a constant modulus signal, multipath causes the amplitude to fluctuate
TABLE I p,q Eq-#
1,1 [Y(t)/|Y(t)|]sgn[|Y(t)|- β] (51) 1,2 2[Y(t)/|Y(t)|][|Y(t)|- β] (52)
2,1 2Y(t)sgn[|Y(t)|2-β2] (53)
2,2 4Y(t)[|Y(t)|2- β2] (54)
1,3 3[Y(t)/|Y(t)|][|Y(t)|-β]2sgn[|Y(t)|-β] (55) 1,4 4[Y(t)/|Y(t)|][|Y(t)|- β]3 (56)
3,1 3Y(t)|Y(t)|sgn[|Y(t)|3- B3] (57)
3,2 6Y(t)|Y(t)|[|Y(t)|3- β3] (58)
significantly when the signal has a wide bandwidth. The LMS adaptive array is a spatial filter that also exploits the different directions of arrival the multipath signals.
2) The CMA filter uses scaled, time shifted versions of the received input signal to remove unwanted multipath. The LMS adaptive array approach removes the unwanted multipath signals by placing an antenna pattern null in their direction of arrival.
3) The CMA filter applies to wideband signals only. The LMS adaptive array approach applies to unmodulated carriers, narrowband signals, and wideband signals.
4) The CMA filter requires a large number of adaptive loops. The LMS adaptive array approach can use as few as four linear adaptive loops (two antenna elements, each having two linear adaptive loops).
5) The CMA filter applies to a single signal input adaptive filter. The LMS adaptive array approach applies to multiple signal inputs from an antenna array.
6) The CMA filter does not use a reference signal. The LMS adaptive array uses a reference signal that is generated by amplitude limiting the adaptive array output signal.
7) The CMA filter was derived by a whole new theory. The LMS adaptive array uses the traditional LMS theory.
8) The CMA filter applies primarily to signals of constant modulus. The LMS adaptive array approach does not have this limitation. Since the CMA feedback equation (52) occurred in the LMS adaptive array which removes unwanted multipath, it implies that the other CMA filter feedback equations obtained from equations (48) and (49) can also be used in an adaptive array to remove unwanted multipath. And it is the application of these feedback equations to an adaptive array that makes the invention different from the prior art. Just like the LMS adaptive array implementation of equation (52), the adaptive array implementation of the CMA feedback equations places an antenna pattern null in the direction of arrival of unwanted multipath/interference signals; these new CMA adaptive arrays work for broadband signals, narrowband signals and unmodulated carriers; they also work for signals without constant modulus such as AM signals. In addition, they require as few as four linear adaptive loops (two antenna elements and two linear adaptive loops for each antenna element).
By comparing equation (50) to the LMS feedback equation, equation (18b), it is seen that the CMA and LMS feedback equations have the same form. Only the definition of ∈ is different. ∈ for the LMS adaptive array is given by the difference between the adaptive array output signal and the reference signal. ∈ for the CMA feedback equations is derived from equations (48), (49), and (50). This means that the form of the weight adjustment is the same for the CMA adaptive array and LMS adaptive array except for the computing of ∈ .
Figure 10 shows the generalized adaptive array implementation based on the CMA feedback equation (50) and the LMS feedback equation (18b). The input signals to the adaptive array are developed by appropriate input devices such as antenna elements 10, bandpass filters 12, and mixers 18. The second input signal to the mixers 18 is generated by a single local oscillator 16. Thus, mixers 18 convert the input signals to phase coherent signals at an appropriate IF frequency. The respective mixer 18 output signals go to a corresponding tapped delay line 20 which has M output terminals. Each tapped delay line 20 output signal goes to a corresponding amplitude weight circuit 26 and multiplier 22.
The output signal from each amplitude weight circuit 26 is applied
to adder 30 where they are summed. The adder 30 output signal is the adaptive array output signal which goes to the appropriate next stage of signal processing, such as an IF amplifier, a demodulator, etc. The output signal of adder 30 is also applied to feedback function circuit (FF) 104 which computes the feedback signal in equation (50). The form of FF 104 depends on the specific equation for being used and its particular implementation. Table I shows the equations of for some values of p and q. Specific implementations of FF 104 are presented below. The output signal from FF 104 is applied to each multiplier 22. Each multiplier 22 multiplies the feedback signal from FF 104 with the corresponding output signal from tapped delay line 20. The output signal from each of multipliers 22 is applied to a corresponding integrator 24. The output signal of integrator 24 is applied to a corresponding amplitude weight circuit 26, which, accordingly, adjusts the weight values applied to the output signal of its corresponding tapped delay line 20. This weight adjustment process continues until the weights reach equilibrium values. The system adjusts the weights so that the unwanted multipath/interference signals are rejected, resulting in less distortion of the signal of interest at the adaptive array output signal.
Figure 11 shows a phase shifter 86 placed between FF 104 and multipliers 22 in Figure 10. The output signal of FF 104 is applied to phase shifter 86. The output signal of phase shifter 86 is then applied to multipliers 22. The system functions in the same manner as the system in Figure 10 except that the phase shifter 86 can shift the phase of the feedback signal to optimize the stability of the system if necessary, because as the phase is removed from its optimum value, the system can exhibit a drift.
It will be seen from the material presented below that although there are many possible values of p and q and that each pair of p and q values can have many implementations, there are only a handful of fundamental implementations. The other implementations are more elaborate versions of these fundamental implementations.
As discussed above, equation (52) can be implemented as an LMS adaptive array with the reference signal being generated by amplitude limiting the adaptive array output signal. This is an implementation of equation (52) where the equation is separated into to the two terms:
Y(t), the adaptive array output signal, and β[Y(t)/|Y(t)|], the generated reference signal.
Equation (52) can also be separated into the two factors 2[Y(t)/|Y(t)|} and {|Y(t)|- β}. Figure 12 and Figure 13 show two FF 104 implementations of this form. In Figure 12 the output signal of adder 30 is applied to a biased envelope detector 28 and an amplitude limiter 32. The amplitude limiter 32 amplitude limits the adder 30 output signal. The amplitude limiter 32 output signal goes to multiplier 42. The biased envelope detector 40 detects the envelope of the output signal of adder 30 and biases it a constant negative value. The output signal of biased envelope detector 28 is the second input signal to multiplier 42. The multiplier 42 multiplies the output signal of amplitude limiter 32 and the output signal of biased envelope detector 28. The output signal of multiplier 42 is the feedback signal ∈ and is applied to the multipliers 22 in Figure 10 (or phase shifter 86 in Figure 11). This form of the adaptive array is one embodiment of the present invention.
The second implementation is given in Figure 13. The output signal from adder 30 in Figure 10 is applied to the envelope detector 44 and the divider 46. The envelope detector 44 detects the envelope of the output signal of adder 30. The output signal of envelope detector 44 is applied to the biaser 48 and the divider 46. Biaser 48 shifts the output signal of envelope detector 44 a constant negative amount. The output signal of biaser 48 is one of the input signals to multiplier 42. Divider 46 divides the output signal from adder 30 by the output signal from envelope detector 44. The output signal of divider 46 is applied to multiplier 42. Multiplier 42 multiplies the output signals from divider 46 and biaser 48. The output signal of multiplier 42 is the feedback signal and is applied to multipliers 22 in Figure 10 (or phase shifter 86 in Figure 11). This form of the adaptive array is another embodiment of the present invention.
Equation (51) can be implemented by extending the implementations of equation (52). A sgn means, such as a comparator referenced to zero volts, can be added to the implementation in Figure 12 by having the the output signal of biased envelope detector 28 applied to the sgn means; and the output signal of sgn means is applied to multiplier 42. Similarly, equation (51) can be implemented through the addition of a sgn means to the implementation of equation (52) in Figure 13 by having
the output signal of biaser 48 applied to the sgn means, and the output signal of sgn means is applied to multiplier 42.
Equation (54) can be separated into the two factors Y(t) and [|Y(t)|2- β2] . Figure 14 shows an implementation of this form. The output signal of adder 30 in Figure 10 is applied to multiplier 42 and to envelope detector 44. Envelope detector 44 detects the amplitude envelope of the output signal of adder 30. The output signal of envelope detector 44 is applied to both input terminals of multiplier 50. Multiplier 50, so connected, squares the output signal of envelope detector 44. The output signal of multiplier 50 is applied to biaser 48. Biaser 48 shifts the output signal of multiplier 50 a constant negative amount. The output signal of biaser 48 is the second input signal of multiplier 42. Multiplier 42 multiplies the output signals of biaser 48 and adder 30. The output signals of multiplier 42 is the feedback signal ∈ and is applied to multipliers 22 in Figure 10 (or phase shifter 86 in Figure 11). This form of the adaptive array is another embodiment of the. present invention.
Equation (54) can also be separated into the two terms, -4p Y(t) and 4|Y(t)|2Y(t). Figure 15 shows an implementation where β has been chosen to be equal to 1. The adaptive array output signal from adder 30 is applied to envelope detector 44, multiplier 42 and subtractor 34. Envelope detector 44 detects the envelope of the output signal of adder 30. The output signal of envelope detector 44 is then applied to both input terminals of multiplier 50, which, so connected, squares the output signal of envelope detector 44. The output signal of multiplier 50 is also applied to multiplier 42. Multiplier 42 multiplies the output signal of multiplier 50 and the output signal of adder 30. The output signals of multiplier 42 is applied to subtractor 34. Subtractor 34 subtracts output signal of adder 30 from output signal of multiplier 42. The output signal of subtractor 34 is the error signal ∈ and it is applied to the multipliers 22 in Figure 10 (or phase shifter 86 in Figure 11). This form of the adaptive array is yet another embodiment of the present invention.
Equation (53) can be implemented by extending the implementation in Figure 14 by placing a sgn means, such as a comparator referenced to zero volts, between the biaser 48 and the multiplier 42. The output signal of biaser 48 is applied to the sgn means, and the output signal of sgn means is in turn applied to multiplier 42.
The feedback equations corresponding to the other values of p and q are implemented by adding more multipliers, biasers, etc., to the FF 104 circuits already presented. They are extensions of the forms presented above. The significant differences between these CMA adaptive array inventions and the CMA filters are:
1) The CMA filter exploits the fact that for a constant modulus signal multipath causes the amplitude to fluctuate significantly when the signal has a wide bandwidth. The CMA adaptive array is a spatial filter that also exploits the different directions of arrival of the multipath signals.
2) The CMA filter uses a scaled, time shifted version of the received input signal to remove unwanted multipath. The CMA adaptive array approach removes the unwanted multipath signals by placing an antenna pattern null in their direction of arrival.
3) The CMA filter applies only to wideband signals. The CMA' adaptive array approach applies to unmodulated carriers, narrowband signals, and wideband signals.
4) The CMA filter requires a large number of adaptive loops. The CMA adaptive array approach can use as few as four linear adaptive loops (two antenna elements, each having two linear weights). 5) The CMA filter applies to single signal input. The CMA adaptive array approach applies to multiple signal inputs for an antenna array. 6) The CMA filter applies primarily to signals of constant modulus. The CMA adaptive array approach does not have this limitation.
APPROXIMATE FEEDBACK EQUATION Using equations (50) and (52), the feedback equation corresponding to equation (52) is
Let equation (59) be approximated by
(60)
where the factor 1/|Y(t)| is moved outside the integral. Feedback equation (60) can be used to derive a new form for the adaptive array. The feedback signal for equation (60) is
∈ =2Y(t)[|Y(t)|- β ]. (61)
Equation (61) can be separated into two factors: 2Y(t) and [ Y(t)- β ] . Figure 16 shows an implementation of FF 104 for this form. The output signal from adder 30 is applied to both the baised envelope detector 28 and multiplier 42. The biased envelope detector 28 detects the amplitude envelope of the output signal of adder 30 and shifts it a constant negative amount. The output signal of biased envelope detector 28 is applied to the multiplier 42. Multiplier 42 multiplies the output signal of biased envelope detector 28 and output signal of adder 30. The output signal of multiplier 42 is the feedback signal and is applied to multiplier 22 in Figure 10 (or phase shifter 86 in Figure 11). This form of the adaptive array is still another embodiment of the present invention.
Equation (61) can also be separated into two terms: 2|Y(t)|Y(t) and -2 β Y(t). Figure 17 shows an implementation of this two term separation for β =1. The output signal of adder 30 in Figure 10 is applied to envelope detector 44, multiplier 42, and subtractor 34. Envelope detector 44 detects the amplitude envelope of the output signal of adder 30. The output signal of envelope detector 44 is applied to multiplier 42. Multiplier 42 multiplies the output signal of envelope detector 44 and the output signal of adder 30. The output signal of multiplier 42 is applied to subtractor 34. Subtractor 34 subtracts the output signal of adder 30 from the output signal of multiplier 42. The output signal subtractor 34 is the feedback signal, ∈ , and is applied to multipliers 22 in Figure 10 (or phase shifter 86 in Figure 11). This form of the adaptive array is a further embodiment of the present invention. When the same approximation is applied to equation (51), the feedback equation becomes
∈ =2Y(t)sgn[|Y(t)|-β]. (62)
Equation (62) can be implemented with an extension of the implementation in Figure 16 through the addition of a sgn means, such as a comparator referenced to zero volts, between the biased envelope detector 28 and multiplier 42. The output signal of biased envelope detector 28 is applied to the sgn means; the output signal of the sgn means is applied to multiplier 42.
The other feedback equations for the different values of p and q with |Y(t)| as a factor can also be approximated by moving the |Y(t)| factor out of the integral in a similar manner.
RESTRICTED BIAS FEEDBACK EQUATION The equations for ∈ which contain the "sgn" function permit further simplification to their implementation. Consider the case when β is selected to be less than |Y(t)|. Since β >0 and |Y(t)|>0,
|Y(t)|-β> 0 (63)
|Y(t)|2-β2 > 0 (64)
| Y(t)|3-β3 > 0.
Using equations (63), (64), and (65) and using the fact that sgn(x)=1 when x>0, equations (51), (53), (55), and (57) become respectively,
∈ =Y(t)/|Y(t)| (66)
∈ =2Y(t) (67)
∈=3[Y(t)/|Y(t)|][|Y(t)|-β]2 (68)
∈=3Y(t)|Y(t)| (69)
Implementation of equation (68) results in a more complex version of the implementation shown in Figure 12. The output signal of biased envelope detector 28 is squared by a multiplier, the output signal of which goes to multiplier 42.
Equation (69) can be implemented as shown in Figure 18. The
output signal from adder 30 goes to the first input of multiplier 42 and the input of envelope detector 44. The output signal of envelope detector 44 goes to the second input of multiplier 42. The output signal of multiplier 42 is the feedback signal and goes to the multipliers 22 in Figure 10 (or phase shifter 86 in Figure 11). This form of the adaptive array is yet another embodiment of the present invention.
Equation (66) is the equation for amplitude limited Y(t). Its implementation is shown in Figure 19. The output signal from adder 30 in Figure 10 is applied to the input of amplitude limiter 32. The output signal from amplitude limiter 32 is the feedback signal and is applied to multipliers 22 in Figure 10 (or phase shifter 86 in Figure 11). This form of the adaptive array is yet another embodiment of the present invention. Implementation of equation (67) is important because it is the simplest implementation. Equation (67) is equivalent to the output signal from adder 30 multiplied by 2. Letting the factor of 2 be folded into the gain constant "k", equation (67) is implemented by using the output signal from adder 30 as the feedback signal. As shown in Figure 20, the output signal from adder 30 in Figure 10 is connected directly to multipliers 22 in Figure 10 (or phase shifter 86 in Figure 11). This is the simplest possible implementation of the feedback function. This simplicity is possible because of the natures of the multipath and low level interference problems that are being solved. This form of the adaptive array is yet another embodiment of the present invention.
Now consider the case where β >|Y(t)|. Since sgn(x)=-1 when x<1, equations (51), (53), (55), and (57) yield equations which are the negative of equations (66) to (69). One method to implement this an 180 degree phase shift from the implementations of equations (66) to (69).
It can now be seen from equations (51), (53), (54), and (57) that the new cases -|Y(t)|< β <0 and β <-|Y(t)| give similar results.
LOG APPROACH
In equation (47), the fact that |γ(t)| - βP goes to zero when |Y(t)|= β is exploited. An alternate approach is to use a logarithmic function. The feedback function can be written as
∈ =Y(t)log[|Y(t)|/ β ]. (70)
Figure 21 shows an implementation of FF 104 for equation (70). The output signal of adder 30 in Figure 10 goes to envelope detector 44 and the first input of multiplier 42. The output signal of envelop e detector 44 goes to the first input of divider 88. A second input signal to divider 88 from DC source 90, divides the first input signal to divider 88. The output signal from divider 88 is the input signal to logarithm device 92, such as a log amplifier. The output signal from logarithm device 92 goes to the second input of multiplier 42. The output signal from multiplier 42 is the feedback signal and goes to multipliers 22 in Figure 10 (or phase shifter 86 in Figure 11). This form of the adaptive array is yet another embodiment of the present invention. Although all the equations and their approximations give rise to more complicated version s of the direct p ath feedb ack function implementation in Figure 20, there are performance differences between them. They provide different convergence rates, different degrees of adapting to the ideal valu e, approach the steady state th rough different paths, etc. They also apply to different ranges of problem parameters; the less complicated implementations tend to apply to the narrower ranges. Consequently, one specific implementation can be more eff ectiv e f or a given signal environm ent than the other implem entation s . The CMA implementations of the invention p resented here used the multiplier 22, the integrator 24, and the weighting function 26 to change the weight value. But the invention is not limited to these implementations. It can use other implementations to change the weight v alu es. The new approximations and restricted parameters to the feedback equations for the CMA adaptive array implementations can be applied to the C MA filter as well. Figure 10 would have only a single in put signal in this case (besides an antenna input signal, any other input signal made u p of multiple image s of th e desired signal is applicable). The implementation of the feedback equations in Figures 16, 17, 18, 19, and 20 to the C MA filters are more embodiement of the invention.
The implementations presented above is a new invention, however, the implementations of the invention are n ot limited to the
implementations presented.
All the forms of invention can be implemented in software, digital, analog and hybrid form.
OTHER INTERFERENCE SOURCES
The adaptive array is also able to reduce distortion effects caused by interference sources other than multipath signals. As described above, the dominate term in the array output signal is the term with the largest amplitude, which, in turn, corresponds to the array input signal with the largest amplitude. Consequently, the reference signal generated by amplitude limiting the array output signal is dominated by this term also. Since the adaptive array correlates the reference signal to the array input signals, and since the dominate term of the reference signal corresponds to the dominate array input signal, the adaptive array receives the dominate array input signal and rejects the signals with a lower amplitude level. Therefore, when the amplitude of the signal of interest is greater than the amplitude of the interference signals, the interference signals are rejected.
Similarly, the CMA adaptive array and filter implementations of this invention can also reduce distortion effects caused by interference. When the amplitude of the signal of interest is greater than the amplitude of the interference signal, the interference signal is rejected.
HARDWARE DESCRIPTION
Having presented the principles of operation of the adaptive array, a detailed description is presented below. The signal of interest contains no special codes, transmitted tones, or special waveform structures to achieve the rejection of the indirect path signals as has been the case in the prior art. The DOA and the TOA of the signals are can also be unknown.
Figure 5 shows an LMS adaptive array with the reference signal generator of the present invention. The input signals to the adaptive array are developed by appropriate input devices such as antenna elements 10, bandpass filters 12, and mixers 18. The second input signal to the mixers 18 is generated by a single local
local oscillator 16, thus, mixers 18 convert the input signals to phase coherent signals at an appropriate IF frequency. The respective mixer output signals go to a corresponding tapped delay line 20 which has M output terminals. Each tapped delay line 20 output signal goes to a corresponding amplitude weight 26 and multiplier 22. The multiplier 22 associated with each tapped delay line 20 output signal receives the error signal from subtractor 34 as Its second input signal. E ach multiplier 22 output signal goes to a corresponding integrator 24. The integrator 24 output signal goes to a corresponding amplitude weight 26. The amplitude weight 26 weights its input signal f rom the corresponding tapped delay line 20 output signal. The amplitude weight 26 output signals go to adder 30 where they are summed. The adder 30 output signal is the adaptive array output signal which goes to the appropriate next stage of signal processing, such as an IF amplifier, a d emodulato r, etc.
The adder 30 output signal also goes to the amplitude limiter 32 and the subtractor 34. The amplitude limiter means 32, which keeps its output signal amplitude at a constant level no matter wh at the amplitude of its input signal, generates the reference sign al; the frequency and phase of the amplitude limiter input signal are preserved in the output signal. The amplitude limiter 32 output signal, which is the reference signal, goes to the subtractor 34. Sub tractor 34 subtracts the array output signal of adder 30 from the reference signal of amplitude limiter 32 to yield an error signal. The error signal from subtractor 34 is then applied to each multiplier 22 as described above. The system adjusts the amplitude weights so that the unwanted multipath and/or interference signals are rejected resulting in less distortion of the signal of interest at the adaptive array output signal. The invention work s f or unmodulated, angle m od ulated, amplitude modulated, etc. signals .
Presented below are manufacturer part/model numbers for the key components of a specific hardware implementation of Figure 5 and Figure 8. This implementation operates at the intermediate frequency of 10 Mhz after down converting from the received frequency with mixers 18 and local oscillator 16. The tapped delay lines 20 can be implemented with a Data Delay Devices 1505-100A tapped delay line. It has equal taps which, when including an undelayed version of the antenna element input signal, gives the tapped delay line 20 six output terminals ,
where each output terminal must be properly impedance matched to the system. For narrow bandwidth signals, an alternative to the tapped delay line is the ninety degree hybrid which can be implemented by the Mini- Circuits PSCQ-2-10.5. The multiplier 22 can be implemented with Mini-Circuits SBL-1 mixer operated in the linear multiplication region. The integrator 24 can be implemented with National Semiconductor LH0032 operational amplifier in an integrator circuit. The amplitude weight 26 can be implemented with the Motorola MC1595 four quadrant linear multiplier with proper output impedance matching. The adder 30 can be implemented from a network of Mini-Circuits MSC-2-1 two way power combiners where the number of signals to be summed determines the number of power combiners required. The amplitude limiter 32 can be implemented by an Avantek, Inc. UTL-1002 signal limiter. The subtractor 34 can be implemented by a Mini-Circuits PSCJ-2-1 180 degree two way power combiner. The phase shifter 38 can be implemented by a Data Delay Device 1503- 100 A variable delay, the output port of which is properly impedance matched. This is one specific hardware implementation of the invention, however, the invention is not limited to the use of these components or this specific implementation.
Figure 10 shows the CMA adaptive array and filter implementation of the present invention using the feedback function described above. Presented below are manufacturer part/model numbers for additional key components for a specific hardware implementation of Figure 10 (and Figure 11) using the FF 104 implementations presented above. The same components are used for a specific implementation as in the implementations in Figures 5 and 8.
In addition, the key components for different feedback functions are presented. The biaser 48 can be implemented by the National Semiconductor LH0032 operational amplifier and a DC voltage source.
The dividers 46 and 88 can be implemented by the Motorola MCI 595 four quadrant linear multiplier and a National Semiconductor LH0032 operational amplifier. The envelope detector 44 can be implemented by a diode detector and the bias envelope detector 28 can be implemented by a diode detector and an operational amplifier with a DC voltage biasing the output signal of the envelope detector. For narrowband signals the phase shifter 86 can be implemented by a Data Delay Device
1503-100A variable delay.
This is just one specific set of hardware for the implementations of the present invention, however, the invention is not limited to the u se of these comp onents or thes e sp ecific implem entations .
S O F T WARE This invention can also be implemented in software. Figure 22 shows the adaptive array which uses a computer, microprocessor, or digital signal processor(DSP). The signal of interest and the unwanted multipath and/or interference signals are received by the antenna elements 10. The composite received antenna element signal is applied to bandpass filter 12. The bandpass filter 12 output signal is applied to mixer 14. The mixers 14 also receive a second input signal which is the output signal of the local oscillator 16. The mixer 14 output signal is applied to the analog to digital (A/D) converter 60. The output signals f rom the A/ D converters 60 are ap plied to the computer/microprocessor/DSP 62. In the computer/microprocessor/ DSP 62 th e adaptive array alg orithm is implemented.
Figure 23 shows a flow chart of the algorithm for an LMS adaptive array software implementation, however, the software implementation of this invention is not limited to this specific LMS adaptive array algorithm implementation. The invention is not limited to an LMS adaptive array algorithm and includes all adaptive array algorithms that use a reference signal. Each of the antenna element signals from the A/ D converters 60 is ap plied to s plitter/delayer 64. Th e splitter/ delayer makes copies of each input signal and delays each copy an appropriate length of time in such a way that it is the software equivalent of the M output tapped delay line; the number of copies of each input signal and the magnitude of each time delay depends on the signal frequencies, signal bandwidth, signal environment, performance required, etc. The splitter/delayer 64 output signal is applied to the weighter 66 where each of the input antenna element signals, delayed and undelayed, are weighted by an initial weight value by weighter 66. The weighted signals from weighter 66 are summed in adder 68. The adder 68 output signal is the adaptive array output signal and is applied to clamper 94 and delay 102. Clamp er 94 d etermines the adaptive array output signal's sign and gives it an amplitude of +F if it is positive and -F if it is negative at each data sample. The
convenient value determined by the amplitude of the re ceived signals, parameter values of other software functional blocks, round off errors, required performance, etc. The clamper 94 outp ut sig nal i s of a rectangular wave form. The clamper 94 output signal is applied to digital filter 96 which removes the 2nd and higher harmonics to convert the rectangular wave f orm from clamper 94 t o a sinu soid f orm of constant amplitude. The resulting output signal from th e digit al filter 96 is an amplitude limited version of the adaptive array output signal and is the desired reference signal. The reference signal and a delayed version of the adaptive array output signal from delay 102 are applied to subtractor 98. Delay 102 adjusts the data sample lag that can be introduced by the clamper 94 and the digital filter 96. The appropriately adjusted delay 102 output signal is subtracted f rom the reference signal by subtractor 98 to form the error signal. The error signal is applied to the weight calculator 100, which calculates the value of each weight using the set of equations (19b). The computed weight values from weight calculator 100 are applied to weighter 66 to update the weight values. Th e weighter 66 weights the new value of each of the antenna element/splitter/delayer input signals with the new weight values. The cycle continues to repeat itself with the weight v alu es convergin g to t heir e quilib riu m v alu es .
Figure 24 shows a flow chart for software implementations of the CMA adaptive arrays and filters, however, the software implementations of the various embodiments of the present invention are not limited to the implementations presented below. Each of the digitized antenna element signals from A/D converters 60 go to splitter/ delayer 64. The splitter/delayer 64 makes copies of each input signal and d elays each copy an appropriate length of time in s uch a way that it is th e software equivalent of an M output tapped delay line; the numb er of copies of each input signal and the magnitude of each time d elay d epend s on the sign al f requ encies, signal b andwidth, sign al environment, performance required, etc. Each signal copy is associated with an adaptive loop. The splitter/delayer 64 output signal goes to the weighter 66 where each of the input antenna ele ment s ig nals , delayed and undelayed, are weighted b y an initial weight value. The splitter/delayer 64 output signal also goes to multiplier 72. Th e weighted signals from weighter 66 are summed in adder 68. The output signal of adder 68 is the adaptive array output signal and goes to
feedback function 70. Feedback function 70 computes the feedback signal ∈ . Which specific feedback equation is implemented by feedback function 70 depends on which values of p and q are chosen in equations (48), (49), and (50) and, where appropriate, whether an approximation or restricted parameter are chosen. Two examples of feedback function 70 implementations are presented below. The output signal ∈ f rom feedhack function 70 goes to multiplier 72. Multiplier 72 multiplies the feedback signal with each of the delayed and undelayed signals from splitter/ delayer 64. The multiplied output signals for each adaptive loop f rom multiplier 72 goes to integrator 74. Integ rator 74 integrates the output signal of multiplier 72 for each resp ective adaptive loop. The integrator 74 output signal goes to weighter 66 which updates the value of each corresponding weight. This cycle continues to repeat itself with the weight values converging to their equilib rium values.
Figure 25 shows the flow chart for the feedback function 70 for one software implementation of equation (61) when it is separated into the terms 2Y(t) and 2[|Y(t)|-β ]. The output signal of adder 68 in Figure 24 goes to the biased envelope detector 76 and delay 78. The biased envelope detector 76 determines the signal envelope of the adder 68 output signal. One such envelope detector implementation would be a relative peak detector. The detected envelope signal is biased by a constant negative value. The biased envelope detector 76 output signal goes to multiplier 80. When necessary, delay 78 delays the output signal of adder 68 to account for a time delay required to implement the biased envelope detector 76 so that the output signal of biased envelope detector 76 and the output signal of adder 68 are properly synchronized. The output signal of delay 78 goes to multiplier 80. Multiplier 80 multiplies the output signal of biased envelope detector 76 and the output signal of delay 78. The output signal of multiplier 80 is the feedback signal and goes to multiplier 72 in Figure 24.
Figure 26 shows the flow chart for the feedback function 70 for one software implementation of equation (52) when separated into the two factors 2[Y(t)/|Y(t)|] and [|Y(t)|-β ]. The output signal of ad der 68 in Figure 24 goes to the biased envelope detector 76 and amplitude limiter 82. One implementation of the amplitude limiter is a clamp er and lowp as s digital filter.
At each data sample, the clamper assigns to the output signal of
adder 68 an amplitude of +F if the output signal of adder 68 is positive and -F if the output signal of adder 68 is negative. The clamper output signal is of a rectangular wave form and goes to the digital filter which removes the second and higher harmonics to convert its rectangular wave form to a sinusoid form of constant amplitude. The resulting output signal from the digital filter is an amplitude limited version of the output signal of adder 68. The amplitude limiter 82 output signal goes to delay 84.
The biased envelope detector 76 determines the signal amplitude envelope of the output signal of adder 68. One such envelope detector implementation would be a relative peak detector. The detected envelope signal is biased by a constant negative value. The output signal of biased envelope detector 76 also goes to delay 84.
Delay 84 appropriately delays either the output signal of biased envelope detector 76 or the output signal of amplitude limiter 82 so that the two signals are properly synchronized. Which of the two signals is actually delayed depends on the details of the specific implementations of the biased envelope detector 76 and the amplitude limiter 82. The synchronized output signals of the biased amplitude detector 76 and of amplitude limiter 82 go from delay 84 to multiplier
80. Multiplier 80 multiplies these signals. The multiplier 80 output signal is the feedback signal and goes to multiplier 72 in Figure 24.
It would be clear to a person skilled in the art that the adaptive array and filter software implemented by the CMA adaptive array and filter software flowchart in Figure 24, Figure 25, and Figure 26 can be implemented by other means.
It would be clear for someone skilled in the art that the invention can be implemented in either digital software, digital hardware, analog, and hybrid forms.
From the forgoing description, it will be apparent that the invention disclosed herein provides novel and advantageous signal processing systems. It will be understood by those familiar with the art, the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.