EP0814456A2

EP0814456A2 - Active noise or vibration control (ANVC) system and method including enhanced reference signals

Info

Publication number: EP0814456A2
Application number: EP97302679A
Authority: EP
Inventors: Steve C. Southward; Lane R. Miller
Original assignee: Lord Corp
Current assignee: Lord Corp
Priority date: 1996-06-17
Filing date: 1997-04-18
Publication date: 1997-12-29
Also published as: EP0814456A3

Abstract

An ANVC system (50) and method of enhancing the reference (input) signal (51) provided from a reference sensor (52) in a feedforward-type Active Noise or Vibration Control (ANVC) system (50). Preferably, an Adaptive Line Enhancer (ALE) (54) is provided in the input path for reducing the uncorrelated noise present in the reference signal (51). In one aspect, a tone(s) present can be enhanced by reducing broadband uncorrelated noise. In another aspect, the broadband input to the ANVC control (58) can be enhanced by eliminating uncorrelated tone(s). The filter structure used in the ALE (54) may include IIR or FIR and the algorithm used to update the ALE filters may include LMS, RLS, or GMV. Parametric and adaptive inverse ALEs (69) and (80) are also described. In alternate embodiments, multiple ALEs are arranged in cascaded or parallel relationship. Further, the tonal output (22) of the ALE (54) may be used as an input to auxiliary components such as Engine Vibration Monitors (EVMs). The ALEs are beneficial in both tonal and broadband ANVC systems (50).

Description

Field of the Invention

The invention relates to the area of active noise and vibration control. Specifically, the invention relates to a feedforward active noise and vibration control system using a reference input.

Background of the Invention

Active noise and vibration control (ANVC) systems generally utilize input or reference sensors which provide signals indicative of the source of disturbance (noise or vibration), error sensors which provide signals indicative of residual noise or vibration to be canceled, and adaptive processing of the input and error signals in order to arrive at output signals. These output signals drive output transducers thereby producing antinoise or antivibration with the result of reducing the residuals at the error sensors, thereby reducing the noise or vibration thereat. It is known that the Single-Input Single-Output (hereinafter SISO) ANVC variant is a subset of the more general Multi-Input Multi-Output (hereinafter MIMO) ANVC system. See IEEE paper, Vol. ASSP-35, No. 10, Oct. 1987, by Elliott, Stothers, and Nelson, entitled "A Multiple Error LMS Algorithm and Its Application to the Active Control of Sound and Vibration" for a description of one MIMO system. It is desired to have a reference signal which is only indicative of the source of disturbance to be canceled, if possible. However, in many real-world applications, the input signal may be corrupted with background noise which is not correlated with the disturbance to be canceled. A corrupted input signal is likely to corrupt the output signals, as any noise contained therein may be passed on directly to the output transducers (speakers, Active Vibration Absorbers (AVAs), or other actuators), and may even be amplified. The presence of this background noise detracts from the effectiveness of the ANVC system and contributes to increase the overall level of noise detected by the error sensors.
Therefore, there is a recognized need for a method of reducing the background noise present in input signals, i.e., a method of increasing the signal-to-noise ratio. In particular, there is a dire need for such a method for application to tonal ANVC systems where a reference signal contains one or more dominant tones that are being controlled, yet the input signal including the tones, also includes unwanted broadband noise.
Adaptive line enhancement (ALE) methods are known for separating a signal into a periodic component signal and a random component signal. One known example application of ALE techniques is for canceling the maternal heartbeat in fetal electrocardiography as described in Adaptive Signal Processing, pp. 334-337, by Widrow and Stearns, 1985. Another known example application of ALE techniques is for removing periodic interference from training noise in an ANVC system identification process as described in Investigations in Active Line Enhancer Techniques by Shawn Steenhagen, MS Thesis, Univ. of Wisconsin, Madison, 1993.
Prior Art Fig. 1 describes one conventional prior art ALE implementation. The conventional Finite Impulse Response (FIR) ALE 20a processes an ALE input signal s+n, 21a, containing the periodic component signal s plus the random component signal n with the objective of separating the periodic component signal from the random component signal. The ALE input signal s+n is delayed Δ>0 samples by the delay operator 28a to produce the delayed input signal 29a. The delayed input signal is processed by an FIR filter 25a to produce an estimate s' for the periodic component signal 22a. The periodic component signal estimate is subtracted from the input signal by the error summation block 24a to produce an estimate n' for the random component signal 23a. One or both of these component estimates may be used depending on the requirements of the application. The random component signal 23a is also interpreted as an ALE error signal ek, given by $e_{k} = x_{k} - y_{k}$
where xk is the ALE input 21a, and yk is the adaptive FIR filter output 22a given by
The ALE input signal 21a and the broadband component estimate signal n' are processed by an adaptive update block 26a, using a gradient descent method such as LMS $A [i]_{k+} _{1} = A [i]_{k} {+ µ}_{A} e_{k} x_{k} _{-} _{i} _{-Δ} i = 0,(N_{A} -1)$
to provide updated filter coefficients 27a to the FIR filter 25a.
Prior Art Fig. 2 describes a second conventional prior art ALE implementation. The conventional Infinite Impulse Response (IIR) ALE 30b processes an ALE input signal s+n, 21b, containing the periodic component signal s plus the random component signal n with the objective of separating the periodic component signal from the random component signal. The ALE input signal s+n is delayed Δ>0 samples by the delay operator 35b to produce the delayed input signal 36b. The delayed input signal is processed by an FIR A-filter Ak, 31b, to produce an output 39b. The estimate s' for the periodic component signal 22b is processed by an FIR B-filter Bk, 32b, to produce an output 40b. The A-filter output is combined with the B-filter output in the path summation block 41b to generate the periodic component signal estimate 22b. The periodic signal component estimate is given by
The periodic component signal estimate is subtracted from the ALE input signal by the error summation block 24b to produce an estimate n' for the broadband component signal 23b. One or both of these component estimates may be used depending on the requirements of the application. The ALE input signal 21b and the broadband component estimate signal n' are convolved by an A-filter correlator block 33b which is used in the LMS gradient descent method to provide A-filter adjustments 34b to the FIR A-filter 31b. The periodic component signal estimate s' and the broadband component estimate signal n' are convolved by a B-filter correlator block 37b which is used in the LMS gradient descent method to provide B-filter adjustments 38b to the FIR B-filter 32b. These filter coefficient updates are given by $A [i]_{k} _{+1} = A [i]_{k} {+ µ}_{A} e_{k} x_{k-i-} _{Δ} i = 0,(N_{A} - 1)$
$B [j]_{k+1} = B [j]_{k} {+ µ}_{B} e_{k} y_{k-j} j = 1, (N_{B} - 1)$
when the LMS algorithm is used. However, none of the above address the need for an ANVC system with an enhanced reference signal.

Summary of the Invention

Therefore, in light of the problems associated with prior art ANVC systems identified above, the present invention, in one aspect thereof, is directed to a method and means for reducing unwanted background noise that is present in a reference (input) signal in a feedforward-type ANVC system. In particular, the present invention is directed to adaptive gradient descent means for reducing the noise present in the reference (input) signal of an ANVC system. More specifically, an Adaptive Line Enhancer (ALE) is preferably placed in the input path of the ANVC system. The adaptive means, such as an ALE, for enhancing the reference signal can reduce broadband noise from the input signal if there are one or more dominant tones therein to be controlled, or conversely, tones may be reduced if a broadband input is required for the input to the ANVC system.
The ALE may include IIR, FIR, parametric or adaptive inverse implementations, or the like. The ALEs may be adaptively controlled via any of the known gradient descent algorithms. The use of ALEs in the input path is beneficial to any of the known ANVC systems, including Active Noise Control (ANC), Active Structural Control (ASC), and Active Isolation Control (AIC). Further, the ALE enhanced reference signal described herein can be used with any of the known ANV control processes, such as those including FIR and IIR adaptive filters. In another aspect, multiple ALEs may be used in parallel or cascaded relationship to provide, for example, a reference signal with contributions from multiple tones. Further, the enhanced reference signal provided by the ALE, besides being used as an input to the control process, may be used as an input to auxiliary components, such as an Engine Vibration Monitor (EVM) which is used for monitoring, and/or, displaying the vibration of aircraft engines.
The main advantage of the invention is that it provides a reference (input) signal to the ANVC system that has substantially higher Signal-To-Noise (SNR) ratio.
It is another advantage of the invention that the power requirements to drive the output transducers can be reduced.
It is another advantage of the invention that it may reduce the computational requirements for the adaptive control of the ANVC system, for example, the number of taps in a multi-tap control algorithm may be reduced.
It is another advantage of the invention that it may enhance the performance and convergence of the ANVC system.
The abovementioned and further novel aspects, features and advantages of the invention will be apparent from the accompanying descriptions of the preferred embodiments and attached drawings.

Brief Description of the Drawings

The accompanying drawings which form a part of the specification, illustrate several key embodiments of the present invention. The drawings and description together, serve to fully explain the invention. In the drawings,

Fig. 1 is a schematic diagram of a prior art FIR ALE,
Fig. 2 is a schematic diagram of a prior art IIR ALE,
Fig. 3 is a block schematic view of an Active Noise Control (ANC) system using a microphone input sensor, an ALE for reducing noise in the reference signal which is uncorrelated with the disturbance to be canceled and a loudspeaker as an output transducer,
Fig. 4 is a schematic view of another Active Noise Control (ANC) system using an accelerometer input sensor, an ALE in the input path, and a loudspeaker as the output transducer,
Fig. 5 is a schematic view of an Active Structural Control (ASC) system using an accelerometer input sensor, an ALE on the input, and an Active Vibration Absorber (AVA) as the output transducer,
Fig. 6 is a schematic view of an Active Isolation Control (AIC) system using an accelerometer input sensor, an ALE in the input path, and an active mounting as the output transducer,
Fig. 7 is a schematic block diagram of one embodiment of the present invention ANVC system including a FIR ALE in the input path and an FIR process control for tonal control,
Fig. 8 is a schematic block diagram of another embodiment of the present invention ANVC system including a IIR ALE in the input path and an FIR process control for tonal control,
Fig. 9 is a schematic block diagram of another embodiment of the present invention ANVC system including a IIR ALE in the input path and an II R process control for tonal control,
Fig. 10 is a schematic block diagram of a parametric ALE,
Fig. 11 is a schematic block diagram of an adaptive inverse ALE,
Fig. 12 is a schematic block diagram of another embodiment of the present invention ANVC system including a parametric ALE in the input path and an FIR process control for tonal control,
Fig. 13 is a schematic block diagram of another embodiment of the present invention ANVC system including a adaptive inverse ALE in the input path and an FIR process control for tonal control,
Fig. 14 is a schematic block diagram of another embodiment of the present invention ANVC system including a FIR ALE and FIR process control for broadband control,
Fig. 15 is a schematic block diagram of another embodiment of the present invention ANVC system including multiple Low-Order IIR ALEs,
Fig. 16 is a schematic block diagram of another embodiment of the present invention ANVC system including a High-Order FIR ALE,
Fig. 17 is a schematic block diagram of another embodiment of the present invention ANVC system including multiple cascaded ALEs,
Fig. 18 is a schematic block diagram of another embodiment of the present invention ANVC system including using the ALEs output for an input to an auxiliary component,
Fig. 19 is a figure illustrating a reference signal having multiple tones and broadband background noise, and
Fig. 20 is a figure illustrating an output signal from the Band Pass Filter (BPF) and also from the ALE within the input path showing the resulting further reduction in unwanted broadband background noise in the reference signal.

Detailed Description of the Preferred Embodiments

With reference to the figures herein, where like reference characters are employed where possible to indicate like parts, there is shown in Fig. 3, an ANVC system, and in particular, an ANC system 50c comprising an input sensor 52c for providing a reference signal indicative of the disturbance acoustic noise or vibration causing the disturbing acoustic noise. In the ANC case, the disturbance may be an acoustic noise emanating from a noise source, such as an aircraft engine or the like. In most applications, the reference signal generally will include some unwanted noise therein. By the term noise, what is referred to is background noise which is uncorrelated with the disturbance which is sought to be canceled. In the ANC system 50c, adaptive means are provided for reducing the noise present in the reference signal.
In particular, an Adaptive Line Enhancer (ALE) 54c, which includes an adaptive filter and update means for updating the coefficients or weights of the adaptive filter, is preferably is used. An error sensor 62c is provided for generating an error signal indicative of the residual acoustic noise at the point adjacent where a quiet zone is desired. The means for processing the error signal and the reference signal and producing an output signal is provided in the control process 58c, in this case, ANC control, which includes a filter taking the form of a IIR or FIR filter structure with adaptive feedforward control. An output transducer 60c, in this case, a loudspeaker, is dynamically driven responsive to the output signal. The output transducer 60c produces antinoise which preferably minimizes the noise at the point of interest to produce a quiet zone. The controller 56c includes both the adaptive means, such as ALE 54c, for reducing the unwanted noise on the input signal and the control process 58c therewithin. It should be understood that the reference enhancement means is based upon an adaptive gradient descent method and is generally implemented within the software. It should also be understood that there may be an optional filtering/conditioning step before the reference signal is provided to the ALE 54c.
Fig. 4 represents another ANC system 50d which is identical to the system of Fig. 3 except that the reference sensor is an accelerometer 52d. One ANC system for which the ALE used within the input path as described herein may be useful is discussed in commonly assigned US Application Serial No. 08/553,227 to G. Billoud entitled "Active Noise Control System for Closed Spaces Such As Aircraft Cabins" filed Sept. 25, 1995. Other ANC systems are described in US Pat. No. 4,562,589 to Warnaka et al. entitled "Active Attenuation of Noise in a Closed Structure" and US Pat. No. 4,473,906 to Warnaka et al. entitled "Active Acoustic Attenuator."
Fig. 5 represents another ANVC system, and in particular, an ASC system 50e which is identical to the system of Fig. 4 except that the output transducer is an AVA 60e. Active systems including AVAs are described in PCT Patent Application Serial No. PCT/US95/13610 entitled "Active Systems and Devices Including Active Vibration Absorbers (AVAs)" and US Pat. No. 4,715,559 to Fuller entitled "Apparatus and Method For Global Noise Reduction." In aircraft ASC systems, AVAs are attached directly to the interior surface of the aircraft's fuselage and dynamically shake the fuselage wall to generate canceling noise in the aircraft's cabin.
Fig. 6 represents another ANVC system, and in particular, an AIC system 50f which is identical to the system of Fig. 5 except that the output transducer is an active mount 60f. Active mounts are taught in commonly assigned US Pat. No. 5,174,552 to Hodgson et al. entitled "Fluid Mount with Active Control" and US Pat. App. Serial No. 08/260,945 entitled "Active Mounts for Aircraft Engines." Further descriptions may be found in a Lord paper entitled "Frequency-Shaped Control of Active Isolators" by D. A. Hodgson. Active mounts 60f attach between an engine and the structure the engine is attached to and are dynamically actuated (driven) to control vibration therebetween or noise at a remote location.
Fig. 7 represents a detailed block diagram of a tonal ANVC system 50g including a reference sensor 52g, an optional band pass filter 53g, an ALE 54g, and output transducer 60g, and error sensor 62g and an adaptive control process 58g which may include system identification, hereinafter referred to as ID 42g. The ID 42g may be accomplished in an on-line or off-line fashion. Further, filtering or other signal conditioning is commonly used on the output signal and error signal paths, however, they are not shown for the sake of clarity in all figures described herein. The ALE 54g herein includes a FIR filter structure as is described fully with reference to Fig. 1. The reference signal 51g from reference sensor 52g is preferably band-pass filtered and received at the ALE input 21g. The output of the ALE 22g is provided to the control process 58g for the ANVC system 50g. In this embodiment, the ALE output 22g is comprised of the periodic component of the reference signal 51g, i.e., the one or more tones that are indicative of the noise or vibration 49g generated by the source of disturbance 48g.
In this embodiment, the control process 58g is achieved by an FIR filter 59g including update means, such as filtered-x LMS, or the like. A further discussion of a Single-Input Single-Output (SISO) Filtered-x LMS control with system ID can be found in "Adaptive Control of a Two-Stage Vibration Isolator Mount" by S. D. Sommerfeldt and J. Tichy, Journal of the Acoustical Society of America, Vol. 88, No. 2, pp. 938-944, 1990. A further discussion of Multiple-Input Multiple-Output (MIMO) systems including FIR filters is described in US Pat. No. 5,170,433 to Elliott et al. entitled "Active Vibration Control." In General, the ALE output 22g is filtered through an error path model 63g (sometimes referred to as the X filter) representative of the transfer function between each output transducer 60g and error sensor 62g pair. The filtered ALE output is referred to as the filtered regressor 65g. Error information in line 64g represents the error signal 55g and any filtered training noise from training block 42g. Error information 64g and the filtered regressor 65g are inputted to an update method and means 61g, such as Filtered-x LMS, for determining the new filter weights to be passed to the FIR control filter 59g. The output 22g from the ALE 54g is filtered by the control filter 59g to arrive at the output signal 57g used to drive the output transducer 60g, e.g. a loudspeaker, AVA, active mount or the like. It should be understood that the use of the ALE 54g in the input path provides a cleaner reference signal to the control filter 59g, therefore, the output signal 57g to the output transducer 60g is also cleaner and the ANVC system 50g will do a better job at cancellation of the noise or vibration.
Fig. 8 represents another tonal feedforward ANVC system 50h including a reference sensor 52h, an adaptive gradient descent means for reducing the uncorrelated noise present in the reference signal 51h, such as an ALE 54h, an adaptive control process 58h including an FIR filter 59h which may include system identification 42h, an output transducer 60h, and an error sensor 62h. The ALE 54h used herein is fully described with reference to Fig. 2 and represents a IIR filter ALE. Again, the output 22h from the IIR ALE 54h is used as an input to the control process 58h which is identical to that described with reference to Fig. 7.
Fig. 9 represents another tonal feedforward ANVC system 50j including identical elements as described with reference to Fig. 8 except that the control process 58j includes a IIR filter instead of a FIR filter. Therefore, the ANVC system 50j is a combination of a IIR control filter process 58j and a IIR ALE 54j. Control process 58j may include system ID 42j which may be implemented in an on-line or off-line fashion. IIR control filter structures are described in US Pat. No. 4,677,676 to Eriksson entitled "Active Attenuation System with On-Line Modeling of Speaker, Error Path, and Feedback Path" and US Pat. No. 4,677,677 to Eriksson entitled "Active Sound Attenuation System with On-Line Adaptive Feedback Cancellation."
Fig. 10 is a schematic showing one embodiment of a parametric IIR ALE. In this configuration, the adaptive filter is a IIR filter similar to the prior art Fig. 2; however, the parametric IIR ALE described herein does not require a delay operation as in 35b, and the filter coefficient update process is greatly simplified with a constrained adaptation process due to the parametization. The parametric IIR ALE 69m processes an ALE input signal s+n, 21m, containing the periodic component signal s plus the random component signal n with the objective of separating the periodic component signal from the random component signal. The ALE input signal is processed by an FIR A-filter A(ωk), 31m, to produce an output 39m. The estimate s' for the periodic component signal 22m is processed by a FIR B-filter B(ωk), 32m, to produce an output 40m. The A-filter output is combined with the B-filter output in the path summation block 41m to generate the periodic component signal estimate 22m. The periodic component signal estimate is subtracted from the ALE input signal by the error summation block 24m to produce an estimate n' for the broadband component signal 23m. One or both of these component estimates may be used depending on the requirements of the application.
Each of the IIR filter coefficients in A(ωk) and B(ωh) are explicitly parametized by the center frequency ωk. This parametization is accomplished by first selecting a desired frequency response for the IIR filter. A preferred response is a second-order band-pass filter which is given by
The filter bandwidth (BW) and the sharpness of resonance (Q) are given by
where ξ is the damping ratio, and ω_o is the center frequency. Using the pre-warped bilinear transform, the three non-zero digital filter coefficients may be analytically expressed in terms of the center frequency and bandwidth. This parametization is given by

where T is the sample period, and ωk is the time varying center frequency. For a constant bandwidth, the digital filter coefficients are parametized by center frequency only.
There are many methods available for adaptively updating the center frequency. One preferred approach is to use an LMS gradient descent method which minimizes the instantaneous squared ALE error ek, represented by 23m, which is equivalent to n'. The ALE error is given by $e_{k} = x_{k} - y_{k}$
where xk is the ALE input 21m, and yk is the adaptive IIR filter output 22m. For the second-order band-pass filter response, the adaptive IIR filter output is given by $y_{k} = A_{0} {(ω}_{k}) x_{k} {- A}_{0} {(ω}_{k}) x_{k} _{-2} - B_{1} {(ω}_{k}) y_{k} _{-1} - B_{2} {(ω}_{k}) y_{k} _{-2}$
Taking the derivative of the instantaneous squared ALE error with respect to center frequency ωk
the update expression for the center frequency becomes
where µ is a constant step size which controls the rate of convergence. The derivatives are also analytically available for the second-order band-pass filter response, and are given by
The parametric FIR filter coefficients 31m and 32m are updated with each new estimate of the center frequency 77m. The center frequency is updated by the center frequency update 76m wherein the gradient estimate 75m is added to the previous center frequency value. The gradient estimate is the output of a gradient summation block 74m which sums the A-filter gradient contribution 71m and the B-filter gradient contribution 73m. The ALE input signal is convolved with n' in the A-filter correlator block 33m which produces an A-filter convolution vector 78m. The A-filter convolution vector is multiplied by the gradient of the A-filter coefficients with respect to center frequency, in the A-filter parametric gradient product block 70m, which produces the output A-filter gradient contribution 71m. The periodic component signal estimate s' is convolved with n' in the B-filter correlator block 37m which produces a B-filter convolution vector 79m. The B-filter convolution vector is multiplied by the gradient of the B-filter coefficients with respect to center frequency, in the B-filter parametric gradient product block 72m, which produces the output B-filter gradient contribution 73m.
The parametric IIR ALE may be further simplified in several ways. One improvement to the invention is to restrict the operational range of the center frequency, which is easily accomplished when the center frequency is adapted explicitly. The restriction is accomplished by not allowing the adaptation process to select a center frequency outside of some prescribed range. Upon inspection of the partial derivatives of the filter coefficients with respect to center frequency, it may be observed that some of the derivatives are several orders of magnitude smaller than the others. These small derivatives may be assumed zero and the corresponding parameter values may be assumed constant over the restricted frequency range. The transcendental parametric expressions for the coefficients whose derivatives are not negligible may also be preferably replaced with polynomial curve fit, or other expressions which are simpler to evaluate in real time.
Fig. 11 is a schematic showing one embodiment for an adaptive inverse ALE. In this configuration, the adaptive filter is an FIR filter similar to the prior art Fig. 1; however, the adaptive inverse ALE uses the adaptive filter coefficients in a novel way for separating the periodic component signal from the random component signal. The adaptive inverse ALE, 80p, processes an ALE input signal 21p where reference numerals 21p, 28p, 29p, 34p, 33p, 25p, and 24p are exactly as described in reference to Fig. 1. Signal 81p is an auxiliary periodic component signal and signal 82p is an auxiliary random component signal which are only used in the adaptation of the FIR A-filter Ak.
The ALE input signal is further processed by a IIR filter which is constructed from the modified FIR filter coefficients Ak, and an additional filter Gk as described below. The ALE error signal ek is given by $e_{k} = x_{k} - y_{k}$
where xk is the ALE input 21p, and yk is the adaptive FIR filter output 81p. The preferred FIR A-filter is a two-coefficient filter whose output is given by $y_{k} = A {[1]}_{k} x_{k} _{-Δ} + A {[2]}_{k} x_{k} _{-1-Δ}$
The LMS gradient descent update for each of these two coefficients is given by $A {[1]}_{k} _{+1} = A {[1]}_{k} {+ µ}_{A} e_{k} x_{k} _{-Δ}$
$A {[2]}_{k} _{+1} = A {[2]}_{k} {+ µ}_{A} e_{k} x_{k-1-} _{Δ}$
In order to understand how the adaptive inverse ALE operates, we first look at an example input signal which is a pure tone as represented in complex notation by
where X is an arbitrary non-zero amplitude, ω_o is the radian frequency, k is the sample index, T is the sample period, and φ is an arbitrary phase angle. Substituting this result into the expression for the ALE error signal defined above

Under perfect convergence conditions, the ALE error signal will be zero and thus
This equation may be solved for the ideal or optimal A-filter coefficients as
It can be shown that the LMS gradient descent update described above will drive the A-filter coefficients toward the optimal values given above. A further simplification can be achieved by selecting Δ=1. The ALE input signal is input to a FIR feedforward filter Gk, 31p, which produces a G-filter output 83p. The periodic component signal estimate 22p is produced from the path summation 41p whose inputs are the G-filter output and the modified A-filter output 84p. In the preferred embodiment, the G-filter is a single coefficient which appropriately scales the ALE input signal. The signal s' is delayed by the delay block 28p' to produce the delayed output 29p' which is input to the modified A-filter 85p. The modified A-filter produces the modified A-filter output 84p. If the A-filter coefficients from 25p were used directly in 85p without modification, the resulting digital IIR filter would have undamped poles at the frequency of the input excitation. In practice, damping is added to the poles. In the preferred embodiment, the transfer function G(z) of the resulting IIR filter is given by
and the filter output rk, which is equivalent to s', is given by $r_{k} = G_{k} x_{k} {+ γA[1]}_{k} r_{k} _{-Δ} {+ γ}^{2} A {[2]}_{k} r_{k} _{-1-Δ}$
where 0<<γ<1. The poles of the resulting IIR filter are ${z = γ[cos(ω}_{o} T) ± j {sin(ω}_{o} T)]$
$∥z∥ = γ = e ^{{-ξω}_{0} T} = e ^{{-(}^{BW} /_{2}) T}$
and thus we see that the modification of scaling A[1] by γ and scaling A[2] by γ² preserves the center frequency while adding damping. The G-filter must generally be selected such that the resulting IIR filter has the appropriate gain at the center frequency. In the preferred embodiment, the gain is unity and the resulting scalar G-filter coefficient must be $∥ G (j ω_{o})∥ = 1 ⇒ G_{k} = (1 - γ) \sqrt{{1-2γcos(2ω}_{o} T {)+γ}^{2}}$
thus we see that the G-filter is parametized by center frequency.
The estimate s' for the periodic component signal 22p is subtracted from the ALE input 21p in the error summation 24p' to produce an estimate for the random component signal 23p. One or both of these component estimates may be used depending on the requirements of the application.
The adaptive inverse ALE may be further simplified in several ways. By choosing Δ=1, A[2]=-1 it does not require adaptation, and A[1]=2cos(ωoT). Since A[1] is parametrically related to the center frequency ωo, the adaptive update expression may be written in terms of an adaptive center frequency from which A[1] is then computed using the known parametric relationship. The scalar G-filter coefficient may then easily be computed using its known parametric relationship. One improvement to this aspect of the invention is to restrict the operational range of the center frequency, which is easily accomplished when the center frequency is adapted explicitly. The restriction is accomplished by not allowing the adaptation process to select a center frequency outside of some prescribed range. When the operating frequency range is restricted, the transcendental parametric expressions for A[1] and the G-filter coefficient may be preferably replaced with polynomial curve fit or other expressions which are simpler to evaluate in real time.
Fig. 12 represents another tonal feedforward ANVC system 50m including a reference sensor 52m, adaptive gradient descent means for reducing the uncorrelated noise present in the reference signal 51m, such as ALE 54m, an adaptive control process 58m including a FIR filter 59m which may include system identification 42m, an output transducer 60m, and an error sensor 62m. The ALE 54m used herein receives its ALE input 21m in the form of the band-pass-filtered reference signal. The ALE output 22m which represents a more refined tonal signal, as compared to the reference signal 51m and the band-pass-filtered reference signal, is provided to the control process 58m. The ALE 54m is fully described with reference to Fig. 10 and represents a parametric ALE. The simulated performance of this parametric embodiment is illustrated in Fig. 20. It should be understood that, although a FIR control process 58m is shown, a IIR control process as described with reference to Fig. 9 could also be employed in combination with the parametric ALE 54m.
Fig. 13 represents another tonal feedforward ANVC system 50p identical to that described with reference to Fig. 12 except that the ALE 54p is an adaptive inverse ALE. The ALE 54p used herein is fully described with reference to Fig. 11 and represents an adaptive inverse FIR ALE. It should be understood that although a FIR control process 58p is shown, a IIR control process as described with reference to Fig. 9 could also be employed in combination with the adaptive inverse ALE 54p. As described with previous embodiments, the ALE includes its ALE input 21p and ALE output 22p within the reference path and is used to provide a refined tonal signal with the broadband uncorrelated noise reduced.
Fig. 14 represents a broadband feedforward ANVC system 50r including an ALE 54r. The ANVC system is identical to that described with reference to Fig. 7 except that the ALE 54r is used to reduce unwanted uncorrelated periodic noise (tones) in the reference signal path and leave only the broadband noise, which is desired to be controlled. Since broadband noise is desired to be reduced by the broadband control process, then any uncorrelated tones present in the reference signal will detract from the effectiveness of the broadband control. The ALE 54r used herein is fully described with reference to Fig. 1 and represents a conventional FIR ALE. It should be understood that although a FIR control process 58r and conventional FIR ALE 54r are shown, that other combinations are possible. For example, the output from the conventional IIR ALE 30b in Fig. 2 at 23b may be used as a broadband input to any ANV control such as the FIR ANV Control described with reference to Fig. 8. Similarly, the broadband output from the parametric ALE 69m described in Fig. 10 may be the broadband input to an FIR control similar to that described with reference to Fig. 12. Finally, the broadband output from the adaptive inverse ALE 80p at 23p (Fig. 10) may be used as an input to an ANV control, such as the control process described with reference to Fig. 13.
Fig. 15 represents a tonal feedforward ANVC system 50t including multiple ALEs 54t and 54t' providing multiple signals to the ANV control. The ANVC system 50t generally represents a MIMO system for controlling multiple tones emanating from the source 58t which produce unwanted noise or vibration at a point of interest, for example within an aircraft cabin. In this embodiment, the reference sensor 52t picks up the disturbance signal containing therein the multiple tones to be controlled. The signal is band-pass filtered to separate into two frequency ranges, fr1 and fr2, the signals containing the tones of interest. For example, BPF 53t passes the lower range of frequencies and BPF 53t' passes only the upper range of frequencies.
The ALEs 54t and 54t' in this embodiment are IIR ALEs as described with reference to Fig. 8 and are arranged in a parallel relationship. The ALEs 54t and 54t' may also be low-order IIR, FIR, parametric, or adaptive inverse ALEs. Low-order implies they each have only a low number of taps. ALE 54t passes a lower frequency tone, for example, a signal indicative of the N1 engine rotation frequency of an aircraft engine to the ANV control 58t. Similarly, ALE 54t' passes a higher frequency tone, for example, a signal indicative of the N2 engine rotation frequency of an aircraft engine to the ANV control 58t. The ANV control process 58t then drives output transducer(s) 60t responsive to the error signals from the error sensor(s) 62t to preferably cancel the unwanted tones present at the point of interest that are caused by the source 48t. It should be understood that in the tonal case, the ALEs 54t and 54t' reject the uncorrelated broadband noise within each frequency range of operation fr1 and fr2 and thereby provide an enhanced signal representative of the tones of interest to the ANV control 58t.
Fig. 16 represents a tonal feedforward ANVC system 50v including a high-order FIR ALE 54v providing a signal to the ANV control 58v. By the term high-order it is meant that the FIR filter therein has a high number of taps.
Fig. 17 represents a tonal feedforward ANVC system 50w including a pair of cascaded ALEs 54w and 54w' providing reference signals to the ANV control 58w. In this embodiment, the first ALE 54w is used to enhance only the first tone of interest, while the second tone is enhanced by the second ALE 54w'.
Fig. 18 represents a tonal feedforward ANVC system 50z including an ALE 54z for providing a clean signal to the ANV control 58z. In this embodiment, the output from the ALE 54z is also used as an input to an auxiliary component 90z. For example, the output from the ALE 54z may be used in an auxiliary component 90z such as an Engine Vibration Monitor (EVM) including a peak detect 92z, signal processing means 94z, and display means 96z.
Fig. 19 and Fig. 20 represent the raw reference signal 51m from the reference sensor 52m and the filtered output from the BPF 53m, i.e., the ALE input 21m, and the tonal-enhanced ALE output 22m from the ALE 54m for the parametric embodiment of Fig. 12. The raw reference signal 51m provided from the reference sensor 52m is indicative of a reference signal (vibration) that is picked up by an accelerometer mounted on a jet engine. The engine has multiple vibrations, for example, at N1 and N2, which produce tonal noise within the aircraft's cabin. Present in the reference signal 51m is unwanted broadband background and some periodic noise 66m. It is desired to have a separated reference signal that has contributions only at the tones that are to be controlled. The cleaner (less broadband) the reference signal 51m the better, as any broadband noise present in the input to the FIR control filter 59m may be passed onto the output transducers 60m. The ALE input 21m represents the reference signal once it has been filtered through the band pass filter 53m.
In this case, the range of the band pass filter 53m is centered around the nominal of N1 and rejects broadband noise below and above its operating range. The ALE 54m further enhances the input signal 21m and produces an ALE output signal 22m which is significantly more indicative of the tone of interest, in this case N1. It should be understood that similar results may be obtained for the conventional FIR ALE of Fig. 1, the conventional IIR ALE of Fig. 2 and the adaptive inverse ALE of Fig. 11. Through the use of an ALE on the input path in a feedforward ANV control system, similar results can be achieved if one desires to remove the tones and leave the broadband noise as is described with reference to Fig. 14. This may be desirable for a system where the input is from a road wheel in a vehicle and broadband road noise is being controlled in the vehicle compartment or cabin.
In summary, the present invention is directed to a method and means for reducing unwanted background noise present in a reference (input) signal of a feedforward-type ANVC system. More specifically, an Adaptive Line Enhancer (ALE) is preferably placed in the input path of the ANVC system and enhances the reference signal by reducing broadband noise or tone(s) contained therein. The ALE may include IIR, FIR, parametric or adaptive inverse implementations, or the like. Further, the use of ALEs in the input path is beneficial to Active Noise Control (ANC), Active Structural Control (ASC), and Active Isolation Control (AIC) systems. In another aspect, multiple ALEs may be used in parallel or cascaded relationship and, further, the ALE output may be used as an input to auxiliary components, such as Engine Vibration Monitors (EVMs). EVMs are described in SAE paper 871732 entitled "The V-22 Vibration, Structural Life, and Engine Diagnostic System, VSLED" by M. J. Augustin and J. D. Phillips.
While the preferred embodiments of the present invention have been described in detail, various modifications, additions, alternatives, changes, and adaptations to the aforementioned may be made without departing from the spirit and scope of the present invention defined in the appended claims. It is intended that all such modifications, additions, alternatives, changes, and adaptations be considered part of the present invention.

Claims

A method of enhancing a reference signal in an active noise or vibration control system, comprising the steps of:
(a) receiving said reference signal from a reference sensor for providing a reference signal indicative of a disturbance, said reference signal including uncorrelated noise therein,

(b) reducing the noise included within said reference signal by adaptive gradient descent means,

(c) providing an error signal indicative of one selected from a group consisting of a residual noise and a residual vibration,

(d) processing said error signal and said reference signal and producing an output signal, and

(e) driving an output transducer based upon said output signal.
A method of claim 1 wherein adaptive gradient descent means for reducing said uncorrelated noise present in said reference signal is an Adaptive Line Enhancer (ALE) including an adaptive filter and update means.
A method of claim 2 wherein said ALE removes said uncorrelated noise which is at least one tone present in said reference signal.
A method of claim 2 wherein said ALE removes said uncorrelated noise which is broadband noise present in said reference signal.
A method of claim 2 wherein said adaptive filter within said ALE is a IIR filter or an FIR filter.
A method of claim 2 wherein coefficients of said adaptive filter within said ALE are modified according to said update means which includes an adaptive algorithm.
A method of claim 6 wherein said adaptive algorithm is an algorithm selected from a group consisting of a Least Means Square (LMS) algorithm, a Recursive Least Squares (RLS) algorithm, and a Generalized Minimum Variance (GMV) algorithm.
A method of claim 2 wherein said ALE is one selected from the group consisting of a conventional IIR ALE and a conventional FIR ALE.
A method of claim 2 wherein said ALE is an adaptive inverse ALE or a parametric ALE.
A method of claim 2 wherein said adaptive means for reducing said uncorrelated noise present in said reference signal is comprised of multiple Adaptive Line Enhancers (ALE).
An active noise or vibration control system of claim 10 wherein said multiple Adaptive Line Enhancers (ALE) are arranged in one selected from a group consisting of a cascaded relationship and a parallel relationship.