Multistage median cascaded canceller
First Claim
1. In a Multistage Weiner Filter having an analysis section and a synthesis section, the filter including:
- a main input channel for receiving a main input signal;
an auxiliary input channel for receiving an auxiliary input signal; and
a processor;
the improvement comprising the processor includes an algorithm for generating a data adaptive linear transformation followed by computing an adaptive weighting wmed of the data and for applying the computed adaptive weighting wmed to a function of the main input signal and the auxiliary input signal to generate an output signal, and wherein the adaptive weighting wmed comprises a sample median value of the real part of the ratio of a main input weight training data signal to an auxiliary input weight training data signal.
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Abstract
An adaptive signal processing system utilizes a Multistage Weiner Filter having an analysis section and a synthesis section that includes a processor. The processor includes an algorithm for generating a data adaptive linear transformation, computing an adaptive weighting wmed of the data, and applying the computed adaptive weighting wmed to a function of a main input signal and an auxiliary input signal to generate an output signal. A plurality of building blocks in a Gram-Schmidt cascaded canceller-type configuration sequentially decorrelate input signals from each other to produce a single filtered output signal. Each building block generates an adaptive weight wmed that is applied to generate a local output signal. The effect of non-Gaussian noise contamination on the convergence MOE of the system is negligible. In addition, when desired signal components are included in weight training data they cause little loss of noise cancellation.
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Citations
28 Claims
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1. In a Multistage Weiner Filter having an analysis section and a synthesis section, the filter including:
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a main input channel for receiving a main input signal; an auxiliary input channel for receiving an auxiliary input signal; and a processor; the improvement comprising the processor includes an algorithm for generating a data adaptive linear transformation followed by computing an adaptive weighting wmed of the data and for applying the computed adaptive weighting wmed to a function of the main input signal and the auxiliary input signal to generate an output signal, and wherein the adaptive weighting wmed comprises a sample median value of the real part of the ratio of a main input weight training data signal to an auxiliary input weight training data signal. - View Dependent Claims (2, 3, 4, 5)
where K is the number of weight training data samples, z is the main input signal, j is a unit imaginary number, and x is the auxiliary input signal.
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4. A Multistage Weiner Filter as in claim 3, wherein the output signal, r, is obtained from the equation:
r=z−
w*med x.
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5. A Multistage Weiner Filter as in claim 4, wherein the processor includes a reduced rank truncation to decrease training data requirements and reduce processing time.
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6. In a Multistage Weiner Filter for receiving a plurality of input signals corresponding to a common target signal and for sequentially decorrelating the input signals to cancel the correlated noise components therefrom, the filter having an analysis section and a synthesis section, and the filter further including:
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a data adaptive linear transformation to apply to an input data vector; a plurality of building blocks arranged in a cascaded configuration for sequentially decorrelating each of the input signals from each other of the input signals to thereby yield a single filtered output signal; wherein each building block includes; a local main input channel which receives a local main input signal, a local auxiliary input channel which receives a local auxiliary input signal, and a processor; the improvement comprising the processor includes an algorithm for calculating an adaptive weighting wmed, and generating a local output signal, utilizing the adaptive weighting wmed. - View Dependent Claims (7, 8, 9, 10)
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11. An adaptive signal processing method, comprising the steps of:
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receiving a plurality of input signals corresponding to a common target signal; transforming the input signals to a data stream; applying the data stream to a steering vector to commence processing with non-adaptive calculations by forming an initial estimate of the desired signal; isolating the rest of the data by applying the transform x0(k) =B x(k), where B is a blocking matrix for finding a projection of the data orthogonal to the steering vector; applying the initial data and the orthogonal projection of the data to at least one adaptive stage processor, said applying including; determining the projection of the data in the direction most correlated with the desired signal; determining the orthogonal projection to the direction most correlated with the desired signal; applying said projections to the data; inputting the input signals into a plurality of building blocks arranged in a cascade configuration for sequentially decorrelating each of the input signals from each other of the input signals; in each building block, calculating an adaptive weighting wmed and generating a local output signal, utilizing the adaptive weighting wmed; and generating a single filtered output signal. - View Dependent Claims (12, 13, 14, 15, 16, 17)
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18. A multistage median cascaded canceller, comprising:
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a means for receiving a plurality of input signals corresponding to the same target signal; a means for transforming the input signals to a data stream; a means for applying the data stream to a steering vector to commence processing with non-adaptive calculations by forming an initial estimate of the desired signal; a means for isolating the rest of the data by applying the transform x0(k)=B x(k), where B is a blocking matrix for finding a projection of the data orthogonal to the steering vector; a means of determining and applying a data adaptive linear transformation via a cascade of orthogonal subspace projections to create an efficient input data transformation; a means for applying the initial data and the orthogonal projection of the data to at least one adaptive stage processor, said adaptive stage processor including a plurality of building blocks arranged in a cascade configuration for sequentially decorrelating each of the input signals from each other of the input signals; and a means for generating a single filtered output signal; and wherein each building block includes a means for receiving a local main input signal, a means for receiving a local auxiliary input signal, and a processing means for calculating an adaptive weighting wmed and generating a local output signal, utilizing the adaptive weighting wmed. - View Dependent Claims (19, 20, 21, 22)
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23. A multistage median cascaded canceller for receiving a plurality input signals corresponding to a common target signal and for sequentially decorrelating the input signals to cancel the correlated noise components therefrom, comprising:
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an analysis section; a synthesis section; a processor configured for; applying the data stream to a steering vector to commence processing with non-adaptive calculations by forming an initial estimate of the desired signal; isolating the rest of the data by applying the transform x0(k)=Bx(k), where B is a blocking matrix for finding a projection of the data orthogonal to the steering vector; applying the initial data and the orthogonal projection of the data to at least one adaptive stage processor, said adaptive stage processor including a plurality of building blocks arranged in a cascade configuration having N input channels and N-1 rows of building blocks, for sequentially decorrelating each of the input signals from each other of the input signals to thereby yield a single filtered output signal; wherein each building block includes; a local main input channel which receives a local main input signal, a local auxiliary input channel which receives a local auxiliary input signal, and wherein the processor is further configured to calculate an adaptive weighting wmed and generate a local output signal, utilizing the adaptive weighting wmed ; and wherein an end building block supplies the local output signal to a separate local output channel for follow on processing. - View Dependent Claims (24, 25, 26, 27, 28)
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Specification