Data processing: measuring – calibrating – or testing – Measurement system – Measured signal processing
Reexamination Certificate
2000-08-09
2003-08-26
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Measured signal processing
C702S190000, C324S614000
Reexamination Certificate
active
06611794
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Technical Field
The present invention relates generally to apparatus and methods for processing information-bearing signals corrupted by noise. More particularly, the present invention relates to a signal processing apparatus and method which can be used to correct signal amplitude level variations, restoring them to receiver optimal design levels.
2. History of Related Art
Chaotic processes applied to information-bearing signals have properties that provide low probability of intercept (LPI) and low probability of detection (LPD), making them a natural choice as the foundation of communications systems. Chaotic signals have the prima facie appearance of noise for LPD, extreme sensitivity to initial conditions for LPI, and dynamics rendering receivers relatively intolerant of signal amplitude variations.
Communications systems based on chaotic processes have improved significantly in the decade since chaotic synchronization was first achieved. Several methods of synchronization combine with various modulation techniques to produce secure communication using signals in analog and digital form (e.g. Chua's circuit and chaotic maps, respectively). However, since chaotic processes are extremely sensitive to signal amplitude variation, chaotic receivers tend to lose synchronization and data recovery capabilities at variation levels as small as +/−3 dB. Thus, the inability to communicate beyond shouting distance has kept chaos-based communications systems in the laboratory.
There has been some work directed toward addressing this “distance problem”, such as Tom Carroll's customized chaotic process that achieves amplitude-independent synchronization (see “Amplitude Independent Chaotic Synchronization and Communication,” in
Chaotic Circuits for Communication
, Proceedings of the SPIE 2612, pp. 181-188, 1995). The need to customize chaos to achieve some degree of amplitude variation tolerance is a restrictive requirement, though, and an apparatus or method that works with an arbitrary or unrestricted chaotic process is highly desirable.
Since all communication systems are susceptible to interfering signals normally referred to as noise, the problem of chaotic signal reception can become even more difficult. Interfering signals may have harmful effects on the performance of any communication system, depending on the specific system being used, the nature of the noise, and the way the noise interacts with the signal. The magnitude of these effects is also determined by the relative intensity of the noise compared to the signal, which is usually measured as the signal-to-noise-ratio (SNR), or the ratio of the power of the signal to the power of the noise. Such effects are magnified with respect to chaotic systems, given the extreme sensitivity to signal level variation.
Communications receivers, which demodulate a signal to recover its information content, employ demodulation techniques whose designs are based on the statistics of both the signal and the corrupting noise. This approach has its roots in the Shannon channel capacity theorem, which states that channel capacity is a function of SNR. There are a variety of methods for the recovery of data corrupted by random noise, including maximum likelihood (ML) decision generators, also known as maximum a priori detectors; maximum a posteriori (MAP) detectors; minimum Euclidean distance calculators; correlation receivers; cross-correlation detectors; and matched filter detectors. While all techniques derive from the noise statistics, not all algorithms explicitly calculate noise power or SNR. Estimates of the noise power are typically used to derive SNR in order to estimate the quality of the receiver output; and they sometimes aid in the data recovery process, as with critical applications employing the MAP detector. Two examples of quality metrics calculated from SNR estimates are (1) a bit error rate (BER) and (2) a GPS navigation solution accuracy approximation.
Prior techniques of calculating SNR have involved various methods of estimating noise power and signal power individually, and dividing them to find the signal-to-noise ratio. For example, a binary phase-shift-key modulation technique or a direct sequence spread spectrum chipping code utilize a voltage for a logical one, and the negative of the voltage for a logical zero at baseband. Subtraction of sequential values is one method of estimating the noise content of such a received signal, and squaring the estimated noise voltages yields the noise power. Chaotic processes, however, associate a logical state with a noise-like sequence of values, rather than a single voltage level. This deterministically random characteristic of chaotic signals precludes the use of prior noise estimation methods, because the continuum of values corresponding to a logical state is not amenable to noise estimation methods that are based on the association of a constant quantity with a logical state. As a result, estimates of the individual signal power and noise power could not be made by conventional techniques.
The design of a new chaotic receiver utilizing a MAP detector requires knowledge of the noise statistics, specifically mean and power, so as to construct a window to superimpose on the chaotic transmit Probability Density Function (PDF). Investigation of the received PDF yielded observations of a relationship between received values and SNR. This relationship was developed and exploited to enable the estimation of SNR directly from received values, without the need to first estimate the individual signal and noise power values, solving the implementation dilemma for using a MAP detector with a chaotic receiver operating in a lossless channel.
Chaotic systems, however, have been observed to lose synchronization with as little as +/−3 dB amplitude variation, and so are intolerant of the propagation losses and processing losses and/or gains that are integral to real communications systems operating in lossy channels. The new SNR estimation method had no means of separating the signal power from the noise power. The receiver design was, therefore, relegated to the same status of laboratory curiosity as the other chaotic designs.
Thus a method and apparatus which enables the isolation of the message signal power from the noise power in a received signal, estimating the received message signal power deviations from receiver design values, generating a correction factor, and scaling the received signal to the proper level for optimal receiver operation would be highly beneficial. Such a method and apparatus would accurately adjust for signal attenuation and amplification via the correction factor so as to restore received signals to optimal levels, even in the face of amplitude deviations due to transmitter amplification and propagation losses. Even more beneficial would be the provision of a generalized technique, usable with receivers of linear, nonlinear, and/or nonlinear chaotic design. Such a method and apparatus would be most useful if the only a priori knowledge required to accomplish signal amplitude restoration were a transmit signal probability density function (PDF) and the optimum receiver design operational power level. Since the SNR is typically calculated in many communications applications, the apparatus and method might also make use of this ratio for estimating results such as message recovery fidelity, circular error probability for global positioning satellite systems (GPS), and receiver-feedback transmit power level control.
SUMMARY OF THE INVENTION
The apparatus of the present invention for signal amplitude restoration, having a received signal input and a scaled received signal output, includes an amplitude correction factor generator which has an estimated signal-to-noise power ratio input and a received signal input; a variable gain amplifier which uses the correction factor generator output to control its gain, and which amplifies or attenuates the received signal input to provide the scaled received signal o
Baker & Botts L.L.P.
Baran Mary Catherine
Hoff Marc S.
Southwest Research Institute
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