System and method for stochastic characterization of a signal with four embedded orthogonal measurement data items

#(1) Field of the Invention

#The invention relates generally to the field of systems and methods for performing digital signal processing operations in connection with signals and more particularly to systems and methods for characterizing signals to determine their stochastic properties, that is, to determine whether they are random. More particularly, it relates to a system for performing this characterization function upon signals having embedded therein four-items of mutually orthogonal measurement information. If the signals are random, they may be determined to constitute noise. If the latter is the case then the principal function of the overall signal process (e.g., extraction of the information component) and its associated computational burden can be avoided. Stated another way, the system and method allows a determination to be made of the extent to which a pattern of data items, or sample points, representing four dimensions of measurement information conforms to a random structure of data.

#(2) Description of the Prior Art

#This invention is pointed out with particularity in the appended claims. The above and further advantages of this invention may be better understood by referring to the following description taken in conjunction with the accompanying drawings, in which:

#FIG. 1 is a functional block diagram representing a signal processing system which processes four items of mutually orthogonal measurement information, and which includes a stochastic characterization subsystem constructed in accordance with the present invention;

#FIGS. 2A and 2B together comprise a flow chart depicting the operations of the system depicted in FIG. 1; and

#With reference to FIG. 1, a signal processing system 10 includes a noise likelihood determination sub-system 11 constructed in accordance with the invention. The signal processing system 10 includes, in addition to the noise likelihood determination sub-system 11, an input sub-system 12, an information processing and decision sub-system 13 and an output 14. Input sub-system 12 includes one or more analog transducers, and performs a front end processing function that provides a digital output signal which represents four mutually orthogonal items of measurement information (which typically are in the form of a time-multiplex message format in which three items of measurement data other than clock-based time are multiplexed signals, and clock-based time or time interval measurements are embedded in the timed relationships involved in the operation of the multiplex system). The transducer receives the signal, which is in acoustic, electrical, electromagnetic or other form and converts it to preferably digital form for processing. For example, sub-system 12 may be embodied as submarine borne sonar array transducer equipment including a front end processing stage for feeding digital data to sub-system 13 embodied as contact localization and motion analysis (CLMA) modules of the submarine's combat control system embedded as programs in programmed digital computer equipment. The signal provided to such a CLMA sub-system 13 comprises three orthogonally related sensor measured items, described in detailed later herein, and clock-based time and/or time interval measurement information, all transmitted via a multiplex system as described above. This information is derived from a passive sonar acoustic signal which emanates as emission from a sonar contact and which is received by one or more analog transducer arrays, including a linear transducer array towed behind the submarine. Such input sub-system 12 may process the received acoustic signal to provide a multiplexed digital output of items of data (sometimes hereinafter and in the appended claims referred to as "sample points" or simply "points") comprised of signal components representative of (i) signal power in a sector of conical angle representing an actual physical spatial relationship to the contact, (ii) signal power in a sector or "frequency bin" of the spectral density distribution function of the acoustic signal, (iii) signal-to-noise-ratio (SNR), and (iv) clock time measurement data related to the sampling process which generated the other measurement data. The information processing and decision sub-system 13 performs signal processing operations, such as adaptive and other filtering, to extract the information components from the digital signal. In accordance with the invention, the noise likelihood determination sub-system 11 determines the likelihood that the signal is solely noise, and also provides an assessment of the degree to which the incoming signal is composed of noise. This information will determine whether sub-system 13 would provide a useful result, and may be used to control whether or not the signal from input sub-system 12 is applied to information and decision sub-system 13.

#The operations performed by the noise likelihood determination sub-system 11 will be described in connection with the flowcharts in FIGS. 2A and 2B. Basically, the noise likelihood determination sub-system 11 performs several tests in connection with digital signal sample points. Each digital signal sample point, or simply "point", within each population comprises one of a series of composite digital signals, with each composite signal containing components representing four mutually orthogonal items of measurement information. For example, the sample point may be in the form of a multiplexed message containing three sensor-based information components and also conveying the fourth clock-based information. Each sample point is generated in a symbolic four-dimensional aperture defined, for example, by a selected repetitive interval of time. In turn, each signal sample point is one of a series of such points in a selected population of "N" points. In the case of sub-system 13 embodied as CLMA subsystem of a submarine's combat system, the characteristic of mutual orthogonality of the four items of measurement information is based upon the linear independence of the measurement parameters, as will be well understood by those of skill in the art. The series of symbolic spatial apertures used in generating the various populations may be overlapping or non-overlapping. FIG. 3 is a perspective view in which the round, black dots diagrammatically represents a sequence of digital data points, each representing a signal sample point taken at successive intervals in time. It will be appreciated that a four-dimensional symbolic spatial relationship cannot be literally depicted by in a drawing. However, FIG. 3 diagrammatically illustrates a system of measurement information items having four-dimensional linear independence, consisting of three-symbolic dimensions of sensor measured items and a fourth-dimension of a clock-based time measurement, t. The "t" axis (which in the perspective view of FIG. 3 is the bottom horizontal axis) represents clock time, and the location of a black dot sample point relative thereto represents the time at which the sample point was extracted from the signal. The time axis also indicates beginning and end times of the interval of time which generates the hyperspatial aperture. Clock time constitutes one of four mutually orthogonal items of measurement information diagrammatical depicted in FIG. 3. The "x" axis (horizontal axis in the perspective view portion of the Figure) provides a Cartesian representation of the relationship of another of the four mutually orthogonal items of measurement information. The "y" axis (axis perpendicular to the hyperplane of the "t" and "x" axis in the perspective view) provides a Cartesian representation of a third of four mutually orthogonal items of measurement information. The "z" axis perpendicular to the t-x-y hyperplane provides a Cartesian representation of a fourth of the four mutually orthogonal items of measurement information. Successive populations of "N" signal sample points data are represented by successive cubical volumes, or regions of symbolic three-dimensional hyperspace, in the perspective view portion of the Figure.

#With reference again to the flow chart of FIG. 2A, the noise likelihood determination sub-system 11 will initially record the digital values represented by the various sample points, such as those shown in FIG. 3, for analysis (step 100) and identify the number of populations of sample points to be analyzed (step 101).