Estimation of Power Spectral Density by Filters

ABSTRACT

An algorithm is presented which differs from previous methods by being more adapted for searching over wide frequency ranges and which permits the estimation of spectral density as a function of time as well as of frequency. The algorithm may offer economy, especially on smaller computers. The algorithm is based on the analogy between a broadly tuned filter and a truncated cross correlation.

REFERENCES

• 1: BLACKMAN, R. B., AND TUKEY, J.W. The Measurement of Power Spectra, p. 7. Dover, 1958.
• 2: DAVENPORT, W. B., JR., AND POOT, W.L. Random Signals and Noise, p. 107. McGraw- Hill, 1958.
• 3: BLACKMAN, R. B., AND TUKEY, J.W. The Measurement of Power Spectra, p. 120--121. Dover, 1958.
• 4: MATHER, K. B., GAuss, E. J., AND CRESSWELL, G. R. Diurnal variations in the power spectrum of telluric currents at conjugate points. In preparation, 1963.
• 5: FLOCK, W. L., BELON, A. E., AND HEACOCK, R.R. Geomagnetic agitation and overhea aurora, Int. Conf. on the Ionosphere, Imperial College, London, July, 1962.
• 6: Cecil Hastings, Approximations for Digital Computers, Princeton University Press, Princeton, NJ, 1955
• 7: HERSHBRGER, W.D. Principles of Communication Systems, p. 18. Prentice Hall, 1955.
• 8: E. J. Gauss, A Comparison of Machine Organizations by Their Performance of the Iterative Solution of Linear Equations, Journal of the ACM (JACM), v.6 n.4, p.476-485, Oct. 1959 [doi>10.1145/320998.321001]

INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS

Additional Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.3 Enhancement
          Subjects: Filtering

General Terms:
Algorithms, Measurement, Performance, Theory