Abstract

When a beam emitted from an active monostatic sensor system sweeps across a volume, the echoes from scatterers present will fluctuate from ping to ping due to various interference phenomena and statistical processes. Observations of these fluctuations can be used, in combination with models, to infer properties of the scatterers such as numerical density. Modeling the fluctuations can also help predict system performance and associated uncertainties in expected echoes. This tutorial focuses on "physics-based statistics," which is a predictive form of modeling the fluctuations. The modeling is based principally on the physics of the scattering by individual scatterers, addition of echoes from randomized multiple scatterers, system effects involving the beampattern and signal type, and signal theory including matched filter processing. Some consideration is also given to environment-specific effects such as the presence of boundaries and heterogeneities in the medium. Although the modeling was inspired by applications of sonar in the field of underwater acoustics, the material is presented in a general form, and involving only scalar fields. Therefore, it is broadly applicable to other areas such as medical ultrasound, non-destructive acoustic testing, in-air acoustics, as well as radar and lasers.

Highlights

  • Echoes, as measured through the receiver of an active monostatic sensor system, will typically fluctuate from ping to ping as the beam emitted from the system scans across a volume containing scatterers or as the scatterers in that volume move through the beam (Fig. 1)

  • One convenient approach is to normalize the random variable by its root-mean-square value hx2i1=2 and plot the probability density function (PDF), cumulative distribution function (CDF), and probability of false alarm (PFA) versus the random variable divided by hx2i1=2, where hÁ Á Ái is the average over a statistical ensemble of values

  • Since ai in this case is observed through the receiver of the sensor system, the exponential PDF includes the effects of both fluctuations from the stochastic nature of the scatterer and the variability due to the scatterer being randomly located in the beam

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Summary

INTRODUCTION

As measured through the receiver of an active monostatic sensor system, will typically fluctuate from ping to ping as the beam emitted from the system scans across a volume containing scatterers or as the scatterers in that volume move through the beam (Fig. 1). The formulations are completely limited to (1) first-order statistics as described above, and are mostly limited to (2) narrowband signals that are long enough so that the echoes from the scatterers overlap significantly, (3) direct path geometries where there are no boundaries present, and (4) a homogeneous medium Beyond those limited scenarios, examples are presented of more complex cases involving pulsed signals in which the echoes from the scatterers only partially overlap and the presence of boundaries and/or heterogeneities, including waveguide effects. II–IV in a presentation of echo statistics formulations for a wide range of important physical scenarios These scenarios include beampattern effects associated with main lobes of various width, narrowband and broadband signals, completely overlapping echoes (long signal) and partially overlapping echoes (short signal), single scatterers and mixed assemblages of scatterers, elongated and randomized scatterers, and geometries involving either a direct path and a homogeneous medium or ones involving the presence of boundaries and/or heterogeneities. II–IV are given, as well as references to previously published papers

KEY ELEMENTS OF ECHO STATISTICS
Interfering signals
Stochastic scattering from a single scatterer
EXPLOITING ECHO STATISTICS FOR VARIOUS APPLICATIONS
Inferring information on scatterers
Inferring number of scatterers
Discriminating between echo from scatterer of interest and background
Removing beampattern effects to isolate properties of resolved scatterer
Discriminating between different types of aggregations of scatterers
Further considerations
Predicting error or uncertainty in signal magnitude
KEY EQUATIONS FOR SCATTERING AND ASSOCIATED STATISTICAL PROCESSES
Deterministic scattering
Single scatterer
Multiple scatterers
Definitions and equations
Calculating PDFs
Non-uniform spacing of bins
Randomizing the deterministic scattering equations
Function of a single random variable
Function of two random variables
Product of two random variables
Sum of random variables
IN-DEPTH TREATMENT OF ECHO STATISTICS
Arbitrary cases
Adding independent realizations of the complex signal A
Complex scatterers with stochastic properties
Point scatterer
Rayleigh scatterer
Randomized prolate spheroid
Accounting for beampattern effects in echo PDF
PDF of spatial distribution of scatterer
Beampattern PDF for 2D and 3D distribution of scatterers
Echo PDF for different types of individual scatterers in axisymmetric beam
Multiple identical scatterers randomly located in beam
Multiple scatterers of different types and sizes
Split aggregation of type A and B scatterers—mixture PDF
Interspersed aggregation of type A and B scatterers—coherent phasor sum
VIII. SYSTEMS AND ENVIRONMENTS WITH MORE COMPLEXITY
Some simple formulations
Closed form solutions for limiting cases involving a saturated waveguide
DISCUSSION AND CONCLUSIONS
Rayleigh PDF
Rice PDF
Weibull PDF
Log normal PDF
Findings
Generalized Pareto PDF
Full Text
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