Target model pitfalls (Illness, diagnosis, and prescription)

Johnston, S.L.

doi:10.1109/7.588498

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…It has been suggested that proper target models should consist of three submodels: RCS data, a motion model, and a glint model [24]. This work advances research on how to validate proposed motion models for the purpose of creating more realistic radar simulators.…”

Section: Azmentioning

confidence: 98%

Empirical Study of Flight-Dynamic Influences on Radar Cross-Section Models

Persson

Bull

2016

Journal of Aircraft

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In this work, measurements and a method for analyzing flight-dynamic effects on radar cross-section models for aircraft are presented. Flight-dynamic effects need to be considered when designing combat aircraft and creating target models for radar simulators. The work is based on flight data from three different types of aircraft: Piper PA-28 Archer II, Boeing 737, and Saab JAS 39 Gripen. Using inertial navigation and global-positioning systems, the motions of the three aircraft are recorded in flight. From the data, aspect angles toward a radar station located in the extension of the intended flight path are generated using a simulator. It is found that the major contribution to perturbations in aspect angles is due to the rotational degrees of freedom and that bivariate normal distributions are a good candidate for approximating the uncertainty in aspect angles for all three aircraft types. It is also found that each rotational degree of freedom is close to a normal distribution but that the parameter values of the distribution vary with altitude and aircraft type. NomenclatureAz = azimuth aspect angle, deg a = major axis of confidence ellipse, deg b = minor axis of confidence ellipse, deg El = elevation aspect angle, deg E i = incident electric vector field, V∕m E s = scattered electric vector field, V∕m G = antenna gain h r = radar antenna height, m h t = target altitude, m P t = transmitted power, W R = range between radar and target, m R h = range to radar horizon, m R max = maximum detection range, m r e = effective radius of Earth, m S min = minimum detectable signal, W α = alpha risk θ = eigenvalue of Σ λ = radiation wavelength, m σ = radar cross-section, m 2 μ = mean value, deg ρ = correlation coefficient Σ = covariance matrix σ Az = azimuth standard deviation, deg σ El = elevation standard deviation, deg χ 2 2 α = upper αth percentile of chi-squared distribution with two degrees of freedom

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Section: Azmentioning

confidence: 98%

Empirical Study of Flight-Dynamic Influences on Radar Cross-Section Models

Persson

Bull

2016

Journal of Aircraft

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“…But all these models are meant to estimate a radar system's detecting performance, not the stealth performance of an aircraft target. If those models are directly used on a specific aircraft, the process is somehow tedious and may cause great errors in model building (Johnston 1997).…”

Section: Introductionmentioning

confidence: 99%

Calculation of Aircraft Target’s Single-Pulse Detection Probability

Yue¹,

Chen

Shu³

2015

J.Aerosp. Technol. Manag.

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The original radar cross section data or some rough models are often used to estimate a given aircraft target detection probability. The calculation results may not be very accurate as targets are different from one another and the real radar detection process is complex. A new method for radar cross section model generation is proposed and it takes the random factors like air turbulence into account; this makes it conform to the reality. In addition, this radar cross section model can be directly applied to the radar detection process to calculate the detection probability of a specific aircraft at any attitude. Four typical aerial vehicles are taken as examples to demonstrate this method and information such as detection probability, signal to noise ratio and detection distance. Target's instantaneous probability of being tracked, which corresponds to target's detection probability, can also be calculated. Using these calculation results, we can compare two different aircrafts' stealth performance in detail or optimize an aircraft's flight path.

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“…In Figure 13.1 2-D cross-correlation results for seven diiFerent cases. (75,250), (50,280), (100,280) solely from the images. In fact, the images do not get blurred even when Ur deviates by over 20%.…”

Section: Simulation Examplesmentioning

confidence: 99%

“…'Recommeadations are made for target models which, the author claimed, "Nearly approach the real world" in[50],…”

mentioning

confidence: 99%

Synthetic aperture radar imaging of embedded objects in dielectric slabs

Zhang

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This is to certify that the Doctoral dissertation of Geoffirey J. Zhang hcis met the dissertation reqviirements of Iowa State University Major Professor For the Major Program For the Graduate College Signature was redacted for privacy. Signature was redacted for privacy. Signature was redacted for privacy. iii I would like to dedicate this Ph.D. dissertation to Professor Yushi Sun. Without his valuable help I would not have been able to further my graduate studies here at Iowa State University.

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Target model pitfalls (Illness, diagnosis, and prescription)

Cited by 8 publications

References 5 publications

Empirical Study of Flight-Dynamic Influences on Radar Cross-Section Models

Empirical Study of Flight-Dynamic Influences on Radar Cross-Section Models

Calculation of Aircraft Target’s Single-Pulse Detection Probability

Synthetic aperture radar imaging of embedded objects in dielectric slabs

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