Radar modelling

It all started almost 60 years ago when P. Spencer, studying high-power microwave sources for radar applications, observed the melting of a chocolate bar in his pocket at least that is the story told. The first patent in this field was filed by him in 1946 and one year later the first commercial microwave oven appeared on the market. We had to wait until 1955 for domestic models, but by 1976 almost 60% of US households already had a microwave oven. 

Bistatic clutter is a poorly understood branch of radar and few measurements have been undertaken to help develop useful models. This is a subject that requires further and quite urgent research. 

John Maher, Mark E. Davis, Robert J. Hancock, and Sidney W. Theis, High Fidelity Modeling of Space-Based Radar, 2003 IEEE Radar Conference, Huntsville, AL, pp. 185-191, May 2003. 

S. U. Pillai, B. Himed, K. Y. Li, Modeling Earth s Rotation for Space Based Radar, Asilomar Conference on Signals, Systems, and Computer, Pacific Grove, CA, Nov 7-10, 2004. 

SM-1) A method of estimating the current (that is at the time of transmission of next pulse) state of the environment. This is done on the basis of prior measurements together with some model of the dynamics of the environment. It may be important to estimate not only the scatterers of interest (targets) but also those that are not of interest (clutter), since knowledge of the latter may be useful for determination of an optimal radar mode. 

The general objective of all radar detection procedures is to get a constant false alarm rate (CFAR) due to the fact that the test cell almost always contains clutter and noise and only in a very few cases contains radar target echo signals. The statistical model and general detection procedure, in which the detector is fixed only with regard to the noise and clutter statistic and independently to the target statistic, has been developed by Neyman and Pearson. 

But in real radar applications the average noise and clutter power level (/x) is unknown and must be estimated in the detection procedure first. This is done by several published CFAR procedures, which will be discussed in this section, where each specific CFAR technique is motivated by assumptions about a specific background signal or target signal model. 

The Stepperider tore across the drifts, its monstrous wheels tossing up clouds of enough snow to fuel a small blizzard, an irrelevant detail ignored by the simulation. For a long hour the computer-modelled landscape had shown a featureless expanse of uneven layers. A bank of micropower radars was constantly updating the vehicle s map of the immediate world. Now, they registered a few trees, with which the computer dotted the synthetic horizon. 

CliC CLIVAR CLRTAP CM CO COADS COLA COP CORP CPI CPL CPR CRC CRF CRP CRS CSD CSIRO Climate and Cryosphere (CliC) project CLImate VARiability and predictability Convention on Long-Range Transboundary Air Pollution Climate Model Carbon monoxide Comprehensive Ocean-Atmosphere Data Set Center of the Ocean-Land-Atmosphere system study Conference of the Parties Chinese Ozone Research Program Consumer Price Index Cloud Physics Lidar Continuous Plankton Recorder program Chemical Rubber Company Cloud Radiative Forcing Conservation Reserve Program Cloud Radar System Commission on Sustainable Development Commonwealth Scientific and Industrial Research... 

Where effects are known to be dependent on pulsed exposures, and the temporal nature of the exposures is modeled or measured, the exposures can be characterized using a tool such as the Risk Assessment Tool to Evaluate Duration and Recovery (RADAR), developed as part of the efforts of ECOFR AM (ECOFRAM1999 Reinert et al. 2002). This tool provides information on pulse magnitude, duration, and interpulse interval, which is particularly useful for assessing likely effects on classes of organisms with known recovery times and time-exposure responses. 

M. Soumekh, A system model and inversion for synthetic aperture radar imaging, IEEE Transactions on Image Processing, vol. 1, PP- 64—76, 1992. 

Figure 10.3 Radar plots of (left) and asp (right) values for sertindole s DRY, O, and N1 interactions with each of the seven TM regions in amine GPCRs. The plots are based on the same PCM model as in Figure 10.2. As seen, the asp values (left) do not discriminate very clearly between the different receptor regions. However, the asp values (right) reveal that distinct interaction types and TM regions are responsible for selectivity, the DRY-TM2, DRY-TM6, and DRY-TM7 interactions having the largest contributions. (Reproduced from Mol. Pharm. 2002, 67, 1465-1475 by courtesy of the American Society for Pharmacology and Experimental Therapeutics).

Several proposals have been made for the chemistry and minerals involved. The loaded dielectric model uses inclusions of iron sulfides, or other high dielectric minerals, in rocks. Higher amounts of the dielectric minerals produce lower radar emissivities and higher dielectric constants. The iron sulfides, or other dielectric minerals, are destroyed by chemical reactions with Venus atmosphere. The reactions proceed faster at lower elevations, where the temperatures are higher, so the lowest radar emissivity regions are predicted at the highest elevations, or in areas with the youngest volcanic rocks. 

ChilshomA. J. (1973) Alberta hailstorms 1. Radar case studies models. Meteorol. Monogr. 14, 37—95. 

By the 1940s, upper-level measurements of pressure, temperature, wind and humidity clarified more about the vertical properties of the atmosphere. In the 1950s, radar became important for detecting precipitation over a remote area. Also in the 1950s, with the invention of the computer, weather forecasting became not only quicker but also more reliable, because the computers could solve the mathematical equations of the atmospheric models much faster. In 1960, the first meteorological satellite was launched to provide 24-hour monitoring of weather events worldwide. 

Localized quasi-linear inversion increases the accuracy and efficiency of wave-field data interpretation because it is based on a much more accurate forward modeling solution than the Born approximation, used in the original Bleistein method. An example of successful application of the localized QL approximation in radar-diffraction tomography can be found in (Zhou and Liu, 2000). 

Pedley, H.M., Hill, I., Denton, P. Brasington, J. (2000) Three-dimensional modelling of a Holocene tufa system in the Lathkill valley, N. Derbyshire, using ground penetrating radar. Sedimentology 47, 721-735. 

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