To measure the detection probabilities for Radar Cross Section Data using beta Distribution.
Budget: $250 – $750 USD
I'm looking for a professional who can assist in measuring the detection probabilities of radar cross section (RCS) simulated data. Data is collected using CST software and RCS data is of a thin aluminum cylinder.
Aspect angle is from 0 to 360 degrees and bistatic angle is changed with 10 degree variation and RCS is collected on every bistatic angle. Then calculated beta distribution parameters for this RCS data.
From these parameters of beta distribution I have to measure the detection probabilities. This will be done by making a Confluent Hypergeometric Function of a characteristic function and then taking the inverse LaPlace transform of that function and after that integrating this inverse LaPlace function on a certain interval from zero to threshold voltage.
Specific values for Signal SNR, beta distribution parameters, Number of pulses, Probability of False alarm and Threshold voltage are provided. Both slow and fast fluctuation cases will be dealt with. We have to implement the detection probability part in MATLAB.
I would like to mention that it is not the Swerling cases. we can compare the results with Swerling cases for Chi-square distribution.
The research article is attached. Discussion on implementation part is welcomed.
Your expertise will be crucial in making sure that our detection probabilities are accurately measured.
Aspect angle is from 0 to 360 degrees and bistatic angle is changed with 10 degree variation and RCS is collected on every bistatic angle. Then calculated beta distribution parameters for this RCS data.
From these parameters of beta distribution I have to measure the detection probabilities. This will be done by making a Confluent Hypergeometric Function of a characteristic function and then taking the inverse LaPlace transform of that function and after that integrating this inverse LaPlace function on a certain interval from zero to threshold voltage.
Specific values for Signal SNR, beta distribution parameters, Number of pulses, Probability of False alarm and Threshold voltage are provided. Both slow and fast fluctuation cases will be dealt with. We have to implement the detection probability part in MATLAB.
I would like to mention that it is not the Swerling cases. we can compare the results with Swerling cases for Chi-square distribution.
The research article is attached. Discussion on implementation part is welcomed.
Your expertise will be crucial in making sure that our detection probabilities are accurately measured.