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\chapter{References}
\begin{thebibliography}{1}
\bibitem {1}
Ridenour, Louis Nicot, ed. Radar system engineering. Vol.~1. Dover Publications, 1965.
\bibitem {2}
Kissinger, Dietmar. Millimeter-wave receiver concepts for 77 GHz automotive radar in silicon-germanium technology. Springer Science \& Business Media, 2012.
\bibitem {3}
Karnfelt, C. et al., “77 GHz ACC Radar Simulation Platform”, IEEE International Conferences on Intelligent Transport Systems Telecommunications (ITST), 2009.
\bibitem {4}
Rohling, H. and M. Meinecke., “Waveform Design Principle for Automotive Radar Systems”, Proceedings of CIE International Conference on Radar, 2001.
\bibitem {5}
A. Stove, “Linear FMCW radar techniques”, IEE Proceedings of Radar and Signal Processing, vol. 139, no. 5, pp. 343–350, 1992.
\bibitem {6}
I. V. Komarov and S. M. Smolskiy, Fundamentals of Short-Range FM Radar. Boston, MA: Artech House, 2003.
\bibitem {7}
Gardill, Markus. "Characterization and Design of Small Array Antennas for Direction-Of-Arrival Estimation for Ultra-Wideband Industrial FMCW Radar Systems." (2015).
\bibitem {8}
Merrill Skolnik, Introduction to Radar Systems, 3rd Edition, McGraw-Hill, 2001
\bibitem {9} \tt {http://www.yole.fr/} %Radar\_AutomotiveLandscape.aspx#.W1DrJS3Gzow
\bibitem {10} \tt {http://www.microwavejournal.com/}
\bibitem {11} \tt {https://www.microwaves101.com/}
\bibitem {20} \tt {https://www.ims2016.org}
% \\ Dissertation Link
\bibitem {30} \tt{ https://www.linkedin.com/in/maxdi}
% COMPANY WEBISTES
\bibitem {40} \tt {http://www.maxdi.com/} % MXD
\bibitem {50} \tt {http://www.cognitave.com/} %COGN
% COMPANY WEBISTES
\bibitem {412} \tt {https://www.instagram.com >> @maxdinyc} % MXD
\bibitem {40} \tt {https://www.instagram.com >> @maxdiinc} %COGN
\bibitem {40} \tt {https://www.instagram.com >> @maxdisystems} %COGN
\bibitem {40} \tt {https://www.tiktok.com/en >> @mxdnyc} % UGI - MXD STR %articles/29958-mwj-talks-adi-and-autoliv-about-advanced-automotive-radar-sensors-and-rides-in-test-car
\end{thebibliography}QLAP
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Input Database links..
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RF\&MW Simulation and Modelling
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%% Inout Data for DoA Estimation with ANN
% Cognitave, Inc.
% Programmer: Mahdi Haghzadeh, CEO
% Revisions:
% Date: 1/15/2018
%% Modeling the Received Array Signals
% Define a uniform linear array (ULA) composed of 10 isotropic antennas. The
% array element spacing is 0.5 meters.
N = 10;
ula = phased.ULA('NumElements',N,'ElementSpacing',0.5);
%%
% Simulate the array output for one incident signal. The signal is incident
% from 90∞ in azimuth. It's elevation angles is randomly generated. We assume
% that the directione are unknown and need to be estimated. Simulate the
% baseband received signal at the array demodulated from an operating frequency of 300 MHz.
fc = 76.5e9; % Operating frequency
fs = 8192; % Sampling frequency
lambda = physconst('LightSpeed')/fc; % Wavelength
pos = getElementPosition(ula)/lambda; % Element position in wavelengths
%rs = rng(2012); % Set random number generator
randAng = randi(90)
ang1 = [randAng, 0]; % Direction of the signals
angs = ang1;
Nsamp = 1024; % Number of snapshots
noisePwr = 0.01; % Noise power
signal = sensorsig(pos,Nsamp,angs,noisePwr);
%%
% Because a ULA is symmetric around its axis, a DOA algorithm cannot uniquely
% determine azimuth and elevation. Therefore, the results returned by these high-resolution
% DOA estimators are in the form of broadside angles. An illustration of broadside
% angles can be found in the following figure.
%
%
%
% Calculate the broadside angles corresponding to the two incident angles.
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