[Radar signal simulation] Simulate radar signal fuzzy function based on matlab

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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system

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Content introduction

Radar technology is an important technology widely used in military and civilian fields. It can detect the position, speed and direction of a target by sending electromagnetic waves and receiving reflected signals. However, in practical applications, radar signals are usually affected by various interferences and noises, resulting in signal quality degradation and difficulty in accurately detecting targets. In order to solve this problem, people usually use simulated radar signal ambiguity functions to process and optimize the signal.

The simulated radar signal ambiguity function is a mathematical model that can be used to describe the effects of various interferences and noises that radar signals suffer during transmission. These interferences and noises include weather conditions, terrain, target shape and material, signal propagation paths, and more. Fuzzy functions can help us understand the characteristics and behavior of signals and can be used to optimize the performance of radar systems.

In radar signal processing, fuzzy functions are often used to filter and denoise signals. Filtering refers to improving the quality and reliability of signals by removing high-frequency noise and low-frequency interference from signals. Denoising refers to improving the signal-to-noise ratio and readability of the signal by removing random noise and interference from the signal. The fuzzy function can select appropriate filtering and denoising algorithms based on the characteristics of the signal and the type of interference, and the parameters can be adjusted and optimized as needed.

In addition to filtering and denoising, fuzzy functions can also be used for signal reconstruction and target recognition. Signal reconstruction refers to restoring the original shape and characteristics of the signal by processing and interpolating the signal. Target recognition refers to determining the type, location, speed and other information of the target by analyzing and comparing signals. Fuzzy functions can help us accurately reconstruct and identify signals, and can improve the target detection and tracking capabilities of radar systems.

In short, simulating radar signal ambiguity function is an important signal processing technology, which can help us optimize the performance of the radar system and improve the quality and reliability of the signal. In practical applications, we need to select an appropriate blur function based on the characteristics of the signal and the type of interference, and parameter adjustment and optimization need to be performed to achieve the optimal signal processing effect.

Part of the code

?</code><code>?</code><code>clear;clc;</code><code>close all</code><code>fs=100e6;</code><code> f0=0e6;</code><code>pw=20e-6;</code><code>bw=20e6;</code><code>delay_tao=5e-6;</code><code>t= -pw/2:1/fs:(pw/2-1/fs);</code><code>k=bw/pw;</code><code>sig=exp(j*2*pi*( f0 + k*t/2).*t);</code><code>N=length(t);</code><code>sig_mul=zeros(1,N*3);</code><code>sig_mul1=zeros(1,N*3);</code><code>sig_mul2=zeros(1,N*3);</code><code>?</code><code>sig_mul(1: N)=sig; % first signal</code><code>sig_mul1(1:N)=sig_mul(1:N);</code><code>?</code><code>delay = fix( delay_tao*fs); %Round towards zero</code><code>t1=t + delay_tao;</code><code>sig2=0.5*exp(j*2*pi*(f0 + k*t1/ 2).*t1);</code><code>sig_mul2(delay:delay + N-1)=sig2;</code><code>?</code><code>% sig_mul(delay:delay + N -1)=sig_mul(delay:delay + N-1) + 0.5*sig;% Add a second signal</code><code>sig_mul(delay:delay + N-1)=sig_mul(delay:delay + N -1) + sig2;% Add the second signal</code><code>% sig_mul=1*sig_mul + 0.01*(randn(1,N*3) + randn(1,N*3)*i); % Add white noise</code><code>?</code><code>coe=(sig').';</code><code>win = hamming(length(coe))';% Windowing, Hamming window, side lobe attenuation</code><code>coe=coe.*win;</code><code>result1=conv(coe,sig_mul);</code><code>fre=linspace (-fs/2,fs/2,N)/1e6;</code><code>time=linspace(-pw,pw*3,4*N-1)/1e-6;</code><code>?</code><code>figure(1)</code><code>subplot(311);</code><code>n=1:3*N;</code><code>t1=pw /N*n-pw/2;</code><code>plot(t1,real(sig_mul1));</code><code>xlabel('time/s')</code><code> ylabel('amplitude/v')</code><code>title('Echo 1')</code><code>ylim([-1.2 1.2])</code><code>? </code><code>subplot(312);</code><code>% t2=t1 + delay_tao;</code><code>plot(t1,real(sig_mul2));</code><code> xlabel('Time/s')</code><code>ylabel('Amplitude/v')</code><code>title(['Echo 2 delay',num2str(delay_tao) ,'s'])</code><code>ylim([-1.2 1.2])</code><code>?</code><code>subplot(313);</code><code> plot(t1,real(sig_mul));</code><code>xlabel('time/s')</code><code>ylabel('amplitude/v')</code><code>title('Echo 1 and Echo 2 are superimposed and mixed')</code><code>?</code><code>figure(2)</code><code>subplot(211),plot( fre,20*log10(abs(fftshift(fft(sig')))));</code><code>xlabel('frequency/MHz');</code><code>ylabel(' Power/dB');</code><code>title('Echo 1 spectrum')</code><code>subplot(212),plot(time,20*log10(eps + abs(result1 )));</code><code>xlim([min(time),max(time)])</code><code>ylim([-30 80])</code><code>xlabel(\ 'Time/us');</code><code>ylabel('Power/dB')</code><code>title('Time domain results after synthetic echo pulse compression processing') </code><code>?</code><code>?</code><code>?</code><code>?</code><code>x=-4:0.1:4; %shijian </code><code>y=-2:0.1:2; %pinlv</code><code>[X,Y]=meshgrid(x,y);</code><code>%%%%% %?nshu sheji%%%%%%%%%%%%%</code><code>N=5; %zimaichong geshu</code><code>T=0.2; %zimaichong kuandu</code><code>K=2; %pinlv bujin liang</code><code>Tr=0.8; %maijian kuandu</code><code>Z=zeros(size(X));</code><code>d= pi*Y*Tr + eps;</code><code>for p=-(N-1):(N-1)</code><code> ss=abs(X-p*Tr);</code> <code> b=zeros(size(ss));</code><code> c=zeros(size(ss));</code><code> [m,n]=size(ss);</code> code><code> for i=1:m</code><code> for j=1:n</code><code> if ss(i,j)>T</code><code> c(i ,j)=0;</code><code> else</code><code> b(i,j)=pi*(Y(i,j) + K*X(i,j))*(T -ss(i,j)) + eps;</code><code> c(i,j)=((sin(b(i,j))*(T-ss(i,j)))/( T*b(i,j)));</code><code> </code><code> end</code><code> end</code><code> end</code><code>? </code><code> f=sin((N-abs(p))*d);</code><code> fudu=f./sin(d);</code><code>Z=Z + abs(fudu).*abs(c);</code><code>end</code><code>Z=Z/N;</code><code>figure;</code><code>surf (X,Y,Z)

Operation results

References

[1] Gu Chen, Zhang Wenqing, Sun Li, et al. Application of MATLAB in the teaching of “Radar Signal Analysis and Processing” [J]. Science and Technology Innovation Herald, 2018, 15(24):4.DOI:CNKI:SUN: ZXDB.0.2018-24-091.

[2] Xie Hongze. Research on simulation analysis of radar signal fuzzy function[J]. Electronic Testing, 2015(7):3.DOI:10.3969/j.issn.1000-8519.2015.13.015.

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1 Improvements and applications of various intelligent optimization algorithms

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Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization

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Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization