Implementing pulse pressure of 16 pulse signals and moving target display moving target detection MTIMTD 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

In radar systems, pulse signal processing is very critical. By processing pulse signals, we can realize functions such as pulse pressure, moving target display, and moving target detection. This article will focus on the pulse pressure, moving target display and moving target detection technology of 16 pulse signals, namely MTIMTD.

Pulse pressure technology of pulse signals is a method to improve the range resolution of radar systems by increasing the width of pulse signals. The larger the width of the pulse signal, the narrower its spectrum and the higher the distance resolution. Pulse pressure technology for pulse signals can be implemented in a variety of ways, the most commonly used of which are linear frequency modulation (LFM) and phase coding (PC).

In the pulse pressure technology of pulse signals, linear frequency modulation (LFM) is a common implementation method. By introducing linear frequency changes in the pulse signal, the bandwidth of the signal can be extended to a wide range, thereby improving the range resolution of the radar system. The main advantages of linear frequency modulation (LFM) are simple implementation and low cost, so it is widely used in many radar systems.

In addition to the pulse pressure technology of pulse signals, moving target display is also an important radar signal processing technology. The moving target display can superimpose multiple pulse signals received by the radar on a time-distance diagram to form a dynamic target display. Through the moving target display, we can clearly observe the target’s movement trajectory and speed information, thereby better understanding the target’s characteristics and behavior.

Moving target detection is a technology further developed on the basis of moving target display. Moving target detection can automatically detect and identify dynamic targets by analyzing multiple pulse signals received by the radar. Moving target detection technology can be applied to many fields, such as aviation, military and transportation. Through moving target detection, we can achieve automatic tracking and identification of targets, thereby improving the automation level and work efficiency of the radar system.

In MTIMTD technology, we use 16 pulse signals to realize pulse pressure, moving target display and moving target detection. By superimposing and processing multiple pulse signals, we can obtain higher distance resolution and clearer target display. At the same time, MTIMTD technology can also realize automatic detection and identification of dynamic targets by analyzing and processing multiple pulse signals.

In general, the pulse pressure of 16 pulse signals, moving target display and moving target detection technology (MTIMTD) play an important role in the radar system. Through these technologies, we can achieve higher distance resolution, clearer target display and higher levels of automation. In the future, with the continuous development of radar technology, MTIMTD technology is expected to be widely used in more application fields and bring us more convenience and benefits.

Part of the code

?</code><code>close all</code><code>clear </code><code>clc</code><code>SearchAgents=30; </code><code>Fun_name=\ 'F1'; </code><code>Max_iterations=500; </code><code>[lowerbound,upperbound,dimension,fitness]=fun_info(Fun_name);</code><code>[Best_score,Best_pos, SHO_curve]=GO(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness);</code><code>?</code><code>figure('Position',[500 500 660 290])</code><code>%Draw search space</code><code>subplot(1,2,1);</code><code>fun_plot(Fun_name);</code><code>title('Parameter space ')</code><code>xlabel('x_1');</code><code>ylabel('x_2');</code><code>zlabel([Fun_name,'( x_1 , x_2 )'])</code><code>?</code><code>%Draw objective space</code><code>subplot(1,2,2);</code><code> semilogy(SHO_curve,'Color','g');</code><code>?</code><code>title('Objective space')</code><code>xlabel( 'Iterations');</code><code>ylabel('Best score');</code><code>?</code><code>axis tight</code><code>grid on </code><code>box on</code><code>?</code><code>legend('GO')</code><code>?</code><code>display([ 'The best optimal value of the objective function found by GO is : ', num2str(Best_score)]);</code><code>?</code><code>

Operation results

References

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1 Improvements and applications of various intelligent optimization algorithms

Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics location selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization

2 Machine learning and deep learning

Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis

2. Image processing

Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing

3 Path planning

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

4 UAV applications

UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories

5 Wireless sensor positioning and layout

Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization

6 Signal processing

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