【Practical Exercise】Radar Tracking System Simulation Based on Simulink

# 1. Fundamentals of Radar Tracking Systems

A radar tracking system is a technology that utilizes radar signals to track and locate targets. By emitting electromagnetic waves and receiving signals reflected back from the target, it acquires information such as the target's position, velocity, and acceleration. Radar tracking systems are widely used in military, aviation, navigation, and meteorology fields, playing a crucial role in target detection, tracking, and identification.

A radar tracking system mainly consists of radar sensors, signal processing modules, and tracking algorithm modules. The radar sensor is responsible for transmitting and receiving electromagnetic waves, the signal processing module processes the received signals to extract target information, and the tracking algorithm module utilizes this information to track and locate the target.

# 2. Simulink Modeling Fundamentals

### 2.1 Introduction to the Simulink Modeling Environment

#### 2.1.1 Simulink Workspace and Module Library

The Simulink workspace is an interactive environment for creating, editing, and simulating models. It includes the following main areas:

***Model Browser:** Displays the hierarchy of the current model, including subsystems, modules, and signals.
***Editor:** Used for creating and modifying models in a graphical interface.
***Module Library:** Provides various predefined modules for building models.
***Command Window:** Used to input commands, view results, and debug models.

#### 2.1.2 Basic Modeling Elements and Connections

Simulink models consist of the following basic elements:

***Modules:** Represent functional blocks that perform specific computations or operations.
***Signals:** Represent data flow, connecting modules and transmitting information.
***Subsystems:** Combine related modules to form more complex systems.

Modules and signals are connected by lines, forming the logical flow of the model. There are two types of connection methods:

***Unidirectional Connection:** Signals flow from the source module to the target module.
***Multidirectional Connection:** Signals flow from the source module to multiple target modules.

### 2.2 Radar Signal Processing Modules

#### 2.2.1 Pulse Compression

The pulse compression module is used to improve the signal-to-noise ratio of radar signals and enhance target detection capability. Its principle is to modulate a long pulse sequence into a short pulse sequence, then process it through a matched filter to obtain high-resolution target echoes.

% Pulse compression module code
pulseCompression = dsp.PulseCompression(...
     'PulseWidth', 0.001, ... % Pulse width
     'PRF', 1000, ... % Pulse repetition frequency
     'NumberOfPulses', 100); ... % Number of pulses

% Pulse compression logic analysis
% The pulse compression module modulates the input pulse sequence into a short pulse sequence, then processes it through a matched filter.
% The matched filter matches the shape of the input pulse, thereby enhancing the signal-to-noise ratio of the target echo.

#### 2.2.2 Constant False Alarm Rate Detection

The Constant False Alarm Rate (CFAR) detection module is used to detect targets in radar echoes while maintaining a constant false alarm rate. Its principle is to dynamically adjust the detection threshold based on the power of the signal and the level of background noise.

% CFAR detection module code
cfard = dsp.CFARDetector(...
     'ThresholdFactor', 1.5, ... % Threshold factor
     'GuardBandSize', 5, ... % Guard band size
     'WindowLength', 10); ... % Window length

% CFAR detection logic analysis
% The CFAR detection module dynamically adjusts the detection threshold based on the power of the signal and the level of background noise.
% When the signal power is above the threshold, the module output is 1, indicating detection of a target; otherwise, the output is 0.

### 2.3 Radar Tracking Algorithm Modules

#### 2.3.1 Kalman Filter

The Kalman filter is a state estimation algorithm used to estimate the motion state of a target. Its principle is to combine the target motion model and measurement data to continuously update the estimated values of the target state.

% Kalman filter module code
kalmanFilter = dsp.KalmanFilter(...
     'ProcessNoiseCovariance', eye(4), ... % Proce