【Practical Exercise】Radar Signal Processing with MATLAB

# 1. Basic Concepts of Radar Signal Processing

Radar (Radio Detection and Ranging) is a system that uses electromagnetic waves to detect, locate, and track targets. Radar signal processing is a crucial component of radar systems, primarily tasked with extracting and analyzing target information from radar echo signals to achieve target detection, parameter estimation, and classification.

Radar signal processing involves multiple technical domains, including signal processing, statistics, optimization theory, and machine learning. The performance of radar signal processing algorithms directly affects the overall performance of radar systems. Therefore, a thorough understanding of the foundational knowledge of radar signal processing is essential for designing and implementing high-performance radar systems.

# 2. MATLAB Radar Signal Processing Toolbox

### 2.1 Introduction to the Radar Signal Processing Toolbox

The MATLAB radar signal processing toolbox is a suite specifically designed for radar signal processing applications. It offers a variety of functions covering all aspects of radar signal processing, including:

- Signal preprocessing
- Feature extraction
- Target detection and parameter estimation
- Signal classification

The advantage of this toolbox lies in its ease of use and efficiency. It provides an intuitive user interface and comprehensive documentation, enabling engineers to quickly become proficient and efficiently process radar signals.

### 2.2 Common Radar Signal Processing Functions

The MATLAB radar signal processing toolbox includes many useful functions for performing common radar signal processing tasks. Some of the frequently used functions include:

- **phased.RangeDopplerEstimator**: Used to perform pulse Doppler processing to detect and estimate the range and velocity of targets.
- **phased.MatchedFilter**: Used to perform matched filtering to enhance target signals and suppress noise.
- **phased.ConstantFalseAlarmRateDetector**: Used to perform constant false alarm rate detection to detect targets in radar signals.
- **phased.AngleEstimator**: Used to estimate the azimuth or elevation of targets.

These functions offer powerful capabilities, allowing engineers to easily perform complex radar signal processing tasks.

#### Code Block Example:

% Import the radar signal processing toolbox
import phased.radarSignalProcessing.*;

% Create a pulse Doppler processor
rangeDopplerEstimator = phased.RangeDopplerEstimator('RangeResolution', 10, 'DopplerResolution', 1);

% Create a matched filter
matchedFilter = phased.MatchedFilter('FilterCoefficients', matchedFilterCoefficients);

% Create a constant false alarm rate detector
constantFalseAlarmRateDetector = phased.ConstantFalseAlarmRateDetector('Threshold', 0.01);

% Create an angle estimator
angleEstimator = phased.AngleEstimator('SensorArray', phased.ULA('NumElements', 8, 'ElementSpacing', 0.5));

% ...

% Process radar signals using these functions

#### Code Logic Analysis:

- `import phased.radarSignalProcessing.*` - Imports the radar signal processing toolbox.
- `rangeDopplerEstimator = phased.RangeDopplerEstimator(...)` - Creates a pulse Doppler processor and sets its range resolution and Doppler resolution.
- `matchedFilter = phased.MatchedFilter(...)` - Creates a matched filter and sets its filter coefficients.
- `constantFalseAlarmRateDetector = phased.ConstantFalseAlarmRateDetector(...)` - Creates a constant false alarm rate detector and sets its threshold.
- `angleEstimator = phased.AngleEstimator(...)` - Creates an angle estimator and sets its sensor array.
- `...` - Processes radar signals using these functions.

#### Parameter Explanation:

- `RangeResolution`: Range resolution of the pulse Doppler processor.
- `DopplerResolution`: Doppler resolution of the pulse Doppler processor.
- `FilterCoefficients`: Filter coefficients for the matched filter.
- `Threshold`: Threshold for the constant false alarm rate detector.
- `SensorArray`: Sensor array for the angle estimator.

# 3. Radar Signal Preprocessing

### 3.1 Noise Removal

Noise is a common interference factor in radar signal processing that can affect the quality and reliability of the signal. Noise removal is an important step in radar signal preprocessing, aimed at eliminating or reducing the impact of noise on the signal.

#### 3.1.1 Filter Design

A filter is a frequency selector that can selectively ***mon filters in radar signal processing include:

- **Low-pass filter**: Passes low-frequency signals and attenuates high-frequency signals.
- **High-pass filter**: Passes high-frequency signals and attenuates low-frequency signals.
- **Band-pass filter**: Passes signals within a specific frequency band and attenuates signals in other frequency bands.
- **Band-stop filter**: Attenuates signals within a specific frequency band and passes signals in other frequency bands.

Filter design needs to consider parameters such as the filter's cutoff frequency, passband gain, and stopband attenuation.

#### 3.1.2 Denoising Algorithms

In addition to filters, there are various denoising algorithms that can be used in radar signal processing, including:

- **Moving average filter**: Averages signals to eliminate random noise.
- **Median filter**: Applies median filtering to eliminate impulse noise.
- **Wiener filter**: An optimal linear filter that considers both the statistical characteristics of the signal and the noise.
- **Wavelet transform**: A time-frequency analysis method that decomposes signals into components of different frequencies and time scales, then selectively removes noise components.

### 3.2 Clutter Suppression

Clutter is another common interference factor in radar signal processing, referring to reflected signals from non-targets. Clutter suppression aims to eliminate or reduce the impact of clutter on the signal, improving the accuracy of target de