Radars with MATLAB & Simulink
How do Radars work
Radar is a radio frequency. It works by identifying objects within its detection range and measures their distance, speed, and direction, including both azimuth (the horizontal angle or direction of a compass bearing) and elevation angles.
Radar is employed for weather analysis, creating images of objects, and classifying object types. The Radar Toolbox in MATLAB® aids in the analysis, modeling, and simulation of radar systems throughout their entire lifecycle, from system design to signal and data processing.
What is Radar Equation?
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Radar system performance is often assessed by either the detection range at a particular probability of detection (Pd) or the Pd at a given range. The radar equation is used to estimate performance, taking into account factors such as system design, the objects detected, and environmental conditions.
Link-budget analysis, which calculates the power received at the receiver, is based on this equation. Traditionally, this process is done through spreadsheets. However, the Radar Designer app offers a more interactive approach, enabling users to perform link-budget analysis with visual tools. This app also aids in evaluating design choices and developing radar systems that fulfill specific performance objectives.
How do Radars works?
Radar works by capturing and analyzing reflected RF signals from objects or the surrounding environment within its detection range. It can either use its own transmitter or, in the case of passive radars, rely on existing RF energy sources to illuminate the area being monitored. As the energy travels through the environment, it scatters in various directions when it encounters an object or a change in conditions. The radar receives the reflected signal, which may include unwanted noise, interference from other RF sources, and clutter.
Signal processing techniques are used to identify detections, and further data processing algorithms enable the radar to generate object tracks, create images, and classify objects.
Different types of radar
Radar systems can be designed using various system-level architectures, depending on the specific application. These include:
- Monostatic and Bistatic/ Multistatic Radar
- Passive Radar
- Polarimetric Radar
- Imaging Radar
What is monostatic and Bistatic / multistatic radar?
Monostatic radar is the most common configuration, where the transmitter and receiver antennas are located at the same point. On the other hand, bistatic and multistatic radar systems place the transmitter and receiver antennas at different locations, with the distance between them typically matching the expected range to the target. These systems are particularly useful in situations where the reflected signal in the direction of the transmitter is too weak to be detected.
The radarDataGenerator, a statistical radar model in Radar Toolbox, allows for the generation of probabilistic detections in both monostatic and bistatic configurations.
Passive radar
Passive radar is a unique form of bistatic radar as it uses existing commercial broadcast or communication signals as its energy source, rather than relying on an integrated transmitter. The radar system receives and processes these signals to detect and track objects. As it does not emit its own signals, passive radar is harder to detect and is more resistant to interference and jamming. Using Radar Toolbox, you can simulate passive radar systems that utilize available transmitters to identify objects.
Polarimetric Radar
Polarimetric radar employs both vertical and horizontal polarizations in the transmission and/or reception stages to detect and classify objects. Polarimetric radar data enhances object detection and helps in classifying precipitation and atmospheric conditions for weather forecasting. To model a polarimetric radar, both polarizations must be simulated and analyzed.
With Radar Toolbox, you can evaluate the performance of a polarimetric radar in detecting targets using orthogonal polarizations.
Imaging radar
Imaging radar employs a broad bandwidth signal to produce high-resolution images of objects. A wide field of view is typically achieved through phased array antennas and beamforming techniques, or by using synthetic aperture radar (SAR), which collects reflected energy while moving over the surveyed area. To enhance resolution in both range and cross-range directions, pulse compression techniques improve range resolution, while cross-range compression methods—such as range migration and back-projection—boost cross-range resolution.
Where are radars used?
Radars are used across many different industries and are used for a number of different reasons. Radar applications include:
- Air Traffic Control
- Automotive
- Weather
- Remote sensing
- Aerospace and Defense
Air Traffic control
Airport Surveillance Radar (ASR): Radar is used in air traffic control systems to detect and locate aircraft near the airport. It also plays a crucial role in assisting airplanes during their final approach, especially in times of adverse weather conditions.
Automotive
Radars are used extensively in the automotive industry. Radars installed on vehicles are used to detect obstacles and other vehicles, determining their location and speed. Automotive applications utilize two types of radar: short-range radar (SRR) for functions like blind-spot monitoring and parking assistance, and long-range radar (LRR) for applications such as adaptive cruise control, collision avoidance, and blind spot detection.
With Radar Toolbox, you can simulate probabilistic radar detections, clusters, and tracks, including the effects of multipath propagation.
Weather
Another common application of radars is their use in the detection, measurement and location of precipitation, by capturing and analyzing reflections from the atmosphere. The two primary radar technologies used for weather forecasting are Doppler radar and polarimetric radar.
Doppler radar helps establish the direction and speed of precipitation, while polarimetric radar is used to identify the type of precipitation or assess turbulence.
Using Radar Toolbox, you can create in-phase and quadrature (IQ) signals for weather radars and measure parameters like shear (wind speed & direction) or turbulence.
Remote sensing
Imaging radar systems are essential tools in remote sensing, offering critical data for environmental and geospatial analysis. For instance, satellite-based synthetic aperture radars (SAR) capture detailed surface images of the Earth, helping to monitor changes in water levels, forest heights, and ecosystems.
Another radar type, ground penetrating radar (GPR), uses similar principles to SAR but focuses on subsurface imaging, supporting applications like geological studies, archaeology, utility detection, and concrete assessments.
The radarTransceiver in Radar Toolbox allows the generation of IQ signals, which are key for developing advanced signal processing and imaging techniques in remote sensing tasks.
Aerospace & defense
Radar systems in aerospace and defense are primarily used to detect and track objects over long distances and across a wide area. Depending on what they are monitoring, these radars are usually grouped into categories like air surveillance, maritime surveillance, or ground surveillance.
Some radars, called multifunction radars, can do multiple tasks at once, such as searching for, confirming, and tracking objects. Using MATLAB and Radar Toolbox, you can design and model these multifunction radar systems, adjusting key features like frequency and pulse repetition frequency (PRF).
why matlab & simulink for radar?
With MATLAB and Simulink, you can create and study different types of radar systems, from simple models to more detailed, physics-based designs. Radar Toolbox™ offers a set of tools, functions, and pre-built solutions that can help you:
- Make your link-budget analysis (calculate the strength of a signal as it travels from the transmitter to the receiver) more accurate by factoring in system components and environmental losses using the Radar Designer app.
- Simulate real-life situations, including environmental interference and the movement of multiple objects or platforms.
- Model and simulate the appearance of ghost targets and their tracks, caused by multipath reflections, using both statistical and physics-based approaches.
- Conduct closed-loop simulations for multifunction radar systems and optimize resource management by incorporating adaptive tracking strategies.
- Create synthetic aperture radar images to train deep learning models for recognizing targets.
- Generate radar data to train deep learning models for classifying objects and analyzing received signals.