Mastering Scalable Radar Signal Processing with AMD Versal Devices

In the fast-paced world of radar technology, the ability to rapidly develop and implement advanced signal processing techniques is crucial for maintaining a competitive edge. Fidus Systems and AMD have collaborated to create a scalable radar reference design utilizing the AMD VCK190 evaluation board and the powerful VC1902 Versal core device. This collaboration aims to address the inherent challenges in radar signal processing and demonstrate the robust capabilities of the Versal AI engines.

This blog post will provide an in-depth look into the webinar where we presented this radar reference design. We will explore the complexities of radar signal processing, the innovative approach to creating a scalable architecture, and the phased development strategy that enabled rapid prototyping and demonstration. By the end of this post, you will gain a comprehensive understanding of how Fidus and AMD have pushed the boundaries of radar technology through this groundbreaking project.

The webinar covered the following key topics:

Introduction to Radar Demonstration Platform

One of the significant challenges in developing a radar demonstration platform is effectively showcasing the signal processing capabilities of the radar systems. Traditional radar performance metrics focus on the interaction between the radar and its target, emphasizing aspects such as range resolution, the ability to avoid jamming, and filtering out clutter. These metrics define how well the radar performs at a system level. However, when demonstrating signal processing capabilities, the focus shifts to different specifications: bandwidth processing, sample rates, bit widths, data types, and latency. These lower-level specifications are crucial for highlighting the effectiveness of algorithms and the computational power of devices like the VC1902 Versal core.

To effectively demonstrate improvements at the algorithm level, it is essential to tie these enhancements to system-level performance. This linkage allows us to show how changes in signal processing can lead to tangible benefits in radar operation. Our reference design aims to bridge this gap, providing a clear path from algorithmic improvements to enhanced system performance.

Challenges in Demonstrating Radar Signal Processing

Several inherent challenges arise when demonstrating radar signal processing capabilities:

• Range Resolution and Clutter Filtering: Ensuring that the radar can distinguish between targets at varying distances and effectively filter out irrelevant signals or clutter.
• Jamming Avoidance: The ability to prevent jamming signals from interfering with radar operations, which is critical for maintaining accurate target detection.
• Bandwidth and Sample Rates: Demonstrating the radar’s ability to process a wide bandwidth and operate at high sample rates, which are essential for high-resolution radar imaging.
• Data Types and Latency: Handling different data types and ensuring low latency in processing to maintain real-time performance.

System-Level vs. Algorithm-Level Specifications

While system-level specifications focus on overall radar performance, algorithm-level specifications delve into the specifics of signal processing. To showcase the benefits of our reference design, it is crucial to demonstrate how improvements at the algorithm level translate into better system-level performance.

For instance, an algorithm that enhances clutter filtering must show how it improves the radar’s ability to detect targets in cluttered environments. Similarly, an improvement in jamming avoidance algorithms should demonstrate how it enables the radar to maintain accurate target detection even in the presence of jamming signals.

Key Focus Areas for the Reference Design

Our reference design for the radar demonstration platform focuses on the following key areas:

• Scalable Architecture: Developing a design that can adapt to various devices within AMD’s portfolio, ensuring versatility and future-proofing.
• Phased Development: Implementing a phased approach to development, allowing for rapid prototyping and continuous improvement.
• Effective Signal Processing: Leveraging advanced algorithms like STAP to enhance radar performance in complex environments.
• System-Level Validation: Ensuring that improvements at the algorithm level are validated against system-level performance metrics.

By addressing these challenges and focusing on these key areas, our reference design provides a robust solution for demonstrating the advanced signal processing capabilities of modern radar systems, leveraging the powerful VC1902 Versal core device from AMD.

Building a Scalable Design Architecture

Creating a scalable design architecture was a primary goal for the radar reference design developed by Fidus Systems in collaboration with AMD. Scalability ensures that the design can adapt to various devices within AMD’s portfolio, providing flexibility and future-proofing the solution.

This section delves into the key aspects of our scalable design architecture and the benefits it brings to radar signal processing.

Targeting the VC1902 Versal Core Device

The initial focus of our design was the VC1902 device, a Versal core device equipped with AI engines. These AI engines significantly enhance the signal processing capabilities of the radar system. By leveraging the computational power of the VC1902, we aimed to showcase the benefits of using AI engines in radar signal processing.

The VC1902 offers the following advantages:

Scaling Up to VP2502 and VP2802 Devices

While the VC1902 was our initial target, the design architecture needed to be scalable to more powerful devices within AMD’s portfolio, such as the VP2502 and VP2802 Versal premium devices. These premium devices offer higher I/O bandwidth due to their GTH transceivers and a significant number of DSP58 blocks in addition to the AI engines.

Benefits of scaling up to premium devices include:

Adapting to Mid-Range AI Edge Devices

In addition to scaling up, our design architecture also needed to scale down to mid-range AI edge devices, which are smaller and have fewer AI engines. These devices are suitable for SWaP (Size, Weight, and Power)-constrained platforms where compact and efficient designs are crucial.

Key considerations for scaling down include:

Benefits of a Scalable Design

Designing a scalable radar platform provides numerous benefits, ensuring the system remains adaptable, efficient, and future-proof. Here are the primary advantages:

• Adaptability to Different Applications: A scalable radar platform can be tailored to meet the specific needs of various applications, from military and defense to automotive and aerospace. This adaptability ensures that the same core technology can be used across multiple domains, reducing development time and costs.
• Cost Efficiency: Scalability allows for incremental upgrades and expansions rather than complete overhauls. This approach reduces the total cost of ownership and allows for budget-friendly improvements as new technologies and requirements emerge.
• Future-Proofing: As new technologies and techniques in radar signal processing become available, a scalable platform can incorporate these advancements without significant redesign. This future-proofing ensures that the radar system remains relevant and effective over a longer period.
• Rapid Prototyping and Deployment: Scalable platforms support rapid prototyping, allowing developers to quickly test and validate new ideas and configurations. This capability accelerates the development cycle and speeds up time-to-market for new radar solutions.
• Enhanced Performance and Reliability: By leveraging the high-performance capabilities of AMD Versal devices, scalable radar platforms can achieve superior processing power and reliability. This enhanced performance is crucial for applications that require real-time data processing and high accuracy.
• Customization for Specific Needs: The reconfigurability of Versal’s FPGA fabric allows for customized solutions tailored to specific operational needs. Whether it’s adjusting the number of processing units, integrating new algorithms, or expanding data handling capacities, the platform can be precisely tuned to meet specific requirements.

Phase One: Establishing Rapid Development

The phased approach to development was a critical strategy for ensuring the successful implementation of our radar reference design. By breaking the project into manageable phases, we could demonstrate progress quickly, meet milestones, and maintain flexibility for future enhancements. Phase one focused on rapid development, laying the foundation for subsequent phases.

Importance of a Phased Approach

The phased approach allows for incremental development, providing several advantages:

• Quick Demonstrations: Early phases enable us to demonstrate capabilities rapidly, which is crucial for securing contracts and customer confidence.
• Milestone Tracking: Each phase has specific milestones, making it easier to track progress and ensure that the project remains on schedule.
• Flexibility: By focusing on one phase at a time, we can adjust our approach based on feedback and evolving requirements, ensuring that the final design meets all specifications.

Goals and Achievements of Phase One

Phase one aimed to create a quick demonstration platform showcasing the hardware capabilities and initial algorithm performance. Key goals and achievements of this phase included:

• Rapid Prototyping: Developing a prototype quickly to demonstrate the potential of the VC1902 Versal core device.
• Algorithm Validation: Implementing the initial version of the space-time adaptive processing (STAP) algorithm and validating its performance.
• Problem Solving: Addressing initial challenges in radar signal processing, such as clutter filtering and jamming avoidance, to set the stage for more complex functionalities in later phases.

Setting the Foundation for Future Phases

The focus in phase one was on establishing a solid foundation for future development. This involved:

• Hardware Demonstration: Creating a platform that effectively demonstrates the capabilities of the hardware, including the AI engines and other critical components.
• Algorithm Implementation: Implementing the basic elements of the STAP algorithm, focusing on weight application and data selection, with weight calculation performed externally in MATLAB.
• Performance Metrics: Establishing performance metrics and benchmarks that would guide the development in subsequent phases, ensuring that each phase builds on the successes of the previous ones.

Algorithm Implementation: Space-Time Adaptive Processing

The space-time adaptive processing (STAP) algorithm is a cornerstone of our radar reference design. STAP is essential for enhancing radar performance in complex environments, particularly for detecting moving targets amidst clutter and jamming signals. In this section, we delve into the details of STAP implementation, the challenges it addresses, and the benefits it brings to radar signal processing.

Introduction to STAP

STAP is a sophisticated signal processing technique used to improve radar performance by filtering out unwanted signals and enhancing the detection of moving targets. It combines spatial and temporal filtering to adaptively remove clutter and jamming interference.

Key Concepts of STAP:

• Space-Time Data Cube: STAP processes a three-dimensional data cube comprising range, Doppler, and spatial (antenna) dimensions. This data cube represents the radar returns from multiple pulses and antennas, capturing both the target and clutter signals.
• Clutter Mitigation: By analyzing the spatial and temporal characteristics of the received signals, STAP distinguishes between clutter and moving targets. It effectively filters out the clutter, allowing the radar to focus on detecting and tracking moving objects.
• Adaptive Filtering: STAP uses adaptive filtering techniques to dynamically adjust the filter weights based on the received signal environment. This adaptability ensures optimal performance in varying operational conditions.

Advantages of STAP:

• Enhanced Target Detection: STAP significantly improves the radar’s ability to detect moving targets in the presence of strong clutter. By filtering out stationary objects, STAP enhances the visibility of moving targets, even in complex environments.
• Adaptive Performance: The adaptive nature of STAP allows it to dynamically adjust to changing signal conditions, maintaining optimal performance across different operational scenarios.
• Improved Clutter Suppression: STAP provides superior clutter suppression compared to traditional filtering techniques. This improved clutter rejection enhances the radar’s ability to operate in environments with high levels of interference.
• Increased Range and Resolution: By effectively managing clutter and noise, STAP allows the radar to operate at longer ranges and with higher resolution. This capability is critical for applications requiring precise target detection and tracking.

Leveraging DSP Functions on the CPI Data Cuboid

The implementation of space-time adaptive processing (STAP) involves creating and manipulating a radar data cube, which serves as the cornerstone for various digital signal processing (DSP) functions. This section will delve into the structure of the radar data cube, the different DSP functions that can be performed, and how our scalable architecture supports these operations.

Structure of the Radar Data Cube

A radar data cube is a three-dimensional representation of radar signal data, with each dimension corresponding to specific parameters:

• Spatial Dimension: This dimension represents the number of antennas used in the radar array. Each antenna captures signals from different angles, contributing to spatial resolution.
• Range Dimension: This dimension is based on the number of range samples, which are obtained by measuring the time it takes for radar pulses to travel to a target and back. It helps in determining the distance to the target.
• Temporal Dimension: The temporal dimension involves coherent pulses over time, which are essential for Doppler processing and identifying moving targets.

Performing Various DSP Functions

The radar data cube enables a wide range of DSP functions, each addressing different aspects of radar signal processing:

DSP Functions	Objective	Implementation
Beamforming	Enhance signal detection from specific directions by combining signals from multiple antennas.	Uses spatial filtering techniques to focus the radar beam, improving target resolution and detection accuracy.
Doppler Processing	Identify the relative velocity of targets by analyzing frequency shifts (Doppler effect).	Temporal filtering techniques are applied to the data cube to distinguish between stationary and moving objects, critical for tracking moving targets.
Clutter Filtering	Remove reflections from stationary objects that can obscure the detection of moving targets.	Combines spatial and temporal filtering to suppress clutter and highlight moving targets, enhancing overall radar performance.
Jamming Suppression	Mitigate the impact of jamming signals that can interfere with radar operation.	Applies adaptive filtering to detect and filter out jamming signals, ensuring reliable target detection even in the presence of interference.

Scalability of the Design

Our reference design’s architecture is inherently scalable, allowing for adaptation to various devices within AMD’s portfolio. This scalability is demonstrated in how the design can handle different sizes and complexities of radar data cubes:

Scaling Up:

• Higher I/O Bandwidth: Devices like the VP2502 and VP2802 offer higher I/O bandwidth, enabling the processing of larger radar data cubes with more antenna elements and higher sample rates.
• Increased Computational Power: More DSP58 blocks and AI engines in these devices provide the necessary computational power to handle complex DSP functions on larger data sets.

Scaling Down:

• Efficiency in SWaP-Constrained Platforms: The design can be adapted for mid-range AI edge devices with fewer AI engines, ensuring efficient performance within size, weight, and power (SWaP) constraints.
• Optimized Processing: Even with reduced computational resources, the design maintains robust radar performance by focusing on essential DSP functions tailored to specific application requirements.

Integrating Space-Time Processor Components

The space-time adaptive processing (STAP) algorithm involves several critical components that work together to filter out clutter and jammers, revealing the desired targets in radar data. This section delves into the technical details of these components and their roles in the STAP process.

Covariance Matrix Calculation

The first step in the STAP process is calculating the covariance matrix, which models the effects of the environment on radar signals. The covariance matrix captures the statistical properties of the radar returns, including clutter and jamming signals.

Statistical properties	Purpose	Method
Environmental Modeling	Understand the statistical characteristics of the environment to identify and filter out unwanted signals.	Collect radar returns over multiple pulses and antennas to estimate the covariance matrix, which represents the clutter and jammer characteristics
Adaptive Weighting	Use the covariance matrix to determine the weights that will be applied to the radar data for filtering	Calculate the weights that minimize the impact of clutter and jammers while maximizing the detection of moving targets.

Steering Vector Application

Once the covariance matrix is calculated, the next step is to apply the steering vector. This vector represents the desired direction of the radar beam and is used to focus the signal processing on specific angles.

Statistical properties	Purpose	Method
Beamforming	Enhance signal detection from the desired direction by combining signals from multiple antennas.	Multiply the radar data by the steering vector to focus the radar beam, improving spatial resolution and target detection accuracy.
Weight Application	Apply the calculated weights to the radar data to filter out clutter and jammers.	Perform matrix multiplication of the radar data and the weights to achieve the desired filtering effect. This step is computationally intensive and leverages the AI engines in the VC1902 device.argets.

Phase One Focus on Weight Application

In phase one of our development, we focused on the weight application portion of the STAP algorithm. This involved using the AI engines to perform efficient matrix multiplication, a critical component of the STAP process.

Matrix Multiplication:

• Implementation: The AI engines in the VC1902 device are used to multiply the radar data cube by weight. This step is crucial for filtering out clutter and jammers and is performed across multiple AI engine kernels to enhance processing speed and efficiency.
• External Weight Calculation: Initially, the weight calculation is performed in MATLAB, providing a benchmark for validating the hardware implementation.

Data Selection:

• Implementation: Select slices from the radar data cube for processing. This involves choosing the appropriate data dimensions (antennas, range samples, and pulses) to apply the weights effectively.

Developing the System Model

A comprehensive system model is essential for validating the radar reference design, ensuring that the algorithm performs as expected in real-world scenarios. This section discusses leveraging MathWorks’ STAP example design, iterative tuning of the algorithm, and validating the hardware implementation.

Leveraging MathWorks’ STAP Example Design

The MathWorks STAP example design serves as a robust starting point for our system model. MATLAB and Simulink provide extensive tools for modeling radar systems, transceivers, antennas, platforms, jammers, and clutter.

• Environmental Modeling: The example design includes models for various environmental factors affecting radar performance, allowing us to focus on implementing and tuning the STAP algorithm.
• Algorithm Development: By building on the example design, we can develop and test our algorithm within a proven framework, ensuring accurate and reliable results.

Iterative Tuning of the Algorithm

Iterative tuning is critical to optimizing the STAP algorithm and ensuring that it meets system-level performance requirements.

• Parameter Adjustment: Adjust system parameters such as sample rates, antenna configurations, and clutter models to match real-world conditions.
• Performance Metrics: Evaluate the performance of the STAP algorithm using metrics such as detection accuracy, clutter suppression, and jamming resistance.
• Continuous Improvement: Refine the algorithm iteratively, leveraging feedback from simulation results to enhance performance continuously.

Validating Hardware Implementation

Validation of the hardware implementation involves comparing the performance of the algorithm running on the VC1902 device with the MATLAB reference design.

• Simulation Comparison: Compare the results from the hardware implementation with the MATLAB reference to ensure that the hardware accurately replicates the desired performance.
• Real-World Testing: Validate the hardware implementation under real-world conditions, ensuring that it meets all performance requirements and handles various environmental factors effectively.

Optimizing AI Engine Matrix Multiplication

Matrix multiplication is a critical component of the STAP algorithm, and the AI engines in the VC1902 device are leveraged to perform this task efficiently. This section covers partitioning matrix multiplication across AI kernels, data transfer and result compilation, and the efficiency of AI engines.

Partitioning Matrix Multiplication Across AI Kernels

Efficient matrix multiplication is achieved by partitioning the task across multiple AI engine kernels, allowing for concurrent processing.

• Kernel Partitioning: Divide the matrix multiplication task into smaller sub-tasks, each handled by a separate AI engine kernel.
• Concurrent Processing: Leverage the parallel processing capabilities of the AI engines to perform matrix multiplication concurrently, enhancing speed and efficiency.

Data Transfer and Result Compilation

Efficient data transfer and result compilation are essential for ensuring seamless integration between the AI engines and the processing system.

• Data Movement: Use GMIO interfaces to transfer data between the processing system and AI engines, ensuring timely and accurate data transfer.
• Result Compilation: Compile the results from multiple AI engine kernels, ensuring that the final output is accurate and ready for further processing.

Efficiency of AI Engines

The AI engines in the VC1902 device offer significant advantages for matrix multiplication tasks.

• High Performance: AI engines provide high computational power, enabling efficient handling of complex matrix multiplication tasks.
• Scalability: The architecture of the AI engines allows for scalability, ensuring that the design can handle larger and more complex data sets as needed.
• Optimized Processing: AI engines are optimized for matrix multiplication, ensuring that the task is performed efficiently and accurately.

Strategic Design Decisions for AI and Scalar Engines

Effective design decisions are crucial for optimizing the performance of the radar reference design. This section discusses data movement methodology, communication between the host application and MATLAB client, and design optimizations.

Data Movement Methodology

Efficient data movement is essential for ensuring that the radar data is processed accurately and in a timely manner.

• GMIO Interfaces: Use GMIO interfaces for efficient data transfer between the processing system and AI engines, ensuring minimal latency and high data throughput.
• Data Management: Implement effective data management strategies to handle the large volumes of data generated by the radar system, ensuring that it is processed accurately and efficiently.

Communication Between Host Application and MATLAB Client

Seamless communication between the host application and MATLAB client is essential for validating the radar reference design.

• TCP/IP Communication: Use TCP/IP for reliable communication between the host application running on the processing system and the MATLAB client, ensuring accurate data transfer and synchronization.
• Custom Protocols: Implement custom serial framing protocols for sending and receiving data, ensuring that the data is correctly formatted and interpreted by both the host application and MATLAB client.

Design Optimizations

Continuous design optimizations are essential for enhancing the performance of the radar reference design.

• Algorithm Optimization: Refine the STAP algorithm to enhance its performance, leveraging feedback from simulation and real-world testing.
• Hardware Optimization: Optimize the hardware implementation to ensure that it meets all performance requirements, minimizing latency and maximizing efficiency.

Implementing Hardware in the Loop Weight Application

The hardware-in-the-loop (HIL) approach involves integrating the virtual VCK190 development board with real-time data simulation and processing. This section discusses the setup, real-time data simulation, and validation of the HIL weight application.

Virtual VCK190 Development Board

The virtual VCK190 development board serves as the primary platform for implementing and testing the STAP algorithm.

• Setup: Configure the virtual VCK190 development board with the necessary connections and settings, ensuring that it accurately represents the real hardware.
• Integration: Integrate the virtual board with the simulation environment, enabling seamless data transfer and processing.

Real-Time Data Simulation and Processing

Real-time data simulation is crucial for testing the STAP algorithm under realistic conditions.

• Data Simulation: Use MATLAB and Simulink to simulate radar data in real-time, generating the necessary inputs for the STAP algorithm.
• Real-Time Processing: Implement the STAP algorithm on the virtual VCK190 development board, processing the simulated data in real-time and generating the desired outputs.

Testing and Validation

Testing and validation are essential for ensuring that the STAP algorithm performs as expected under real-world conditions.

• Performance Metrics: Evaluate the performance of the STAP algorithm using metrics such as detection accuracy, clutter suppression, and jamming resistance.
• Real-World Testing: Validate the algorithm under real-world conditions, ensuring that it meets all performance requirements and handles various environmental factors effectively.

Analyzing Results of Filtering

The results of filtering demonstrate the effectiveness of the STAP algorithm in enhancing radar performance. This section covers the performance of the STAP algorithm, before and after STAP filtering, and validation of target detection.

Performance of STAP Algorithm

The performance of the STAP algorithm is evaluated using various metrics.

• Detection Accuracy: Measure the accuracy of target detection, ensuring that the algorithm effectively identifies and tracks moving targets.
• Clutter Suppression: Evaluate the algorithm’s ability to suppress clutter, enhancing the clarity of radar signals.
• Jamming Resistance: Test the algorithm’s resistance to jamming, ensuring that it can reliably detect targets even in the presence of interference.

Before and After STAP Filtering

Comparing the radar data before and after STAP filtering highlights the effectiveness of the algorithm.

• Raw Data: Analyze the raw radar data, identifying clutter and jamming signals that obscure target detection.
• Filtered Data: Evaluate the radar data after applying the STAP algorithm, demonstrating the enhanced clarity and accuracy of target detection.

Validation of Target Detection

Validating target detection ensures that the STAP algorithm performs accurately and reliably.

• Simulation Comparison: Compare the results from the hardware implementation with the MATLAB reference design, ensuring that the hardware accurately replicates the desired performance.
• Real-World Testing: Validate the algorithm under real-world conditions, ensuring that it meets all performance requirements and handles various environmental factors effectively.

Live Demo – Radar Reference Design with VCK190 Evaluation

The live demonstration showcases the capabilities of the VCK190 evaluation card and outlines future enhancements. This section covers the live demonstration, comparison with MATLAB calculations, and future phases.

Live Demonstration of VCK190 Evaluation Card

The live demonstration highlights the capabilities of the VCK190 evaluation card in implementing the STAP algorithm.

• Setup: Configure the VCK190 evaluation card with the necessary connections and settings, ensuring that it accurately represents the real hardware.
• Demonstration: Showcase the performance of the STAP algorithm on the VCK190 evaluation card, highlighting its effectiveness in real-time data processing.

Comparison with MATLAB Calculations

Comparing the results from the VCK190 evaluation card with MATLAB calculations ensures accuracy and performance.

• Simulation Comparison: Compare the results from the hardware implementation with the MATLAB reference design, ensuring that the hardware accurately replicates the desired performance.
• Performance Metrics: Evaluate the performance of the STAP algorithm using metrics such as detection accuracy, clutter suppression, and jamming resistance.

Future Phases and Enhancements

Future phases will build on the foundation established in phase one, incorporating additional components and optimizing performance.

• Extending Algorithm Capabilities: Implement additional components of the STAP algorithm, such as covariance matrix calculation and adaptive weight determination, directly in the hardware.
• Optimizing Performance: Enhance the performance of the radar system by optimizing the processing algorithms and leveraging the full capabilities of the VC1902 and other Versal devices.
• Scalability: Adapt the design to scale up for more powerful devices and scale down for mid-range applications, ensuring versatility and future-proofing the radar system.

Conclusion

The collaboration between Fidus Systems and AMD has resulted in a robust, scalable radar reference design that leverages the powerful capabilities of the VC1902 Versal core device. By focusing on rapid development, scalable architecture, and advanced signal processing techniques, this design sets a new standard in radar technology. Future phases will continue to build on this foundation, incorporating additional components and optimizing performance to meet the evolving demands of modern radar applications.

Summary of the Collaboration

The collaboration between Fidus Systems and AMD has demonstrated the effectiveness of combining expertise in radar technology and advanced signal processing.

• Innovative Design: The radar reference design showcases innovative approaches to signal processing, leveraging the capabilities of the VC1902 Versal core device.
• Rapid Development: The phased approach to development ensures quick demonstrations and continuous improvement, maintaining customer confidence and project momentum.
• Scalable Architecture: The scalable design ensures versatility across different platforms, providing a future-proof solution for radar technology.

Future Prospects and Developments

Future phases will continue to enhance the radar reference design, incorporating additional components and optimizing performance.

• Extended Capabilities: Implement additional components of the STAP algorithm, enhancing the performance and capabilities of the radar system.
• Optimized Performance: Continuously optimize the processing algorithms and hardware implementation, ensuring that the radar system meets all performance requirements.
• Scalability: Adapt the design to scale up for more powerful devices and scale down for mid-range applications, ensuring versatility and future-proofing the radar system.

Final Remarks

The radar reference design developed by Fidus Systems and AMD represents a significant advancement in radar technology. By leveraging the powerful capabilities of the VC1902 Versal core device and focusing on rapid development and scalable architecture, this design sets a new standard for radar signal processing. Future phases will continue to build on this foundation, ensuring that the radar system meets the evolving demands of modern applications.

For a deeper understanding and detailed insights into these topics, we invite you to watch the full webinar presented by our experts. The webinar covers the technical aspects of scalable radar signal processing with AMD Versal devices, providing valuable knowledge that can be directly applied to your projects.