From Radar to Lidar: Prototyping the Next Generation of Imaging Systems with IDL

Erin Eckles Tuesday, August 12, 2025

This blog was written by Nicholas J. Marechal, Ph.D., retired Distinguished Engineer at The Aerospace Corporation. For more than three decades, Nick helped advance the science of Synthetic Aperture Radar (SAR), signal processing, and remote sensing. He used IDL® to prototype algorithms, analyze complex datasets, and visualize results that supported the development of next-generation imaging technologies. Nick's story offers a fascinating look at how IDL has supported decades of research in radar science, from developing advanced SAR processing techniques to exploring new imaging concepts such as Synthetic Aperture Imaging Lidar (SAIL).

This post is the latest in our IDL Fellows series, which supports passionate retired IDL users who want to continue their scientific work and share it with the broader community. IDL is a scientific programming language widely used for data analysis, visualization, and image processing across disciplines including Earth science, astronomy, remote sensing, and medical imaging. There is continual innovation behind IDL, and this program is one of the many ways we hear about the new ways people are using it on a regular basis. If you're retired, still using IDL, and interested in becoming an IDL Fellow, we'd love to hear from you. Reach out to learn more about the program and see if you qualify.

When you're developing new remote sensing technologies, there isn't always a roadmap.

Much of my career at The Aerospace Corporation involved exploring ideas that were still in the research stage—new ways of processing radar data, improving image quality, or demonstrating concepts that had never been built before. In that environment, success depended on being able to test ideas quickly, visualize results immediately, and refine algorithms as new questions emerged. For me, that meant using IDL.

Over more than three decades, IDL became my primary environment for developing and evaluating signal-processing techniques for SAR and later SAIL. Whether calibrating airborne radar data, experimenting with image formation algorithms, or analyzing prototype sensor performance, IDL gave me the flexibility to move from concept to insight without slowing the research process.

A Code Example Showing the Difference

I began working with SAR in 1987, when airborne SAR systems were still evolving into the sophisticated platforms we know today. One of SAR's greatest strengths is its ability to produce detailed imagery regardless of daylight or weather conditions. Unlike optical sensors, radar can image through clouds and operate day or night, making it invaluable for environmental monitoring, defense, disaster response, and Earth observation.

Over time, researchers expanded those capabilities even further. Interferometric SAR made it possible to generate three-dimensional terrain models and monitor subtle ground movement over repeated observations. Today, missions such as NASA and ISRO's NISAR satellite continue building on decades of research by using repeat-pass radar observations to monitor Earth's changing surface. Watching those advances unfold has been one of the most rewarding parts of my career.

Turning Ideas into Working Algorithms

Research is rarely a straight line. Most new ideas begin with a simple question: "What happens if we try this?"

Answering that question usually requires writing code, processing data, looking at the results, making adjustments, and starting over. That's where IDL consistently proved its value.

One example involved developing Space Time Adaptive Processing (STAP), a technique for identifying moving vehicles hidden within airborne SAR imagery. Stationary terrain creates an overwhelming amount of radar clutter. Vehicles moving across that terrain often become difficult, or impossible, to distinguish in conventional SAR imagery.

Using IDL, we rapidly prototyped STAP algorithms, calibrated multiple radar channels, estimated and corrected phase errors, and evaluated clutter suppression techniques. Interactive FFT analysis and custom phase-unwrapping routines allowed us to investigate how moving targets behaved across many Doppler image cells and compare those observations with theoretical models.

The resulting imagery demonstrated how advanced signal processing could reveal targets that would otherwise remain hidden.

Space Time Adaptive Processing

Figure 1. Space Time Adaptive Processing applied to multi-channel SAR imagery revealed moving targets that were obscured in conventional radar images.

Exploring Synthetic Aperture Imaging Lidar

Radar wasn't my only research interest. Our team also investigated SAIL, exploring whether synthetic aperture techniques developed for radar could be applied to laser imaging.

Laboratory experiments demonstrated the concept, while IDL supported image formation, autofocus processing, visualization, and evaluation of the results. Like many research projects, the goal wasn't simply producing an image, it was understanding how well a completely new approach could work.

synthetic apearture imaging lidar

Figure 2. Laboratory demonstration of Synthetic Aperture Imaging Lidar (SAIL).

Building Better Images

Another project involved developing an experimental W-band SAR system installed on the rooftop of an Aerospace Corporation facility. Operating at 94 GHz with extremely fine resolution, the system allowed us to investigate how higher-frequency radar affected image interpretability.

Again, IDL became the primary environment for processing, analyzing, and displaying the imagery as we refined both the system and the associated algorithms.

W-band sAR imagery

Figure 3. W-band SAR imagery produced using an experimental rooftop imaging system.

Why IDL Endures

Looking back, what stands out isn't any single project. It's how often the research followed the same pattern: formulate an idea, process the data, visualize the results, ask a better question, and repeat. IDL fits naturally into that cycle.

Its combination of numerical processing, visualization, and interactive analysis allowed us to experiment rapidly without constantly switching between different software environments. That flexibility made it an ideal companion for research, where every result leads to the next question.

Even though I retired from The Aerospace Corporation in 2018, I still think about IDL the same way I always have which is not simply as a programming language, but as a research tool that helps transform ideas into working solutions.

Selected References, Figures 1-3.

1. N. J. Marechal, R. P. Dickinson, G. Karamyan, “Moving Targets in SAR Imagery,” Conference Workshop: Challenges in Synthetic Aperture Radar, Institute for Pure and Applied Mathematics, UCLA, February 6-10, 2012.

2. S. M. Beck, J. R. Buck, W. F. Buell, R. P. Dickinson, D. A. Kozlowski, N. J. Marechal, T. J. Wright, “Synthetic Aperture Imaging Laser Radar: Laboratory Demonstration and Signal Processing,” Applied Optics, December 2005, Vol. 44, No. 35.

3. N. J. Marechal, S. S. Osofsky, R. M. Bloom, “Demonstration of W-band SAR Imagery with a Ground Based System Having 7.5 GHz of Bandwidth Obtained with a Stepped Chirp Waveform,” IEEE Trans., on Aerospace and Electronics Systems, Vol. 49, No. 4, October 2013.

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