topherhaddad

TDI (time delay integration)

A little more detail that we didn't get into in the post. The 3-CMG control mode we first uploaded was v1. We had plans to improve the agility with later versions. In v1, we didn't have quite enough rate to match earth's, even with max TDI. We called it Banana Scanning. Kind of like slipping over the earth.

Net - the CMG imagery we captured had a few pixels of along-track smear in it. Which would have been removed in post-processing if we had made it through focus cal.

Our business model is focused on providing VLEO systems (satellites, buses, and full mission services). But future customers may be provide image services :)

Thank you!

1. The proximity improves performance: - Range^2: imaging, other sensing, closing link budgets (data you're sending or signals you're sensing) - Range^3: SAR - Range^4: Active sensing (lidar, radar, etc) - Speed: Comms latency, time to intercept - If you can build systems fast/cheap, this physics unlock creates a new paradigm for system architectures (compared to traditional cost/schedule/performance tradeoffs)

2. Diversification is important for resilience/deterrence - Drag self cleans debris - It's below the belts where radiation gets trapped after a nuclear detonation. Makes for not only a survivable orbit, but one with assured reconstitution

Yes, exactly. Space safety :)

Ultimately it's about the ballistic coefficient. You want high mass, low cross-sectional area, and low drag coefficient (Cd). With propulsion for station-keeping, it's challenging to capture the VLEO benefits with a regular cubesat. That said, there are VLEO architectures different than Clarity that make sense for other mission areas.

Yes it's a big contributor. The atmosphere in VLEO behaves as free molecular flow instead of a continuous fluid.

The lesson there - dig multiple levels deep in supply chain

Alas.. the speed & resources of a startup. But we're learning.

Clarity is designed for a GSD (ground sample distance) of 10 cm. Generally the industry uses resolution<>GSD interchangeably. Agree it's not the true definition of resolution. But I'd argue the diffraction limit is an incomplete metric as well, like how spatial sampling is balanced with other MTF contributors (e.g. jitter/smear). For complete metrics, we like 1) NIIRS or 2) % contrast for a given object size on the ground (i.e. system MTF translated to ground units, not image-space units).

The main performance goal for us was NIIRS 7, and we decomposed GSD/MTF/SNR contributors optimized for affordability when we architected the system

Moving fast to make launch, we had missed a harness checkout step that would’ve caught a missing comms connection into an FPGA, and it was masked because our redundant comms channel made everything look nominal.

On orbit, we fixed it by pushing an FPGA update and adding software-level switching between the channels to prove the update applied and isolate the hardware path — which worked. Broader lesson, it is possible to design a sw stack capable of making updates to traditionally burned-in components.

Great question. Orbit Fab plans to launch fuel depots into space, somewhat aligned with orbits of their customers. For most cubesat/smallsat constellations, it probably does make more sense to just launch a new fleet. Our satellites will be refrigerator size and a bit pricier, so it makes more sense to refuel and extend the life. Yeah check out our careers page!

Great question. The 3 tiers correspond to how much imagery is ordered per year, the more you order the cheaper the per unit price is. The "minimum order" corresponds to the area size on the ground for a single tasking order (a single image).

The comparison shot was taken with a drone at an altitude that corresponds to our 10cm resolution. The 30cm version was obtained through downsampling the 10cm image accordingly. I wish we had a real 10cm satellite image to use, but our satellites won't be on orbit until 2024, so we are using a drone to show examples in the meantime.

I don't have any papers offhand, but the wikipedia page on microbolometers is great. Traditional sensors for thermal imaging are made of materials like InSb and HgCdTe, which require cooling to cryogenic temperatures in order to limit dark noise. These types of sensors are similar to visible sensors where an electron is generated for each incident photon. Microbolometers operate fundamentally differently, where electrical resistance in response to temperature changes. While most space thermal sensors today still use traditional materials, most terrestrial thermal cameras (night-vision, thermal drones) use microbolometers when imaging in LWIR.

I totally agree the 9x is somewhat marketing speak, but I do think that the way GSD has historically been characterized by the 1-dimensional measurement does not do spatial resolution improvements enough justice. Electro-optical imaging systems are analogous to any digital sampling system. If you were sampling a sine wave at 90 Hz instead of 10 Hz, that's clearly a 9x improvement as you end up with 9x more samples. The same is true for imaging, you're digitally sampling an object with discrete pixels (of course there's more to overall image quality than just spatial resolution). Another side note is that the government NIIRS imagery rating system captures this squared effect through the log2(GSD) term in the equation. Photographers also think more about it in 2D, as they characterize resolution in total Megapixels in the image. Ok I'm done, I'll get off my resolution soapbox :)