Sensor Integrated Data Fusion
Let’s Be Honest: More Sensors Haven’t Made the Fight Any Easier
We’ve spent the last two decades fielding more ISR than we know what to do with. EO and IR on every drone. SIGINT everywhere. Radar on ground and air platforms. And yet, when you’re out in it, the picture rarely feels clearer. Overlapping feeds. Missed timestamps. Contradictory tracks. And no time to sort through it all. The burden lands squarely on the operator.
We’re not short on sensors. We’re short on fused understanding at the point of need. Most systems still assume the field has time, bandwidth, and connectivity. It doesn’t.
That gap between data and decision is costing us initiative.
FPGA
TACTICAL EDGE AI
AI - ML
/ THE PROBLEM /
Legacy Fusion Isn’t Wrong. It’s Just Not Fast Enough
The standard fusion model was built around reach-back processing. Raw feeds go to a TOC, analysts stack them, and push insight back out. That works when you’re static. It doesn’t work when you’re maneuvering through degraded comms, jumping between LOS and SATCOM, and dealing with ten-second target windows.
Most fusion engines assume synchronized inputs and clean pipelines. But in practice, you’ve got EO at 30 FPS, radar updating every few seconds, RF signals arriving in bursts, and acoustic sensors logging passively. Clocks don’t align. Packets get dropped. Feeds don’t always show up.
And when they don’t, the system either freezes or gives you false confidence.
/ OUR SOLUTIONS /
We Build: Mission-Specific Fusion That Works at the Edge
At Deca Defense, we build fusion stacks from the ground up for tactical environments. We don’t adapt general-purpose models and hope they survive the field. We build for your actual edge compute, your sensor mix, and your mission tempo.
We do not assume sensor harmony. We model signal conflict. We do not rely on pristine data. We handle sparse, delayed, and degraded inputs. We do not use generic AI. We build tuned pipelines per modality and per mission.
We are not interested in black boxes. We build systems that expose internal disagreement, degrade cleanly, and work under pressure.
/ TECHNICAL DEEPDIVE /
What It Takes to Fuse at the Edge Where It Matters
Handling Asynchronous, Incomplete, and Out-of-Order Data
We don’t assume the data arrives clean or on time. Our ingest layer treats time as an estimate, not a truth. Using statistical methods like dynamic time warping and learned priors from past missions, we align EO detections with RF bursts or radar hits even when timestamps are missing or misaligned.
We have systems that work without GPS, without shared clocks, and without the need for constant connectivity.
Respecting the Strengths and Limits of Each Sensor
Every sensor does something well and something poorly. EO and IR give good spatial detail but struggle in poor visibility. Radar sees motion but throws off false positives in clutter. RF gives presence but no location. Acoustic hears things long after they’re gone.
We do not normalize these. We use dedicated pipelines for each. EO goes through motion-tuned CNNs. Radar is tracked with LSTM and Kalman filters. RF signals are profiled spectrally for burst identity. Acoustic signals are separated into harmonic patterns.
And we carry those uncertainties forward into fusion. We don’t average them into mush.
Fusing With Logic Instead of Blind Math
Most so-called fusion systems stack up predictions and try to vote. Ours reasons.
First we weight each sensor’s output based on how recent, reliable, and relevant it is. Then we look at how they interact. Does a radar detection and an EO motion blob line up in the same area? Did the RF burst happen just before a vehicle showed up on IR?
The system doesn’t just output a yes or no. It gives a decision with confidence, source attribution, and time window. If something doesn’t add up, it flags that too. So operators know if they’re getting a solid picture or an edge case.
Running at the Edge. No Dependencies. No Assumptions.
Our models run where the fight happens. On Jetson, Qualcomm’s embedded AI platforms, Intel low-power edge AI hardware, or custom silicon. We optimize every model for real-time inference with tight power and compute budgets.And we test the pipeline under actual conditions. Sensor dropout. Heat. Clock drift. Partial data. Not just unit tests in a lab, but failure modes that reflect how real missions fall apart.
/ CONCLUSION /
Fusion Isn't a Backend Task Anymore, It's How You Win Fire Control
If fusion still happens off-platform and after the fact, it’s too late. The team that can process and act on sensor inputs at the point of collection will always move faster than the one waiting for a synthesized picture from a remote command node.
This isn’t just a latency issue. It’s about tactical units having the ability to act independently when networks degrade, when bandwidth collapses, or when centralized support isn’t available.
We build for that moment. When ISR is fragmented, time is short, and no one’s coming to help. Our systems don’t just correlate signals. They give warfighters clear, fused outputs, fast enough to make a decision without waiting for upstream coordination.
If you’re already trying to make fusion work at the edge, you’re not alone and you’re not imagining the pain. You’ve seen what happens when EO, radar, and RF sensors get jammed into a shared UI with no real-time reasoning behind them. You’ve worked with asynchronous feeds, conflicting data, and pipelines that only function when everything behaves perfectly. And you’ve probably been pitched “real-time AI” by teams who’ve never tested their models under bandwidth loss, clock drift, or degraded sensing.
If you’re building for low-SWaP hardware, operating in denied or degraded comms environments, and working within sub-minute targeting timelines, we can help. We don’t abstract the problem. We build from the signal layer up, handling ingest, alignment, inference, fusion logic, and deployment in a way that reflects your platform constraints and mission flow.
We don’t sell pre-built solutions. We engineer purpose-built stacks. If you’ve got a real use case, let’s compare notes.