Target intelligence
Detect weak or ambiguous returns in cluttered scenes.
Our purpose
PerspeQtive builds a new class of AI models for radar intelligence.
Learning from the full radar signal, not just the image, to detect targets, changes, and deception that standard pipelines can miss.
PerspeQtive turns raw radar data into target intelligence: material cues, ground perturbations, moving targets, decoy signatures, dark vessels, and subtle changes hidden in noisy scenes.
What we build
Radar intelligence models
Where we start
Raw SAR / SLC-IQ data
What it unlocks
Targets · change · deception
AI's current blind spot
The most valuable radar signal is often wasted before AI sees it.
Radar sensors do not just capture images. They capture complex waves: amplitude and phase.
Many AI pipelines flatten that measurement into image-like inputs. Half of the complex signal can be discarded before training begins, including information linked to material response, motion, coherence, and subtle change.
Sensor output: full radar signal
Amplitude + phase captured
AI bottleneck
Phase discarded before the model sees it
Blind decisions
Missed targets, false alarms, ambiguous signatures
50%
of the complex radar measurement can be discarded before AI training
Image view → signal view
Same radar scene. More evidence preserved.
Drag between a conventional amplitude view and PerspeQtive's full-signal readout.
The goal is not a prettier image. The goal is better evidence for the model: stronger target contrast, clearer separation, and signal structure that survives noise.
PerspeQtive full-signal readout Amplitude-only view Solution
New AI models for radar intelligence.
PerspeQtive trains radar models on richer signal representations, so they can detect targets, motion, changes, and ambiguous signatures in conditions where image-based models struggle.
Change intelligence
Find ground perturbations, coherent shifts, and subtle differences between passes.
Material and deception cues
Expose scattering behavior linked to decoys, surfaces, and object composition.
Operational evidence
Return target maps, confidence, failure cases, and benchmark comparisons.
Quantum core
Why quantum mathematics belongs in radar AI.
Radar waves and quantum states share the same mathematical language: amplitude, phase, interference, and measurement.
PerspeQtive uses that bridge to build AI models that preserve radar wave structure before the signal is reduced to an image, running entirely on standard GPUs.
This is not a quantum computer. It is quantum mathematics applied to radar intelligence.
Wave structure
Radar signals contain phase, interference, and coherence patterns that image-based AI can underuse.
Quantum representation
Quantum mathematics offers a natural way to represent wave-like information.
GPU execution
PerspeQtive brings that representation into deployable AI models on standard hardware.
Where it matters
Radar intelligence where clean imagery is not enough.
Radar is used when optical imagery fails: night, cloud, smoke, distance, denied areas, spoofed navigation. PerspeQtive targets the cases where preserving more of the radar signal can change the decision.
Defense SAR
Target detection in clutter
- Mission problem
- Weak targets can disappear inside coastal clutter, speckle, smoke, cloud cover, or low-visibility scenes.
- Signal cue
- Target contrast, coherent returns, and wave structure that amplitude-only views can flatten.
- Operational output
- Target maps, confidence scores, and failure cases benchmarked against the current radar pipeline.
Ukraine
Decoy discrimination
- Mission problem
- Real assets and false signatures can look similar once radar is reduced to an image.
- Signal cue
- Scattering behavior, material cues, and coherence stability across the signal.
- Operational output
- Prioritized detections with fewer false positives and clearer analyst triage.
Europe
Ground and infrastructure change
- Mission problem
- Small terrain shifts, route traces, and infrastructure deformation are easy to miss in image products.
- Signal cue
- Coherent change, surface perturbations, and repeat-pass structure before the signal is compressed.
- Operational output
- Change maps for routes, sites, bridges, rail, dams, and other critical infrastructure.
Baltic / Strait of Hormuz
Maritime surveillance
- Mission problem
- AIS gaps, spoofed navigation, and noisy waterways reduce trust in vessel tracks.
- Signal cue
- Motion cues, wake structure, coherent changes, and repeat observations that survive poor visibility.
- Operational output
- Suspicious vessel cueing and anomaly maps for contested maritime areas.
These are deployment targets, not claims of completed operational validation. Customer studies should validate each mission against customer archives and agreed baselines.
Benchmarks — measured on real SAR archive data
Measured gains before mission deployment.
PerspeQtive benchmarks show stronger target contrast, class separation, and robustness under speckle noise before mission deployment. The next step is to test the same method on customer radar archives and operational baselines.
+0 dB
Target contrast gap
TCR median: +7.32 dB vs +0.74 dB
The target becomes easier to separate from clutter before classification.
0×
Class separation
Fisher: 0.413 vs 0.194
Object classes separate more clearly at the decision boundary.
+0 pp
Speckle robustness
Accuracy in noise: 71.6% vs 53.2%
Radar models remain useful under noisy SAR conditions.
Feature-level benchmarks on real SAR archive data, against like-for-like baselines. Customer studies validate the result on operational archives and agreed baselines.
Vision
Making raw radar usable by AI.
PerspeQtive is building the model system for radar intelligence: signal ingestion, richer representation, task models, evaluation, evidence, and secure deployment. Radar first: defense, maritime, infrastructure, and environmental intelligence.
The company that turns radar waves into operational intelligence.
Defense Maritime Infrastructure Environment Sovereign deployment
Founder
Built to make quantum mathematics useful in real-world AI.
Ruben Maarek founded PerspeQtive from a simple technical conviction: AI should learn from the physics of the signal, not only from images.
His work combines quantum machine learning, GPU simulation, and radar signal representation to build models that run on standard hardware, with no quantum computer required.
CentraleSupélec / NUS Singapore
Master in Quantum Machine Learning
GPU-accelerated quantum simulation
QbitSoft and J.P. Morgan experience
PerspeQtive core engine
Quantum mathematics applied to radar intelligence
Ruben Maarek
Founder & CEO
Quantum mathematics for radar intelligence
Start here
Test what your radar pipeline is missing.
Start with one archived SAR / SLC-IQ sample. PerspeQtive benchmarks full-signal radar AI against your current approach and delivers a Go/No-Go report for deployment.
Request a radar data study
ruben@perspeqtiveai.com
Data stays under NDA. PerspeQtive methods remain PerspeQtive IP.