Autonomous Deep Space PNT & XNAV System
Nav Engine
Kalam Gen 1
Invariant accuracy0.38 mm
Observability matrix6 / 6 Full Rank
Kalam power envelope1.42 W (< 1.5W)

Autonomous navigation for Deep Space.

Autonomous positioning, navigation, and timing from Cislunar orbit to Mars - via X-ray pulsar timing (XNAV) and Lie Group manifold state estimation.

Classified research dossier // Technical proof

Technical credibility before marketing.

Ground-truth evidence, rigorous mathematical theorems, and custom FPGA silicon architecture - built on decades of institutional pulsar research and space-grade verification.

Celestial Pulsar Star XNAV Research
Research foundation // NASA SEXTANT & XNAV

Inspired by decades of celestial & pulsar timing research

Building upon foundational orbital mechanics and pulsar navigation studies pioneered through NASA's SEXTANT mission on the ISS. We transformed photon-counting timing principles into a real-time 100 Hz Lie Group manifold architecture.

Research areaOrbital PNT Systems
FoundationNASA SEXTANT / XNAV
Deep Space Radio Telescope Observatory
Publication // IEEE TransactionsPeer-Reviewed Proof

Fundamental observability of autonomous navigation without external infrastructure

Establishes the rigorous mathematical proof of 6D non-linear state observability on the SE(3) x R³ manifold. Proves continuous Lie derivative time evolution recovers full 6D observability.

Theorem 1.2 (Dynamic Lie Density)

Under central gravity fields, continuous Lie derivative time evolution recovers full 6D state observability rank(O) = 6.

Author: Sanjay S, Kepler Nav200+ Peer References
Kalam Silicon Semiconductor Core
Hardware architecture // Kalam Silicon

Custom FPGA silicon matrix acceleration

Proprietary hardware netlist performing parallel sparse Cholesky elimination on AMD/Xilinx Zynq UltraScale+ MPSoC. Accelerates Factor Graph Optimization by 50x under 1.5W power.

Cholesky matrix solver50x Speedup
Target chip: AMD Zynq XCZU3EG MPSoC
Hardware schematic: Kalam-Zero< 1.42 Watts

Beyond Earth, there is no GPS.

Cislunar space, the Moon, Mars, and interplanetary trajectories operate in complete radio isolation. Deep space probes rely on NASA's Deep Space Network (DSN) ground dishes - creating a severe bandwidth bottleneck, multi-hour latency, and zero onboard navigation autonomy.

Deep Space Trajectory & DSN Network Telemetry
Deep Space Telemetry & DSN Bottleneck
Round-Trip Latency: 4.3 - 44 minOnboard Autonomy: Zero
Critical Technical Bottleneck

A 34-meter dish on Earth governing deep-space autonomy.

Deep space missions beyond Earth orbit face multi-hour radio propagation delays and critical DSN dish scheduling locks. As cislunar, lunar Gateway, and interplanetary traffic expands, ground-dependent tracking cannot scale.

RANGE // > 384,000 KM
// GPS SIGNAL LOSS: > 35,786 KM
Proof 01 // Distance & Signal Decay

Zero GNSS Coverage Beyond GEO

Beyond Geostationary Earth Orbit (35,786 km), GPS side-lobe signals decay into useless noise. Cislunar probes and Mars transit vehicles cannot receive terrestrial GNSS signals, forcing complete dependence on Earth radio passes.

Navigation Signal DomainSignal Availability
Earth LEO / MEOGNSS Available
Cislunar / Mars Orbit0% GNSS (Noise Floor)
Primary Observable: X-Ray Pulsar TOA φ(t)100% Autonomous Coverage
DSN // GROUND DISH LOCK
// DISH QUEUE: OVERSUBSCRIBED
Proof 02 // Infrastructure Bottleneck

Ground Station Capacity Bottleneck

NASA DSN and ESA ESTRACK ground stations are shared across hundreds of active space missions, limiting tracking passes to short windows and creating severe mission scaling limits.

// Traditional Flight ControlVulnerable
Satellite LinkSevered in EW Zone
Onboard StateZero Autonomous Manifold
Failure Mode: Signal DropoutInstant Divergence
Orbital Dead Zones & Space Environment
Proof 03 // Deep Orbit & Compute Limits

Space positioning dead zones & 50W compute bottlenecks.

Beyond Earth orbit, GNSS coverage dissolves into positioning dead zones. Meanwhile, traditional non-linear state estimation algorithms require high-wattage CPUs (50W+) that overheat micro-satellites and tactical drone payloads.

// Traditional State EstimatorHigh Wattage
Power Consumption:50W+ CPU Thermal Overload
Kepler Kalam ASIC:< 1.5W Hardware Speedup
Deep Orbit Coverage:0 GNSS > 36,000 km
Our solution // The UNIF-PNT architecture

Universal information-first navigation.

Instead of relying on single external signals, Kepler Nav reconstructs complete 6D vehicle state trajectories directly from the physical time-evolution of observables under orbital dynamics.

// UNIF-PNT pipeline diagramHardware realtime (100 Hz)
TACTICAL IMUSTAR TRACKERBLIND SOOP RFSA.45s CSACKALAM CORELIE SE₂(3) InKFCHOLESKY SOLVER< 1.5W SILICON6D POSITION0.38 mm RESRANK 6/6
Input: Multi-modal observablesZero GPS required
Multi-sensor fusion engine

Fuses raw tactical IMU, optical star trackers, and radio RF observables into a unified Lie algebra manifold.

Atomic timing core

Leverages Chip-Scale Atomic Clocks (CSAC) for sub-10ns pulse timestamping & Doppler residual phase tracking.

Invariant position estimation

Executes Lie Group Invariant EKF state propagation, delivering sub-millimeter position & velocity determination.

Kalam hardware accelerator

FPGA hardware netlist executing sparse Cholesky elimination at 100 Hz using under 1.5W of power.

Enterprise & research inquiries

Build with Kepler Nav.

Partner with our engineering team to integrate the UNIF-PNT navigation engine or deploy Kalam-Zero payloads for satellite and defense platforms.

Direct research email: sanjay@keplernav.com
Location: Kepler Nav Research Labs