Real-Time Sensor Fusion
and Inference Architectures

1 — The Latency Stack

Human reaction time is a compounding biological pipeline. The machine replaces each stage with a hardware equivalent one to three orders of magnitude faster. The gap is not marginal. The argument against machine autonomy in these contexts is doctrinal and legal — not capability-based.

2 — Interconnect Fabrics

The bus is frequently the actual bottleneck. Legacy serial buses consume the entire latency budget before inference begins. The progression from MIL-STD-1553 to TSN is five decades of architectural rethinking, not iteration.

3 — FPGA in the Signal Path

FPGAs synthesize programmable gate logic at clock rates in the hundreds of MHz with deterministic, cycle-accurate timing. The correct frame: the FPGA is the sensor interface. Inference silicon is downstream of it.

• ADC conditioning — Raw samples from a MEMS IMU, radar IF stage, or FLIR digitizer are decimated, windowed, and FFT'd inside the fabric before the data reaches system memory. Zero CPU cycles consumed in the hot path.
• Protocol bridging — EtherCAT-to-CAN, AFDX framing, PCIe DMA descriptor generation all at line rate with no software overhead. Xilinx Versal or Intel Agilex handles multi-protocol translation in the same device that runs hardened AI engines.
• Hardware timestamping — Every inbound sample tagged at capture time using IEEE 1588 PTP. Sub-100ns synchronization across multiple sensors. The mechanism that enables coherent multi-modal fusion.
• Partial reconfiguration — Modern FPGAs reprogram sections of fabric at runtime while other sections run. Relevant for field-updateable sensor processing pipelines without system downtime.

4 — Sensor Fusion Architecture

The Kalman filter alignment stage projects all sensor readings to a common time reference. At 20Hz radar that is a 50ms gate regardless of FPGA or ASIC speed. Spatial calibration via extrinsic matrices must be pre-solved — errors here are systematic and will not be corrected downstream.

5 — Inference Silicon

6 — AI Architectures

7 — Consumer and Hobbyist Crossover

The same architectural stack governing avionics pipelines is directly accessible with commodity hardware. The buses are identical. The inference silicon fits in M.2 slots. The latency constraints are comparable to active vehicle systems.

• CAN bus / CANFD — Every modern vehicle exposes wheel speed, yaw rate, throttle position, and collision radar over CAN. A PEAK PCAN-USB or Canable adapter gives socketcan on Linux at the same layer as the factory ECU. CANFD at 8Mbps with 64-byte payloads is sufficient for compressed sensor telemetry.
• EtherCAT — Sub-100µs deterministic motion control on commodity 100Mbps Ethernet. A Raspberry Pi with a Beckhoff EK1100 coupler closes a real-time loop suitable for active suspension or steering feedback. The slave reads its PDO as the frame passes through — the frame never stops and never stores.
• Hailo-8 M.2 on Pi 5 — 26 TOPS at 2.5W for under $100 in silicon. YOLO object detection at 60fps. Practical for dash-mounted classifiers, obstacle detection, or any pipeline previously requiring a discrete GPU.
• RTL-SDR + IQ classifiers — The EW/SDR layer maps directly to a $25 RTL-SDR dongle running a trained RF fingerprinting CNN. Emitter classification at 10dB SNR. The same technique applied to CAN differential waveforms fingerprints ECU nodes and detects spoofed messages.
• IEEE 1588 PTP — Available on commodity NICs (Intel I210, I225). Sub-microsecond sync across sensor devices. Required for any multi-modal fusion pipeline making sub-10ms latency claims.
• Event cameras (DVS346, Prophesee Metavision) — Asynchronous pixel-change sensors. Microsecond temporal resolution, 120dB dynamic range, zero motion blur. Still premium hardware; price trajectory is steep. Native input for SNN inference on Loihi 2.

8 — Synchronization Pipeline

1. Acquisition Timestamping
   Hardware-timestamp every sample at the sensor or FPGA input register. A software timestamp at the driver layer adds 1–50ms of OS scheduling jitter and invalidates sub-10ms latency claims. Use IEEE 1588 PTP on a hardware-timestamping NIC, or a 1PPS GPIO pulse from a GNSS receiver.
2. Temporal Alignment
   Project all sensor readings to the most recent common timestamp via a Kalman filter or linear interpolation. The alignment window gates on the slowest modality. At 20Hz radar that is a 50ms window regardless of everything downstream.
3. Spatial Calibration
   All sensors must share a coordinate frame via pre-solved extrinsic calibration matrices. Errors here are systematic and will not be corrected downstream. Calibrate with a checkerboard at multiple distances and validate residuals before trusting fusion output.
4. Fused State Vector Construction
   Construct the input tensor from aligned, calibrated multi-modal data. Normalization and format must match training-time preprocessing exactly. Mismatched normalization produces silent degradation with no obvious error signal.
5. Inference Dispatch
   DMA the input tensor from FPGA shared memory or pinned host memory directly to the inference accelerator. Avoid CPU copies. A PCIe 5.0 DMA transfer of a 640×640×3 fp16 frame completes in under 100µs.

9 — RF Intelligence and EW Crossover

• RF fingerprinting — Oscillator and PA imperfections create unique per-transmitter spectral signatures. CNNs on raw IQ samples classify transmitter identity at 95%+ accuracy at 10dB SNR. The same technique on CAN differential waveforms fingerprints ECU nodes and detects spoofed messages.
• TDOA geolocation — Time difference of arrival across PTP-synchronized receivers on a 10m baseline geolocates a 433MHz source to meter-level accuracy. Used in avionics EW for radar emitter localization; directly applicable to unauthorized RF hunting.
• Micro-Doppler classification — FMCW radar IQ (same format as TI AWR1843 automotive radar) classifies object type from Doppler signature. Human gait, vehicle, bicycle, and drone each produce distinct profiles detectable by a lightweight CNN without frame-based imaging.
• SDR classifier pipeline — An RTL-SDR or HackRF feeding a trained signal classification model identifies emitter type, modulation scheme, and protocol in under 100ms. The same task on a scope with a reference library takes a human operator seconds to minutes.

10 — F-35 Mission Systems: A Reference Integration

Everything above converges in one airframe. The F-35 pulls radar, electro-optical, infrared, and electronic-warfare returns from apertures spread across the skin, timestamps and fuses them on a central processor, and hands the pilot a single track picture instead of five separate sensor feeds. The fusion engine, not any individual sensor, is the weapon. It is the clearest production example of the sensor-to-decision pipeline in this document.

Compute backbone

The Integrated Core Processor is the brain. One open-architecture computer runs radar and EO/IR signal processing, electronic warfare, the fusion engine, and the cockpit and helmet displays. Technology Refresh 3 swapped in an L3Harris ICP rated at roughly 25 times the compute of the prior generation, paired with a 20x-larger Aircraft Memory System and a new display electronics unit. That headroom is the precondition for the Block 4 capability set.

Data backbone

• Fibre Channel — optical fabric that ties the ICP modules together like a backplane and carries sensor, CNI, and display traffic at multiple Gbps. The high-bandwidth path from aperture to fusion engine.
• IEEE 1394b / AS5643 — the vehicle systems network: 70+ nodes for flight controls, engine, fuel, weapons, and utilities. A deterministic, militarized FireWire chosen over MIL-STD-1553 for bandwidth and cable length.
• Two networks, two jobs — high-bandwidth sensor and display data stays on the optical fabric; slower vehicle traffic is isolated on the 1394b bus. The split keeps fusion latency off the same wire as engine and flight-control housekeeping.

Software and partitioning

A safety-critical RTOS sits under all of it. The F-35 runs Green Hills INTEGRITY-178B, an ARINC 653 partitioned kernel that gives every application a guaranteed time slice and an isolated memory space, so flight-critical and mission code share one processor with provable determinism. The airborne load is roughly 8.6 million lines (Block 3F), about 24 million including the ALIS/ODIN ground suite, written predominantly in C and C++ under the JSF Air Vehicle C++ standard. That standard bans exceptions, recursion, and dynamic allocation after initialization to keep timing and memory bounded.