Tactical Edge AI & Offline Computer Vision Architecture

1. Mission Context & DDIL Requirements

Modern tactical edge operations occur in Disconnected, Intermittent, and Low-Bandwidth (DDIL) environments where reliable high-speed satellite or cellular backhaul to central cloud infrastructure is impossible or dangerous due to electronic counter-measures.

This architecture blueprint specifies the hardware, software, and cryptographic design for deploying real-time Computer Vision (CV) and sensor fusion on ruggedized edge units, delivering autonomous threat detection without emitting detectable radio frequency signatures.

2. Edge Hardware & Software Architecture Stack

+-----------------------------------------------------------------------------------+
|               MULTISPECTRAL SENSORS (Thermal, EO 4K, LiDAR, AIS)                  |
+-----------------------------------------------------------------------------------+
                                         | (GigE Vision / RTSP over TLS)
                                         v
+-----------------------------------------------------------------------------------+
|               HARDENED EDGE COMPUTE (NVIDIA Jetson AGX Orin 64GB)                 |
|                                                                                   |
|   +-----------------------+    +-----------------------+    +-----------------+   |
|   | GStreamer Pipeline    |--->| TensorRT INT8 Engine  |--->| Threat Alert    |   |
|   | (Hardware Accelerated)|    | (YOLOv10 DeepStream)  |    | Dispatch Engine |   |
|   +-----------------------+    +-----------------------+    +-----------------+   |
|                                            |                          |           |
|                                            v                          v           |
|                              [ Encrypted Local DB ]        [ LoRa / UHF Tactical ]|
|                              [ (SQLite AES-256)   ]        [ Short Burst Alert   ]|
+-----------------------------------------------------------------------------------+

3. Real-Time TensorRT Pipeline Implementation

Below is a Python snippet demonstrating asynchronous inference on edge hardware with hardware-accelerated zero-copy memory buffers:

import cv2
import numpy as np
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit

class TacticalVisionInference:
    def __init__(self, engine_path="models/yolov10_int8.engine"):
        self.logger = trt.Logger(trt.Logger.WARNING)
        with open(engine_path, "rb") as f, trt.Runtime(self.logger) as runtime:
            self.engine = runtime.deserialize_cuda_engine(f.read())
        self.context = self.engine.create_execution_context()
        
        # Allocate pinned host & device buffers
        self.input_shape = self.engine.get_binding_shape(0)
        self.output_shape = self.engine.get_binding_shape(1)
        self.h_input = cuda.pagelocked_empty(trt.volume(self.input_shape), dtype=np.float32)
        self.d_input = cuda.mem_alloc(self.h_input.nbytes)
        self.h_output = cuda.pagelocked_empty(trt.volume(self.output_shape), dtype=np.float32)
        self.d_output = cuda.mem_alloc(self.h_output.nbytes)
        self.stream = cuda.Stream()

    def detect_frame(self, frame):
        # Preprocess frame without CPU copy
        resized = cv2.resize(frame, (640, 640)).astype(np.float32) / 255.0
        np.copyto(self.h_input, resized.ravel())
        
        # Asynchronous CUDA memory transfer & inference
        cuda.memcpy_htod_async(self.d_input, self.h_input, self.stream)
        self.context.execute_async_v2(bindings=[int(self.d_input), int(self.d_output)], stream_handle=self.stream.handle)
        cuda.memcpy_dtoh_async(self.h_output, self.d_output, self.stream)
        self.stream.synchronize()
        
        return self.h_output

4. Store-and-Forward Cryptographic Data Sync

1. Local Enclave Encryption: Every video frame triggering a positive classification event is saved to an encrypted NVMe partition using Linux dm-crypt with an ephemeral key bound to the motherboard TPM 2.0.
2. Opportunistic Burst Sync: When an approved tactical data link (e.g. secure satellite burst or low-probability-of-intercept mesh) is established, detection metadata and cryptographically signed hashes are transmitted first, with raw video payloads queued for deferred transfer.
3. Zero Tamper Persistence: Attempted chassis intrusion immediately triggers zeroization of local cryptographic keys via internal capacitor pulse.

5. Field Operational Checklist

• Thermal Stability Stress Test: Operate unit continuously under 100% GPU saturation at +55°C ambient temperature for 72 hours.
• MIL-STD-810H Vibration: Validate camera mount and compute module maintain optical alignment under sustained 5G shock testing.
• Zero Cloud Dependency: Verify unit boots, runs, and outputs audio/tactile alerts with the WAN fiber cable completely severed.
• False Positive Calibration: Confirm classification threshold achieves >98.5% precision against drone and unauthorized vehicle classes.