Implement Threat Assessment Layer and Fleet-Scale Swarm Tracking

.claude/launch.json

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+{
+  "version": "0.0.1",
+  "configurations": [
+    {
+      "name": "radarsense-frontend",
+      "runtimeExecutable": "npm",
+      "runtimeArgs": ["--prefix", "frontend", "run", "dev"],
+      "port": 3000,
+      "autoPort": true
+    }
+  ]
+}

TODO.md

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+# RadarSense — Feature Inventory & Roadmap
+
+_Generated 2026-07-30. Recap of what already exists in the repo, plus a prioritized
+backlog of what to add next._
+
+## 1. What's already built
+
+**Radar DSP & real-sensor pipeline**
+- Real 77GHz I/Q → range-Doppler FFT → CA-CFAR → Kalman tracking (`radarsense/radar_dsp.py`)
+- 4 real sensors bridged into the frozen 11-feature schema: SAAB 77GHz FMCW, UPM RAD-DAR 8.75GHz,
+  TSMS-Drone 2.5GHz CW, DIAT-µSAT X-band (`real_data_bridge.py`, `raddar_experiments.py`,
+  `tsms_experiments.py`, `diat_usat_bridge.py`)
+- Multi-target tracker (OS-CFAR + GNN association + Kalman bank) validated on **3** synthetic
+  targets per scene — see gap #1 below (`radarsense/multi_target_tracking.py`)
+- Multi-sensor fusion module (`radarsense/multi_sensor_fusion.py`)
+- Live acoustic (phone-mic) drone demo as a second real sensor modality (`acoustic_demo.py`)
+
+**Classification core**
+- 6-model bake-off (LogReg/RF/BalancedRF/XGBoost/CatBoost/SVM), 5-fold CV (`train_baseline.py`)
+- Minority-class optimization: SMOTE vs class-weighting vs combined, F2-tuned threshold
+  (`optimize.py`)
+- Isotonic calibration + reliability diagrams (`calibrate.py`)
+- Three feature-schema variants in production: classic 11-feature, 31-feature, 42-feature
+  combined (`feat31_*.py`, `feat42_*.py`)
+- Split-conformal uncertainty (90% coverage guarantee)
+
+**Explainability**
+- SHAP summary + per-detection waterfall (`explain.py`, `feat31_explain.py`, `feat42_explain.py`,
+  `shap_corrected.py`)
+- LIME tabular explainer for classical + quantum models (`ensemble_decision_explainer.py`,
+  `feat42_explain.py`) — **offline only**, writes JSON/plots, not exposed live
+- Frontend currently only has a lightweight **client-side approximation** of SHAP
+  (`getShapContributions` in `frontend/app/page.tsx`), not the real backend SHAP/LIME output
+
+**Robustness / security**
+- Certified adversarial robustness via randomized smoothing (83% provably un-evadable)
+- Physical-cost-constrained evasion study (100% unconstrained → ~22% achievable)
+- Micro-Doppler masking attack + tunable masking defense/rescue (`masking_rescue.py`, wired into
+  `/predict/classic`)
+- Model-extraction attack + defense (`model_extraction_attack.py`, `extraction_defense*.py`)
+- Adversarial training (`adversarial_training.py`)
+
+**Quantum research**
+- QSVC fixed-kernel ablation vs classical RBF/linear (`quantum.py`)
+- Trainable VQC, two SDKs: Qiskit (COBYLA/SPSA) and PennyLane (gradient descent rescue)
+  (`vqc.py`, `vqc_improved.py`, `vqc_pennylane.py`)
+- Real IBM hardware runs, error-mitigated (`ibm_hardware_kernel*.py`)
+- Classical/quantum stacking ensemble (`classical_quantum_ensemble*.py`)
+- Honest negative result reported throughout (classical wins at tested scales)
+
+**Deployment**
+- ONNX export, 1.0000 parity, 0.03ms median latency (`export_onnx.py`)
+- Unified FastAPI server exposing all 4 model variants: classic/31/42/quantum
+  (`api/app.py`, plus standalone `server.py`, `quantum_server.py`, `feat31_server.py`,
+  `feat42_server.py`)
+- Next.js live simulator: client-side ONNX inference (onnxruntime-web), SNR presets, target
+  subtype presets, theme toggle, session stats (`frontend/app/page.tsx`,
+  `RadarSimulator.tsx` — 2.7k lines)
+- `hard/page.tsx` — 3D radar visualization variant
+- Threat Assessment Layer: deterministic doctrine engine on top of the classifier output —
+  proximity/closing/TTI/altitude/evasion/uncertainty scoring, CRITICAL/ELEVATED/MONITOR/CLEAR
+  levels (`frontend/app/components/threatAssessment.ts`)
+- Arduino Nano hardware-in-the-loop demo
+
+**Testing / CI**
+- `pytest tests -q` + GitHub Actions CI (`tests/`, `.github/workflows/ci.yml`)
+
+---
+
+## 2. Gaps / new features to add
+
+### 🔴 High priority (explicitly requested — keep these)
+
+1. **Fleet-scale swarm detection (100–1000 simultaneous drones)**
+   Current `multi_target_tracking.py` is only validated at N=3 targets on a synthetic scene, and
+   the frontend renders individually-spawned targets one at a time — neither the tracker nor the
+   UI has been proven at swarm scale. Needed:
+   - Stress-test the OS-CFAR + GNN + Kalman-bank tracker at 100 / 500 / 1000 simultaneous targets;
+     measure cardinality error, spurious-track rate, and association latency at scale (greedy GNN
+     is O(n²) per frame — likely needs spatial gating/binning or a faster assignment algorithm,
+     e.g. k-d tree gating or Hungarian-on-gated-subsets, once target count gets large)
+   - Backend: a **fleet/session aggregation layer** — dedupe tracks, roll up per-fleet stats
+     (count by class, count by threat level, closest/most-urgent track), swarm-pattern detection
+     (many tracks with correlated heading/velocity = coordinated swarm behavior, not independent
+     birds/aircraft — a distinct threat signature worth flagging on its own)
+   - Frontend: a **swarm view mode** — individual icons stop scaling past some threshold (~30–50
+     tracks); switch to clustered/density rendering (heatmap or aggregated cluster markers) with
+     drill-down, plus virtualized list rendering for the detection table so 1000 rows doesn't
+     tank the UI
+   - API: batch endpoint already exists (`/batch` per `api/server.py`) for bulk classification —
+     but no endpoint yet for ingesting/tracking a whole scene's worth of detections per frame and
+     returning fleet-level state
+
+2. **PDF export of SHAP / LIME explanations**
+   SHAP and LIME both exist today (`explain.py`, `feat42_explain.py`,
+   `ensemble_decision_explainer.py`) but only as offline scripts that write JSON + matplotlib
+   PNGs — nothing generates a shareable report, and the frontend's "SHAP" panel is actually a
+   client-side approximation, not the real backend output. Needed:
+   - Backend: an export endpoint (e.g. `/explain/{detection_id}/pdf`) that renders the real SHAP
+     waterfall + LIME feature-weight breakdown for a given detection into a PDF (reportlab or
+     weasyprint; matplotlib figures embed cleanly into either)
+   - Bundle explainability report generation for a whole session (all detections in a run), not
+     just one detection at a time
+   - Frontend: an "Export PDF Report" button on the detection/inspection panel that calls the
+     endpoint and downloads the file
+   - Decide whether to wire the frontend to real backend SHAP values instead of the client-side
+     approximation now that there's a reason to (the PDF must be trustworthy, not the JS heuristic)
+
+### 🟡 Other suggested features
+
+- **Real SHAP/LIME in the live API**, not just offline scripts — an `/explain` endpoint the
+  frontend and PDF exporter both call, replacing the JS approximation
+- **Persistent track/session history** — currently everything is in-memory in the browser tab;
+  no database to query "what did we see last Tuesday" or replay a session
+- **Alerting/notification hooks** — CRITICAL threat level currently only changes UI color; no
+  webhook/email/SMS integration for unattended operation
+- **WebSocket streaming** instead of polling/local simulation loop, for when a real sensor feed
+  (or the fleet-scale scenario above) needs push updates rather than client-side generation
+- **Auth / role-based access** on the FastAPI server — currently open CORS, no auth, fine for a
+  demo but not for anything resembling deployment
+- **Geo/map integration** — the scope is a synthetic 2D polar display; no real-world coordinate
+  system or map overlay
+- **Drift-monitoring dashboard** — `feature_drift.py` exists as a script; not surfaced anywhere
+  operators would see it live
+- **Frontend E2E tests** — `tests/` covers the Python pipeline; no Playwright/Cypress coverage of
+  `RadarSimulator.tsx`
+- **Containerization** — no Dockerfile/docker-compose for the API + frontend + (optional) quantum
+  backend, despite `start_all.sh` running them as local processes
+
+---
+
+## 3. Suggested sequencing
+
+1. PDF export (contained, no architecture change, high demo value)
+2. Fleet-scale stress test + backend aggregation (validates the swarm claim before touching UI)
+3. Fleet swarm view in frontend (depends on #2's aggregation shape)
+4. Real SHAP/LIME live endpoint (unlocks trustworthy PDF content + removes the JS approximation)
+5. Everything else, prioritized by whether there's a demo/judging deadline driving it

api/alerting.py

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+"""Alerting hooks: dispatch a simulated notification fan-out when a track's
+threat level goes CRITICAL, and (optionally) a real webhook call.
+
+IMPORTANT -- what this does and does NOT do:
+- The "recipient roster" below is entirely fictional/demo data: generic
+  role titles (not real named officials or real agencies), RFC 2606
+  reserved example.com addresses, and 555-01xx phone numbers (the
+  universally-recognized fake-number convention). NO real SMS or email is
+  ever sent to anyone -- every dispatch is logged as SIMULATED.
+- The one REAL side effect this module can have is an optional generic
+  webhook POST, and only if the operator sets RADARSENSE_ALERT_WEBHOOK_URL
+  themselves (e.g. to their own Slack/Discord incoming-webhook URL) -- that
+  is the actual integration point a real deployment would wire up, kept
+  separate from the simulated roster so the two are never confused.
+
+Rate-limited per track (COOLDOWN_S) so one persistent CRITICAL track doesn't
+spam the log every polling tick.
+
+Real personal email delivery (separate from the simulated roster above): if
+RADARSENSE_SMTP_PASS is set (a Gmail App Password -- myaccount.google.com ->
+Security -> App Passwords -- generated and stored by the operator, never
+handled by this code beyond reading the env var), dispatch_alert also sends
+a real email via Gmail SMTP to RADARSENSE_ALERT_EMAIL_TO. SMS to a real
+number is intentionally NOT implemented -- it needs a paid third-party
+provider account (Twilio etc.) the operator would have to set up themselves;
+until then the personal phone number is shown in the response as a labeled,
+explicitly-simulated entry, same as the fictional roster.
+"""
+
+import os
+import smtplib
+import time
+import uuid
+from collections import OrderedDict
+from email.message import EmailMessage
+
+import httpx
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+
+router = APIRouter(prefix="/alert", tags=["alert"])
+
+COOLDOWN_S = 30
+MAX_HISTORY = 200
+WEBHOOK_URL = os.environ.get("RADARSENSE_ALERT_WEBHOOK_URL", "").strip()
+WEBHOOK_TIMEOUT_S = 4.0
+
+SMTP_HOST = os.environ.get("RADARSENSE_SMTP_HOST", "smtp.gmail.com")
+SMTP_PORT = int(os.environ.get("RADARSENSE_SMTP_PORT", "587"))
+SMTP_USER = os.environ.get("RADARSENSE_SMTP_USER", "trivikramnarayanan@gmail.com")
+SMTP_PASS = os.environ.get("RADARSENSE_SMTP_PASS", "").strip()
+ALERT_EMAIL_TO = os.environ.get("RADARSENSE_ALERT_EMAIL_TO", "trivikramnarayanan@gmail.com")
+PERSONAL_PHONE = os.environ.get("RADARSENSE_ALERT_PHONE", "7358084532")
+
+# Fictional demo roster -- see module docstring. Titles are generic/regional,
+# not tied to any real agency; contacts use IANA-reserved example.com and the
+# 555-01xx fake-number block on purpose.
+ROSTER = [
+    {"role": "Regional Air Defense Command", "channel": "sms",
+     "contact": "+1-555-0101 (demo)"},
+    {"role": "Regional Air Defense Command", "channel": "email",
+     "contact": "air-defense-command@alerts.example.com"},
+    {"role": "Counter-UAS Coordination Cell", "channel": "sms",
+     "contact": "+1-555-0102 (demo)"},
+    {"role": "Counter-UAS Coordination Cell", "channel": "email",
+     "contact": "counter-uas-cell@alerts.example.com"},
+    {"role": "Civil Administration Liaison", "channel": "email",
+     "contact": "civil-liaison@alerts.example.com"},
+    {"role": "Local Law Enforcement Control Room", "channel": "sms",
+     "contact": "+1-555-0103 (demo)"},
+]
+
+_history: "OrderedDict[str, dict]" = OrderedDict()
+_last_dispatch: dict = {}   # track_id -> last dispatch epoch seconds
+
+
+class DispatchRequest(BaseModel):
+    track_id: str
+    predicted_class: str
+    threat_level: str
+    threat_score: float
+    range_m: float
+    closing_mps: float
+    tti_sec: float | None = None
+    rationale: str = ""
+
+
+@router.get("/roster")
+def get_roster():
+    return {"roster": ROSTER,
+            "note": "Simulated demo roster -- fictional roles, RFC2606 example.com "
+                    "addresses and 555-01xx numbers. No real SMS/email is ever sent."}
+
+
+@router.post("/dispatch")
+def dispatch_alert(req: DispatchRequest):
+    now = time.time()
+    last = _last_dispatch.get(req.track_id, 0)
+    if now - last < COOLDOWN_S:
+        return {"dispatched": False,
+                "reason": f"cooldown active ({COOLDOWN_S - (now - last):.0f}s remaining)"}
+    _last_dispatch[req.track_id] = now
+
+    alert_id = uuid.uuid4().hex[:12]
+    recipients = [
+        {**r, "status": "simulated_sent", "simulated": True}
+        for r in ROSTER
+    ]
+
+    webhook_result = None
+    if WEBHOOK_URL:
+        webhook_result = _fire_webhook(req, alert_id)
+
+    email_result = _send_email(req, alert_id) if SMTP_PASS else \
+        {"attempted": False, "reason": "RADARSENSE_SMTP_PASS not set -- see api/alerting.py docstring"}
+
+    personal_contacts = [
+        {"role": "Operator (personal)", "channel": "email", "contact": ALERT_EMAIL_TO,
+         "simulated": False, "status": "sent" if email_result.get("ok") else "not_sent",
+         "detail": email_result},
+        {"role": "Operator (personal)", "channel": "sms", "contact": PERSONAL_PHONE,
+         "simulated": True, "status": "simulated_sent",
+         "detail": {"reason": "real SMS needs a provider account (Twilio etc.) -- not configured"}},
+    ]
+
+    record = {
+        "alert_id": alert_id,
+        "timestamp": now,
+        "track_id": req.track_id,
+        "predicted_class": req.predicted_class,
+        "threat_level": req.threat_level,
+        "threat_score": req.threat_score,
+        "range_m": req.range_m,
+        "closing_mps": req.closing_mps,
+        "tti_sec": req.tti_sec,
+        "rationale": req.rationale,
+        "recipients": recipients,
+        "personal_contacts": personal_contacts,
+        "webhook": webhook_result,
+    }
+    _history[alert_id] = record
+    while len(_history) > MAX_HISTORY:
+        _history.popitem(last=False)
+
+    return {"dispatched": True, "alert": record}
+
+
+def _send_email(req: DispatchRequest, alert_id: str) -> dict:
+    """Real email via Gmail SMTP + App Password. Only runs if SMTP_PASS is
+    set (see module docstring) -- never attempted otherwise."""
+    msg = EmailMessage()
+    msg["Subject"] = f"[RadarSense] CRITICAL threat — {req.predicted_class.upper()} track {req.track_id}"
+    msg["From"] = SMTP_USER
+    msg["To"] = ALERT_EMAIL_TO
+    lines = [
+        f"RadarSense threat alert (alert_id {alert_id})",
+        "",
+        f"Track:            {req.track_id}",
+        f"Predicted class:  {req.predicted_class}",
+        f"Threat level:     {req.threat_level} (score {req.threat_score:.0f})",
+        f"Range:            {req.range_m:.0f} m",
+        f"Closing speed:    {req.closing_mps:.1f} m/s",
+    ]
+    if req.tti_sec is not None:
+        lines.append(f"Time to intercept: {req.tti_sec:.0f}s")
+    lines.append(f"Rationale:        {req.rationale}")
+    msg.set_content("\n".join(lines) + "\n")
+    try:
+        with smtplib.SMTP(SMTP_HOST, SMTP_PORT, timeout=10) as server:
+            server.starttls()
+            server.login(SMTP_USER, SMTP_PASS)
+            server.send_message(msg)
+        return {"attempted": True, "ok": True}
+    except Exception as e:
+        return {"attempted": True, "ok": False, "error": str(e)}
+
+
+def _fire_webhook(req: DispatchRequest, alert_id: str) -> dict:
+    payload = {
+        "text": f"[RadarSense] CRITICAL threat — track {req.track_id} "
+                f"({req.predicted_class}), range {req.range_m:.0f}m, "
+                f"closing {req.closing_mps:.1f}m/s. {req.rationale}",
+        "alert_id": alert_id,
+    }
+    try:
+        resp = httpx.post(WEBHOOK_URL, json=payload, timeout=WEBHOOK_TIMEOUT_S)
+        return {"attempted": True, "status_code": resp.status_code, "ok": resp.is_success}
+    except Exception as e:
+        return {"attempted": True, "ok": False, "error": str(e)}
+
+
+@router.get("/history")
+def get_history(limit: int = 50):
+    items = list(_history.values())[-limit:]
+    items.reverse()
+    return {"count": len(_history), "alerts": items,
+            "webhook_configured": bool(WEBHOOK_URL)}
+
+
+@router.delete("/history")
+def clear_history():
+    _history.clear()
+    _last_dispatch.clear()
+    return {"cleared": True}