L1 ⚛ L1-252 ⊙ Testnet

Inverse Synthetic Aperture Radar (ISAR) — rotating-target imaging

Electromagnetics · ISAR range-Doppler imaging of maneuvering targets

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⬇ L1-252.md

# ⚛ L1 Principle — Inverse Synthetic Aperture Radar (ISAR) — rotating-target imaging **ID:** `L1-252` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Electromagnetics — *ISAR range-Doppler imaging of maneuvering targets* > **🎯 Problem class:** linear inverse · **🧮 Solution space:** 2D target reflectivity > **📡 Carrier:** em · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41554000 --- ## 🧠 1. Introduction **Inverse Synthetic Aperture Radar (ISAR) — rotating-target imaging** is a **linear inverse problem** whose unknown lives in **2D target reflectivity** space, within the **ISAR range-Doppler imaging of maneuvering targets** sub-domain of **Electromagnetics**. Measurements consist of electromagnetic field measurements via a **radar pulse array** sensing mechanism. The forward operator applies, in order: L · pulse doppler operator; L · motion compensate operator; L · range doppler map operator; detector accumulates flux over the exposure window. Observations are corrupted by additive Gaussian noise. Conditional stability; mismatch parameters dominate at Omega bounds. ## ⚙ 2. Forward Model Physical chain: **x** → L · pulse doppler → L · motion compensate → L · range doppler map → Temporal integration → **y** (detector). ``` y = ∫_t dt `L.range_doppler_map` `L.motion_compensate` `L.pulse_doppler` x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.pulse_doppler` | L · pulse doppler operator | | `L.motion_compensate` | L · motion compensate operator | | `L.range_doppler_map` | L · range doppler map operator | | `int.temporal` | Detector accumulates flux over the exposure window | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Electromagnetics | | Sub domain | ISAR range-Doppler imaging of maneuvering targets | | Carrier | em | | Problem class | linear_inverse | | Solution space | 2D_target_reflectivity | | Noise model | gaussian | | Integration axis | temporal | | Difficulty delta | 5 | | L dag | 3.5 | ## 📡 4. Measurement Model Conditional stability; mismatch parameters dominate at Omega bounds. | Metric | Value | |---|---| | Metric | PSNR_dB | | Secondary | SSIM | ## 📏 5. Operating Range (Ω) **Center problem class:** `isar` · **Forward operator:** `isar_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | Prf hz | Hz | 1000 | | Snr db | dB | 20 | | N pulses | — | 256 | | Phase error | — | 0 | | N range bins | — | 256 | | Motion error | — | 0 | | Cross range aberration | — | 0 | | Doppler ambiguity error | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | Prf hz | Hz | 10 – 100000 | | Snr db | dB | -10 – 40 | | N pulses | — | 32 – 4096 | | Phase error | — | 0.0 – 0.2 | | N range bins | — | 64 – 4096 | | Motion error | — | 0.0 – 0.3 | | Cross range aberration | — | 0.0 – 0.3 | | Doppler ambiguity error | — | 0.0 – 0.3 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 22.0 | Metric | Range | |---|---| | Psnr db | 10.0 – 45.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **PSNR_dB**, with κ = `600` and δ = `5`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x096365c677c8ba5352962ca84cb8838e263f41fd3333297fa7916b30ef79b545` - **Chain tx hash:** `0x7a1c7ce4120ec5b42fc8b5708ab2631b8fee401941ef43d193b9aca857d6f769` - **Chain block:** `41554000` --- ## File Mapping This bundle consists of: `L1-252.md`, `L1-252.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-252.md` | Source of truth — edit this | Human or LLM | | `L1-252.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-252`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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Read the attached Markdown (L1-252.md).
Regenerate L1-252.json so every field matches the Markdown.
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L2-252-001 Inverse Synthetic Aperture Radar (ISAR) — rotating-target imaging Mismatch-Only Spec

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📋 JSON metadata

{
  "artifact_id": "L1-252",
  "chain_block": 41554000,
  "chain_hash": "0x096365c677c8ba5352962ca84cb8838e263f41fd3333297fa7916b30ef79b545",
  "chain_tx_hash": "0x7a1c7ce4120ec5b42fc8b5708ab2631b8fee401941ef43d193b9aca857d6f769",
  "domain": "Electromagnetics",
  "hardness_fn": {
    "delta": 5,
    "kappa": 600,
    "metric": "PSNR_dB",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "PSNR_dB",
    "regime": "Conditional stability; mismatch parameters dominate at Omega bounds.",
    "secondary": "SSIM"
  },
  "physics_fingerprint": {
    "L_DAG": 3.5,
    "carrier": "em",
    "difficulty_delta": 5,
    "domain": "Electromagnetics",
    "integration_axis": "temporal",
    "noise_model": "gaussian",
    "primitives": [
      "L.pulse_doppler",
      "L.motion_compensate",
      "L.range_doppler_map",
      "int.temporal"
    ],
    "problem_class": "linear_inverse",
    "sensing_mechanism": "radar_pulse_array",
    "solution_space": "2D_target_reflectivity",
    "sub_domain": "ISAR range-Doppler imaging of maneuvering targets",
    "title": "Inverse Synthetic Aperture Radar (ISAR) \u2014 rotating-target imaging"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "isar_forward"
    ],
    "allowed_omega_dimensions": [
      "N_pulses",
      "N_range_bins",
      "PRF_Hz",
      "SNR_dB",
      "motion_error",
      "phase_error",
      "doppler_ambiguity_error",
      "cross_range_aberration",
      "motion_error",
      "phase_error",
      "doppler_ambiguity_error",
      "cross_range_aberration"
    ],
    "allowed_problem_classes": [
      "isar"
    ],
    "center_spec": {
      "epsilon_fn_center": "22.0",
      "forward_operator": "isar_forward",
      "input_format": "measurement_only",
      "omega": {
        "N_pulses": 256,
        "N_range_bins": 256,
        "PRF_Hz": 1000,
        "SNR_dB": 20,
        "cross_range_aberration": 0.0,
        "doppler_ambiguity_error": 0.0,
        "motion_error": 0.0,
        "phase_error": 0.0
      },
      "problem_class": "isar"
    },
    "epsilon_bounds": {
      "psnr_db": [
        10.0,
        45.0
      ]
    },
    "omega_bounds": {
      "N_pulses": [
        32,
        4096
      ],
      "N_range_bins": [
        64,
        4096
      ],
      "PRF_Hz": [
        10,
        100000
      ],
      "SNR_dB": [
        -10,
        40
      ],
      "cross_range_aberration": [
        0.0,
        0.3
      ],
      "doppler_ambiguity_error": [
        0.0,
        0.3
      ],
      "motion_error": [
        0.0,
        0.3
      ],
      "phase_error": [
        0.0,
        0.2
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "ISAR range-Doppler imaging of maneuvering targets",
  "title": "Inverse Synthetic Aperture Radar (ISAR) \u2014 rotating-target imaging"
}

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