Demo — IADS Coverage Explorer

Demo — IADS Coverage Explorer#

This demo makes the layered-defense story tangible. Three coverage rings — an EW surveillance radar, an ACQ acquisition radar, and a TTR fire-control radar — are drawn to scale around a notional adversary site. Slide the target’s radar cross-section (RCS) and watch every ring collapse together, and read off exactly how many kilometers low-observable design buys at each layer.

The idea#

Each ring is a maximum detection range from the fourth-power law, \(R_{\max}\propto\sigma^{1/4}\). Drop the RCS and every ring shrinks by the same percentage — but the absolute kilometers removed are largest where the ring started largest.

\[ R_{\max}(\sigma) = R_{\text{ref}}\cdot 10^{\,\sigma_{\text{dBsm}}/40}. \]

Interactive demo#

Open in full screen

Walkthrough#

  1. Start at σ = 0 dBsm (legacy fighter). Note the layered ring of defenses — a wide EW ring, a medium ACQ ring, a small TTR ring, all centered on the site.

  2. Slide σ down to −30 dBsm (B-21). All three rings collapse. Watch the readout table: the EW ring loses the most absolute kilometers, while every radar shows the same percent reduction.

  3. Flip between presets. Jump between Legacy (0) and B-21 (−30) and confirm the L7 type-along’s claim — LO buys the most absolute kilometers at the outer (EW / ACQ) layer.

  4. Reposition a radar. Drag any radar marker to offset its coverage from the site, and add the AI ring to see the terminal layer close in even on a stealthy target.

Key observations#

  • Same percent, different absolute. The fourth-power law guarantees an identical fractional shrink at every layer; the outer rings simply have more kilometers to lose.

  • LO is standoff, not invisibility. Even at −30 dBsm the inner rings remain — the engagement layers still close in, which is why later blocks add active EW.

  • Geometry matters. Repositioning radars changes where the layered coverage actually overlaps — gaps in coverage are exactly what a mission planner hunts for.

Source#

MATLAB · code/L7_IADSRadarSurvey.m↓

The in-class script tabulates notional \(P_t\), \(G\), \(\lambda\), and \(S_{\min}\) for the EW, ACQ, TTR, and AI classes, computes \(R_{\max}\) against a \(1\ \text{m}^2\) target and a \(-30\) dBsm B-21, and shows where LO buys the most absolute kilometers.

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