Strategies and Technologies for Modern Drone Defense Security
/* ==================== THREAT SCORING
==================== */
function getThreatScore(d) {
const cx = canvas.width / 2;
const cy = canvas.height /
2;
let dx = d.x - cx;
let dy = d.y - cy;
let
distance = Math.max(1, Math.sqrt(dx*dx + dy*dy));
let
speed = Math.sqrt(d.vx * d.vx + d.vy * d.vy);
let
swarmRadius = 50 + wave * 8;
let swarm = drones.filter(o
=>
o !== d && Math.hypot(o.x - d.x, o.y - d.y) <
swarmRadius
).length;
let dirToCenterX = -dx /
distance;
let dirToCenterY = -dy / distance;
let
alignment = (d.vx * dirToCenterX + d.vy * dirToCenterY);
let directionBonus = Math.max(0, alignment) * 35;
let
altitudeFactor = d.altitude === "Low" ? 68 :
(d.altitude
=== "Medium" ? 32 : 12);
let patternBonus = 0;
if
(d.pattern === "Fast Rush") patternBonus = 45;
else if
(d.pattern === "Erratic") patternBonus = 38;
else if
(d.pattern === "Sneaky Slow") patternBonus = 15;
let
targetingCount = interceptors.filter(i => i.target ===
d).length;
let multiTargetBonus = targetingCount * 22;
let rawScore =
(1 / distance) * 140 +
speed * 52
+
swarm * 28 +
directionBonus +
altitudeFactor +
patternBonus +
multiTargetBonus;
let normalized =
Math.min(100, Math.round(rawScore / 4.5));
return {
raw: Math.round(rawScore),
normalized: normalized,
altitude: d.altitude,
speed: Math.round(speed * 10) / 10,
pattern: d.pattern
};
}
(1 / distance) * 120speed * 45swarm * 25| Drone | Distance | Speed | Swarm Count | Threat Score | Priority |
|---|---|---|---|---|---|
| Drone A (Critical) | 30px | 4.2 | 3 | 268 | Highest |
| Drone B (Low) | 380 px | 1.8 | 0 | ≈ 81 | Low |
Military and commercial counter-drone systems evaluate threats using Capability • Opportunity • Intent. Our algorithm mirrors core kinematic principles used in modern TEWA (Threat Evaluation and Weapon Assignment) systems.
Factor |
Our Simulation |
Real-World Systems |
Realism Level |
|---|---|---|---|
|
Distance / Range |
Inverse distance (×120) |
Range, Time-to-Impact, Proximity |
Excellent |
|
Speed / Velocity |
Direct multiplier (×45) |
Velocity + Maneuverability |
Strong |
|
Group Behavior |
Swarm count within radius |
Swarm / Density detection |
Good |
|
Direction / Intent |
Not yet included |
Heading toward asset |
Recommended addition |
Key Insight: Real systems (Dedrone, Cambridge Pixel, Indra, etc.) combine sensor fusion, AI behavior analysis, and probabilistic scoring. Our current model provides a fast, effective kinematic foundation that can be extended with intent, classification, and altitude modeling.
Protecting the defended asset is always the highest priority — exactly as in battlefield air defense.
Fast threats can bypass defenses if not addressed quickly.
Encourages breaking up coordinated attacks before saturation occurs.
Our simulation and real-world systems use a sophisticated kinematic threat scoring algorithm that prioritizes drones based on distance, speed, swarm behavior, direction toward the asset, and existing engagement status.
| Factor | Our Approach | Real-World Alignment |
|---|---|---|
| Distance | Inverse distance with high weight | Range / Time-to-impact (Excellent match) |
| Speed | Velocity magnitude multiplier | Kinematics & maneuverability |
| Swarm | Dynamic radius based on wave | Group behavior / density |
| Direction | Alignment (dot product) bonus | Heading toward protected asset |
| Multi-Targeting | Bonus for existing interceptors | Resource allocation / focus fire |
This approach mirrors modern Threat Evaluation and Weapon Assignment (TEWA) systems used by leading defense organizations.
Connect with us for a consultation, site survey, or capability demo.
Contact: ewo.info@ewodronedefense.com