keplerian-core/dist/physics/orbit-determination.js

High-performance TypeScript library for orbital mechanics calculations, providing numerical integration, state propagation, and perturbation modeling for Keplerian orbits.

/**
 * Implements a simplified 2D orbit determination algorithm (e.g., using Gauss's method principles).
 * This is a placeholder for a more robust implementation.
 * For 2D, we can simplify by assuming a circular orbit or using a minimal set of observations.
 *
 * @param r1 - First position vector (2D).
 * @param r2 - Second position vector (2D).
 * @param r3 - Third position vector (2D).
 * @param dt1 - Time difference between r1 and r2.
 * @param dt2 - Time difference between r2 and r3.
 * @returns An estimated OrbitalState (position and velocity) at the time of r2.
 */
export function determineOrbitFromPositions2D(r1, r2, r3, dt1, dt2) {
    // This is a highly simplified placeholder. Real orbit determination is complex.
    // For 2D, we can approximate velocity from position changes.
    // Estimate velocity at r2 using finite differences
    const v1 = {
        x: (r2.x - r1.x) / dt1,
        y: (r2.y - r1.y) / dt1,
    };
    const v2 = {
        x: (r3.x - r2.x) / dt2,
        y: (r3.y - r2.y) / dt2,
    };
    // Average the velocities to get a better estimate at r2
    const velocity = {
        x: (v1.x + v2.x) / 2,
        y: (v1.y + v2.y) / 2,
    };
    return {
        position: r2,
        velocity: velocity,
    };
}
/**
 * Placeholder for a more advanced orbit determination method, e.g., a simple Kalman filter.
 * This function would take a series of observations and refine the orbital state over time.
 * @param observations - An array of observed position vectors with timestamps.
 * @returns A refined OrbitalState.
 */
export function kalmanFilterOrbitDetermination2D(observations) {
    // This is a conceptual placeholder. A real Kalman filter requires:
    // 1. A state transition model (from physics/integrators)
    // 2. A measurement model
    // 3. Covariance matrices for process and measurement noise
    // 4. Iterative prediction and update steps
    if (observations.length === 0) {
        throw new Error('No observations provided for Kalman filter.');
    }
    // For a very basic placeholder, just return the last observation's position
    // and a zero velocity, or extrapolate from the last two if available.
    const lastObservation = observations[observations.length - 1];
    let estimatedVelocity = { x: 0, y: 0 };
    if (observations.length >= 2) {
        const secondLastObservation = observations[observations.length - 2];
        const dt = lastObservation.timestamp - secondLastObservation.timestamp;
        if (dt > 0) {
            estimatedVelocity = {
                x: (lastObservation.position.x - secondLastObservation.position.x) / dt,
                y: (lastObservation.position.y - secondLastObservation.position.y) / dt,
            };
        }
    }
    return {
        position: lastObservation.position,
        velocity: estimatedVelocity,
    };
}