aegis-filter

High-integrity, deterministic, and #![no_std] capable state estimation and robust filtering engine written in pure Rust.

Designed for mission-critical robotics, aerospace, navigation, autonomous sensor fusion, and embedded telemetry where numerical stability, fail-safe error handling, and adversarial/outlier resilience are paramount.

Developed under Exploidus.

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Architectural & Security Highlights

• Joseph Stabilized Covariance Updates: Standard Kalman Filter updates ($\mathbf{P} = (\mathbf{I} - \mathbf{KH})\mathbf{P}$) frequently become asymmetric and lose positive semi-definiteness under finite floating-point round-off. aegis-filter implements the Joseph Form covariance update: $$\mathbf{P}_k = (\mathbf{I} - \mathbf{K}_k \mathbf{H}) \mathbf{P}_k^- (\mathbf{I} - \mathbf{K}_k \mathbf{H})^T + \mathbf{K}_k \mathbf{R} \mathbf{K}_k^T$$ which unconditionally guarantees symmetry and positive semi-definiteness.
• Strict Non-Finite Float Guards: Hardened against NaN, +Inf, and -Inf on all state vectors, inputs, and measurements. All methods return Result<T, KalmanError> with zero panics.
• Mahalanobis Distance / Chi-Square Outlier Gating: Built-in residual gating to discard wild sensor glitches, adversarial spoofed signals, or hardware spikes without contaminating the filter state.
• Pure #![no_std] & Zero Heap Allocations: Const generic dimensions (KalmanFilter<STATES, MEASUREMENTS>) allow running directly on bare-metal microcontrollers (ARM Cortex-M, RISC-V) without alloc or OS dependencies.

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Filter & Telemetry Architectures

1. ScalarKalmanFilter: Ultra-lightweight 1D scalar filter for temperature, pressure altitude, or single-variable telemetry tracking.
2. LinearKalmanFilter<STATES, MEASUREMENTS>: Fully generic multi-dimensional linear Kalman filter configured via compile-time const generics.
3. MultiSensorFusion1D<CAPACITY>: Multi-sensor redundant fusion engine with dynamic sensor health scoring, automated Fault Detection, Isolation, and Recovery (FDIR), and consecutive-health auto-recovery.
4. UnscentedKalmanFilter2D: Non-linear estimation via Merwe scaled sigma points without numerical Jacobian derivations.
5. HInfinityFilter1D: Minimax bounded worst-case disturbance filter for unmodeled dynamics.
6. AdaptiveFilter1D: Online covariance estimation adapting to non-stationary sensor noise.
7. WindowedStats<CAPACITY>: Zero-allocation sliding-window statistical analyzer computing rolling mean, variance, standard deviation, and peak-to-peak spread over fixed const-generic buffers.

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Installation

Add to your Cargo.toml:

[dependencies]
aegis-filter = "0.3.0"

For #![no_std] environments (bare-metal):

[dependencies]
aegis-filter = { version = "0.3.0", default-features = false }

With optional serde support for state persistence and telemetry serialization:

[dependencies]
aegis-filter = { version = "0.3.0", features = ["serde"] }

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Example Usage

1D Sensor Fusion with Adversarial Outlier Gating

use aegis_filter::{ScalarKalmanFilter, FilterError};

fn main() -> Result<(), FilterError> {
    // Initial state: 20.0, Initial variance: 5.0, Process noise (Q): 0.01, Measurement noise (R): 0.5
    let mut filter = ScalarKalmanFilter::new(20.0, 5.0, 0.01, 0.5)?;

    let sensor_stream = [20.2, 20.4, 20.1, 999.0 /* spoofed spike */, 20.3];

    for &reading in &sensor_stream {
        filter.predict(0.0)?;

        // Update with 3-sigma outlier rejection threshold
        match filter.update(reading, Some(3.0)) {
            Ok(estimated_state) => {
                println!("Valid state estimate: {:.3}", estimated_state);
            }
            Err(FilterError::MeasurementGated) => {
                println!("Sensor glitch detected and safely rejected: {}", reading);
            }
            Err(err) => return Err(err),
        }
    }

    Ok(())
}

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Interactive Simulations & Benchmarks

The repository includes runnable examples showcasing real-world aerospace and robotics applications:

# 1. Flight Telemetry & Spoofing Resilience (3-sigma gating under GPS/baro spikes)
cargo run --example flight_telemetry

# 2. Non-linear Radar Tracking via UKF (aircraft range tracking without Jacobians)
cargo run --example radar_tracking_ukf

# 3. Execution Latency & Nanosecond Benchmark (1,000,000 iterations profile)
cargo run --example benchmark

# 4. Multi-Sensor Fusion & Automated FDIR (GPS spoof quarantine & Baro/LiDAR handover)
cargo run --example sensor_fault_fdir

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License

Dual-licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE)
• MIT license (LICENSE-MIT)