Work in progress This site is under construction — content, figures and specifications are provisional.
2 — Features

Six instruments,
four estimators.

Every element is drawn in OpenGL ES 3 from a single per-frame state snapshot — a crisp, low-latency display that stays readable in direct sunlight.

InstrumentsAttitude first.
Fig.01 — The displaylive, ESKF
FlySys PFS showing attitude, tapes and heading band
Artificial horizon
bank & pitch, pitch ladder
Ground-speed tape
GPS (km/h, kt or mph)
Altitude tape
barometric (ft or m)
Vertical speed
dedicated Vertical Speed Indicator (VSI), m/s or fpm (feet per minute)
Heading band
Inertial Measurement Unit (IMU)-fused, magnetic (°M) or true (°T)
Slip & turn
coordinate the turn
RenderingOpenGL ES — optimised.

The display is drawn in OpenGL ES 3 from a single per-frame state snapshot. The pipeline is built for a steady, low-power, allocation-free frame — so the picture stays smooth and the battery lasts.

Fig.05 — Render pipelinerender-on-dirty
DISPLAY STATE per-frame state SUB-RENDERERS horizon, ladder, roll, tapes, vertical speed, heading, slip BATCHED GEOMETRY zero per-frame alloc few draw calls GPU redraw only on change, up to 60 Hz (vsync-bound)
One state snapshot fans out to focused sub-renderers, batched into few allocation-free draw calls sent to the GPU (Graphics Processing Unit). Frames are issued only when the state changes.
01

Render-on-dirty

A frame is drawn only when the state actually changes — selectable 30 Hz battery-saver, or uncapped 60 Hz bounded by vsync. The instrument streams the complete flight picture at 60 Hz (30 Hz selectable); the display renders exactly what arrives.

02

Zero per-frame allocation

No objects are allocated while drawing, so there are no garbage-collection stalls — motion stays smooth.

03

Batched triangles

Geometry is accumulated and submitted in a few large draw calls instead of many small ones.

04

Cached geometry

Shapes whose parameters haven't changed reuse their cached vertices instead of rebuilding.

05

Shared GL resources

A small, fixed set of shader programs and cached text atlases serve every instrument — minimal state changes.

06

Pre-allocated matrices

Attitude matrices live for the lifetime of the renderer, reused every frame.

PrinciplesHow the attitude is computed.

No single sensor gives a trustworthy attitude. The gyroscope is fast but drifts; the accelerometer and magnetometer are stable on average but noisy and disturbed by manoeuvre. Every estimator below resolves the same conflict — trust the gyro over short intervals, correct it toward gravity and magnetic north over long ones — and they differ only in how they strike that balance. Each is presented in its own section below, in the order they appear in Settings.

Error-state Kalman§1 — ESKF, default.
Fig.02 — Error-state Kalman filterESKF (default)
GYRO ω, TEMP T z: accel, mag, GPS, baro 1 — PREDICT propagate state x propagate covariance P 2 — UPDATE Kalman gain K δx from innovation 3 — INJECT & RESET apply error δx reset δx ATTITUDE q, bias, k_T(T)
The gyro propagates a nominal state openly; the Kalman filter tracks only the small error it accumulates, then injects the correction and resets — keeping the error small is what keeps the linearisation valid.

The gyro propagates attitude fast; every other sensor only ever applies a small correction. The accelerometer anchors pitch and roll from gravity — but it is gated off the instant a sustained turn or linear acceleration makes "down" untrustworthy. What keeps the bank true under load is a set of independent EKF (Extended Kalman Filter) measurement updates, each watching a different quantity and weighted by its own confidence. Its state carries not only the quaternion and gyro bias but a per-device temperature coefficient — the IMU's temperature is logged and fed in, so the bias it removes tracks the sensor as it warms. This is the default estimator.

Fig.02b — Measurement updatesmulti-aided
ACCEL — gravity direction anchors pitch & roll, gated in turns MAG — heading tilt-compensated, pure yaw, no tilt leak GPS TURN — bank bank = atan(V·ω / g), coordinated turn LOAD FACTOR — bank bank = acos(1 / N), from g-load, no GPS BARO & GPS-ALT — pitch pitch = asin(VSI / V) + AoA GPS TRACK — yaw heading fallback when no magnetometer EKF UPDATE innovation z − h(x) K weighted by σ δx = K · innovation ATTITUDE q, bias, k_T
Six independent sources, each weighted by its own σ. The two bank updates — GPS coordinated-turn and load factor (acos 1/N) — are what hold a true bank through a turn, where the accelerometer alone can't.
Predict — gyro, with a temperature-tracked bias
b_{\text{eff}}(T) = b_g + k_T\,(T - T_{\text{ref}}) \qquad P \leftarrow F\,P\,F^{\mathsf{T}} + Q
Update — every source forms an innovation, weighted by its own σ
K = P\,H^{\mathsf{T}}\bigl(H P H^{\mathsf{T}} + R\bigr)^{-1} \qquad \delta x = K\,\bigl(z - h(\hat{x})\bigr)
Bank — two independent measurements that survive a turn
\phi = \arctan\!\frac{V\,\omega}{g}\ \ (\text{GPS turn}) \qquad \phi = \arccos\frac{1}{N},\ \ N=\frac{a_z}{g}\ \ (\text{load factor})
Pitch — from the flight-path angle; α₀ is the angle of attack (AoA)
\theta = \arcsin\!\frac{\mathrm{VSI}}{V} + \alpha_0
Inject & reset
x \leftarrow \hat{x} \oplus \delta x \qquad \delta x \leftarrow 0
Complementary§2 — CF, Mahony-MARG.
Fig.03 — Complementary, Mahony-MARGCF
GYRO ω fast, drifts INTEGRATE gyro propagation ACCEL+MAG gravity & north ERROR e measured vs estimated CORRECTION P & I feedback ATTITUDE roll, pitch, yaw

A Mahony-MARG filter (Magnetic, Angular-rate, Gravity) splits the signal by frequency: the gyro supplies the fast motion, accel and mag the slow reference. The correction is the rotation error between measured and estimated gravity/north, fed back through a proportional and an integral gain — and the integral term also learns and cancels gyro bias. Cheap, robust and predictable — the simple, dependable fallback.

Rotation error, then corrected propagation
e \;=\; \hat{v}_g \times v_g \;+\; \hat{v}_m \times v_m
\dot{q} \;=\; \tfrac{1}{2}\, q \otimes \Bigl(\omega + K_p\, e + K_i \!\!\int e\, dt \Bigr)
Madgwick§3 — MG, gradient descent.
Fig.04 — MadgwickMG (gradient)
GYRO ω orientation rate FUSE gyro & gradient ACCEL+MAG gravity & north OBJECTIVE f alignment error GRADIENT STEP descent rate β ATTITUDE roll, pitch, yaw

Frames orientation as an optimisation: the gyro gives the rate, while a single gradient-descent step nudges the estimate toward the orientation that best matches measured gravity and north — at one tunable rate, β. Smooth and responsive for very little compute.

Objective, gradient, and the fused rate
f(q) \;=\; R(q)^{\mathsf{T}}\, \hat{g} \;-\; a \qquad \nabla f = J^{\mathsf{T}} f
\dot{q} \;=\; \tfrac{1}{2}\, q \otimes \omega \;-\; \beta\, \frac{\nabla f}{\lVert \nabla f \rVert}
Firmware quaternion§4 — FW, pass-through.
Fig.05 — Internal, firmware quaternionFW (pass-through)
IMU SENSOR on-board fusion QUATERNION q already fused PASS-THROUGH no phone filter ATTITUDE

When the IMU already publishes its own fused quaternion, this mode passes it straight to the display — a useful reference, and the right choice when you trust the sensor's on-board fusion over the phone's.

Pass-through — no phone-side estimation
q \;=\; q_{\text{IMU}}
Data sourcesFly, sim, or replay.
Joran AHRS & ADCour own instrument (standard) — computes everything on board, the app is a pure display
Internal sensorsphone accel, gyro, mag, baro
BLE 10-DOF IMUgeneric Bluetooth sensor module
X-Plane UDPsimulator feed (in development)
FlySys TCPTCP gateway (in development)
Replayrecorded CSV from Documents/PFD/
Demosynthetic motion (no hardware)
CalibrationThree sensors.
01

Magnetometer

Hard- and soft-iron fit so heading stays honest near metal and avionics.

02

Accelerometer

A guided 6-face procedure recovers per-axis offset and scale, applied before the filters.

03

Gyro offset

A short static "Align" window zeroes gyro bias the moment you power up on the ground.

Full calibration steps →