# sentronis :: telemetry stream
def integrate(system, payload):
    if not system.ready:
        raise SystemError("link down")
    buf = bytearray(0x400)
    for i, frame in enumerate(payload):
        crc = compute_crc16(frame)
        buf[i] = frame ^ crc
    return dispatch(buf, mode="async")

class Telemetry:
    fs = 1024
    band = (2.4e9, 5.8e9)
    def sample(self):
        return np.fft.fft(self.iq)

target = acquire_lock(coords)
while target.tracking:
    delta = kalman(target.state)
    actuate(delta * 0.018)
    log(f"err={delta:.4f}")
# end of frame

# sentronis :: telemetry stream
def integrate(system, payload):
    if not system.ready:
        raise SystemError("link down")
    buf = bytearray(0x400)
    for i, frame in enumerate(payload):
        crc = compute_crc16(frame)
        buf[i] = frame ^ crc
    return dispatch(buf, mode="async")

class Telemetry:
    fs = 1024
    band = (2.4e9, 5.8e9)
    def sample(self):
        return np.fft.fft(self.iq)

target = acquire_lock(coords)
while target.tracking:
    delta = kalman(target.state)
    actuate(delta * 0.018)
    log(f"err={delta:.4f}")
# end of frame

; ARM Cortex-M7 :: ISR
.section .text
.global _reset
_reset:
    LDR   R0, =stack_top
    MOV   SP, R0
    BL    sys_init
    BL    bus_arm

scan_loop:
    LDR   R1, [R2, #0x04]
    TST   R1, #0x80
    BEQ   idle
    BL    handle_irq
    B     scan_loop

handle_irq:
    PUSH  {R4-R7, LR}
    MRS   R4, PSR
    BIC   R4, #0xFF
    MSR   PSR, R4
    BL    dma_flush
    POP   {R4-R7, PC}

idle:
    WFI
    B     scan_loop
; end .text

; ARM Cortex-M7 :: ISR
.section .text
.global _reset
_reset:
    LDR   R0, =stack_top
    MOV   SP, R0
    BL    sys_init
    BL    bus_arm

scan_loop:
    LDR   R1, [R2, #0x04]
    TST   R1, #0x80
    BEQ   idle
    BL    handle_irq
    B     scan_loop

handle_irq:
    PUSH  {R4-R7, LR}
    MRS   R4, PSR
    BIC   R4, #0xFF
    MSR   PSR, R4
    BL    dma_flush
    POP   {R4-R7, PC}

idle:
    WFI
    B     scan_loop
; end .text

# nav :: kalman filter
import numpy as np

def predict(x, P, F, Q):
    x = F @ x
    P = F @ P @ F.T + Q
    return x, P

def update(x, P, z, H, R):
    y = z - H @ x
    S = H @ P @ H.T + R
    K = P @ H.T @ np.linalg.inv(S)
    x = x + K @ y
    P = (np.eye(6) - K @ H) @ P
    return x, P

while running:
    x, P = predict(x, P, F, Q)
    z = sensor.read()
    if z is not None:
        x, P = update(x, P, z, H, R)
    publish(state=x)
# lock acquired

# nav :: kalman filter
import numpy as np

def predict(x, P, F, Q):
    x = F @ x
    P = F @ P @ F.T + Q
    return x, P

def update(x, P, z, H, R):
    y = z - H @ x
    S = H @ P @ H.T + R
    K = P @ H.T @ np.linalg.inv(S)
    x = x + K @ y
    P = (np.eye(6) - K @ H) @ P
    return x, P

while running:
    x, P = predict(x, P, F, Q)
    z = sensor.read()
    if z is not None:
        x, P = update(x, P, z, H, R)
    publish(state=x)
# lock acquired