Theory of Operation¶

This chapter explains the underlying theory and operation of the qt-test application and SitePoint GPS positioning system.

RTK GPS Positioning¶

Real-Time Kinematic (RTK) positioning achieves centimeter-level accuracy by using correction data from a reference station.

Standard GPS vs RTK:

Standard GPS: 2-10 meter accuracy
RTK GPS: 1-5 centimeter accuracy

How RTK Works:

Base station (or network) measures GPS errors at known location
Corrections transmitted via NTRIP/RTCM to rover
Rover applies corrections to achieve high accuracy
Correction age must be < 30 seconds for optimal performance

Data Flow Architecture¶

The application implements a multi-layer data flow:

        graph TB

    subgraph corrections ["RTCM Correction Path"]

        NTRIP[NTRIP Caster]

        NM[NTRIPManager]

        BLE1[BLEManager]

        GPS1[GPS Device]



        NTRIP -->|"RTCM v3<br/>(1 Hz)"| NM

        NM -->|"Queue RTCM"| BLE1

        BLE1 -->|"BLE 0x0102"| GPS1

    end



    subgraph data ["GPS Data Path"]

        GPS2[SitePoint GPS Device]

        BLE2[BLEManager]

        DM[DataManager]

        QML[QML UI]



        GPS2 -->|"SQSP/SQTP<br/>(8-10 Hz)"| BLE2

        BLE2 -->|"Parse SQTP"| DM

        DM -->|"Format for UI"| QML

    end



    GPS1 -.->|"Same Device"| GPS2



    style NTRIP fill:#e1f5ff

    style GPS2 fill:#e1f5ff

    style NM fill:#fff4e1

    style BLE1 fill:#fff4e1

    style BLE2 fill:#fff4e1

    style DM fill:#fff4e1

    style GPS1 fill:#e1ffe1

    style QML fill:#e1ffe1

Key Points:

NTRIP corrections queued in RTCM buffer (50 message capacity)
BLE FSM paces transmission to match connection interval
GPS data (8-10 Hz) uses change detection to prevent UI thrashing
DataManager formats raw data for QML display

BLE Communication Model¶

Bluetooth Low Energy communication follows a client-server model:

Device Roles:

Peripheral (Server): SitePoint GPS device
Central (Client): qt-test application

Communication Patterns:

Write Without Response (0x0102): RTCM data to device
Notify (0x0105): GPS data from device
Request/Response (0x0105): Commands and configuration

Connection Parameters:

Connection interval: 7.5-15 ms (negotiated)
MTU: 23-527 bytes (negotiated, typical 244)
Supervision timeout: 6 seconds
Slave latency: 0 (no skipped events)

See BLE Communication with SitePoint GPS Devices for complete BLE implementation details.

SQTP Protocol Details¶

SQTP provides reliable framing over BLE’s unreliable write operations.

Frame Assembly:

Application calls sqtpManagerAddDirect() with subframe ID and data
SQTP library packs header, subframe ID, length, payload
CRC-16 calculated and appended
Frame passed to sqtpBleTransmitFrame() callback
BLEManager writes frame to characteristic 0x0105

Frame Parsing:

BLE notification received on characteristic 0x0105
Data passed to sqtpBleParseSubframe() callback
SQTP library validates CRC, extracts subframe ID
Payload routed based on subframe ID
Parsed data stored in global structures

Correction Data Flow¶

RTCM correction data follows a carefully managed path:

Queue Management:

Pre-allocated pool: 50 × 2048 byte buffers
FIFO queuing with overflow detection
Oldest message dropped if queue full

Transmission FSM:

IDLE: Queue empty, no activity
SENDING: Packing and transmitting fragments
WAITING: Pacing delay between transmissions

Fragmentation:

Messages split into MTU-sized chunks
Fragment count = ceil(messageSize / (MTU - 3))
All fragments must be sent before returning to IDLE

Change Detection Optimization¶

GPS data arrives at 8-10 Hz but may not change every update. Change detection prevents unnecessary UI updates:

// In BLEManager::sqtpBleParseSubframe()
static SqspLla_t previousLla;

if (memcmp(&myLla, &previousLla, sizeof(SqspLla_t)) != 0) {
    emit llaDataChanged();  // Only emit if data changed
    previousLla = myLla;
}

Benefits:

Reduces UI update load by ~80%
Prevents TextField editing interruptions
Maintains responsive UI at high data rates

Device Modes¶

SitePoint devices support multiple operational modes:

Mode	Description
DEVICE_MODE_ROVER	Standard rover, no corrections, ~2m accuracy
DEVICE_MODE_ROVER_NTRIP	Rover with NTRIP corrections via BLE, ~2cm accuracy
DEVICE_MODE_ROVER_NEAREST	Rover with Tripod network corrections, ~2cm accuracy
DEVICE_MODE_FIXED_BASE	Fixed base station, broadcasts corrections
DEVICE_MODE_FIXED_BASE_BROADCAST	Fixed base with wider broadcast range

Mode Selection Considerations:

Use ROVER_NTRIP when you have internet and NTRIP account
Use ROVER_NEAREST for Tripod network coverage
Use FIXED_BASE to provide corrections to other rovers
Survey duration affects base station accuracy (longer = better)

For more details, see Device Configuration.

Real-Time Performance Architecture¶

The application is architected for real-time GPS data processing at 8-10 Hz update rates with minimal latency. This section explains the performance optimizations and design decisions.

The 8-10 Hz Challenge¶

GPS data arrives from the BLE device at 8-10 Hz (every 100-125 ms). At this rate:

120-150 ms budget per update cycle
UI must remain responsive (60 FPS = 16ms per frame)
Data processing must not block UI rendering
Property bindings must not create excessive overhead

Traditional Qt approach would use signals/slots extensively, but this creates overhead:

Operation	Latency	Annual CPU Time (8 Hz)
Qt signal emission	~500ns	126 million signals/year
Qt slot execution	~500ns	126 million calls/year
Property notification	~1000ns	252 million notifications/year

With 15+ properties per update, this would be 1.89 billion signal emissions per year of continuous operation.

Global Extern Variables¶

The application uses global variables for GPS data to eliminate signal routing overhead:

// Declared in BLEManager.cpp
SqspLla_t myLla;
SqspStatus_t myStatus;
SqspLbConfig_t myConfig;

// Accessed via extern in other managers
extern SqspLla_t myLla;
extern SqspStatus_t myStatus;
extern SqspLbConfig_t myConfig;

Data Flow with Extern:

        graph TB

    GPS["GPS Device<br/>(8-10 Hz)"]

    BLE["BLEManager::sqtpBleParseSubframe()"]

    WRITE["myLla.latitude = newValue<br/><i>~5ns, single memory write</i>"]

    CHECK["if (memcmp(&myLla, &previousLla, ...))<br/>emit llaDataChanged()"]

    MAP["MapManager::onLlaDataChanged()"]

    READ["return myLla.lat<br/><i>~5ns, single memory read</i>"]



    GPS -->|"BLE Notify"| BLE

    BLE -->|"Direct write"| WRITE

    WRITE -->|"Change detection"| CHECK

    CHECK -->|"Signal emission<br/>(only if changed)"| MAP

    MAP -->|"Direct read"| READ



    style GPS fill:#e1f5ff

    style BLE fill:#fff4e1

    style WRITE fill:#ffe1e1

    style CHECK fill:#ffe1f5

    style MAP fill:#fff4e1

    style READ fill:#e1ffe1

Performance Benefits:

No function call overhead: Direct memory access
No copy operations: Managers read from shared memory
Single source of truth: All managers see consistent data
Cache-friendly: Data co-located in memory

When Extern Is Used:

MapManager: Always uses extern for maximum speed
DataManager: Uses extern with formatting and caching
BLEManager: Owns the globals, provides getter functions for encapsulation

Batched Properties Pattern¶

DataManager uses batched properties to reduce Qt signal overhead:

Traditional Approach (high overhead):

// 15 separate properties = 15 signals per update
Q_PROPERTY(QString latitude ...)
Q_PROPERTY(QString longitude ...)
Q_PROPERTY(QString height ...)
Q_PROPERTY(QString hAcc ...)
// ... 11 more properties ...

void onUpdate() {
    emit latitudeChanged();    // 500ns
    emit longitudeChanged();   // 500ns
    emit heightChanged();      // 500ns
    // ... 12 more emissions = ~7500ns overhead
}

Batched Approach (low overhead):

// Single property with map = 1 signal per update
Q_PROPERTY(QVariantMap llaData ...)

void onUpdate() {
    // Process all fields once
    m_llaData["latitude"] = format(myLla.lat);
    m_llaData["longitude"] = format(myLla.lon);
    // ... all fields ...

    emit dataChanged();  // Single 500ns emission
}

Performance Comparison at 8 Hz:

Approach	Signals/Update	Signals/Second	Annual Emissions
Traditional (15 props)	15	120	3.78 billion
Batched (1 prop)	1	8	252 million
Reduction	93%	93%	93%

Minimalist MapManager¶

MapManager is the ultimate performance optimization - zero processing:

// No member variables (except counter)
// No caching
// No formatting
// Just direct passthrough

double MapManager::lat() const {
    return myLla.lat;  // Single memory read, ~5ns
}

void MapManager::onLlaDataChanged() {
    m_centerUpdateCount++;     // Increment counter
    emit locationChanged();    // Signal QML to re-read
}

Why This Works:

The QML Map component re-reads coordinates at 60 FPS (every 16ms) for rendering, but GPS only updates at 8-10 Hz (every 100-125ms). The property binding is evaluated 60 times/second, but:

Each coordinate read: ~5ns (direct memory access)
Per frame overhead: 2 coordinates × 5ns = 10ns per frame
Map rendering time: ~16ms per frame = 16,000,000ns per frame
Overhead percentage: 10ns / 16,000,000ns = 0.00006% of frame time

Change Detection Optimization¶

GPS data arrives at 8-10 Hz, but values may not change every update. Change detection prevents unnecessary signal emissions:

void BLEManager::sqtpBleParseSubframe(...) {
    static SqspLla_t previousLla;

    // Parse new data into myLla global
    myLla.latitude = newLatitude;
    // ... update all fields ...

    // Only emit signal if data actually changed
    if (memcmp(&myLla, &previousLla, sizeof(SqspLla_t)) != 0) {
        emit llaDataChanged();  // Typical: 1-2 Hz (not 8-10 Hz)
        previousLla = myLla;
    }
}

Measured Impact:

GPS updates: 8-10 Hz (800-1000 ms intervals)
Actual changes: 1-2 Hz (position changes every 500-1000ms when stationary)
Signal reduction: 80-90% when device is stationary
Prevents: TextField editing interruptions in QML UI

C++ vs QML Split Rationale¶

Why C++ for Data Processing:

Performance: Native code vs JavaScript VM
Type safety: Compile-time checking vs runtime errors
BLE/Network APIs: Qt Bluetooth and QTcpSocket are C++ APIs
Memory control: Direct memory access, no GC pauses

Why QML for UI:

Declarative: UI structure obvious from code
Animations: Built-in animation framework
Responsive: Automatic property binding
Rapid iteration: No recompilation for UI tweaks

Data Bridge:

        graph TB

    subgraph cpp ["C++ Layer (Real-Time)"]

        GPS["GPS Device<br/>(8-10 Hz)"]

        BLE["BLEManager"]

        EXT["extern myLla"]

        MAPM["MapManager"]

        SIG["emit locationChanged()"]



        GPS --> BLE

        BLE --> EXT

        EXT --> MAPM

        MAPM --> SIG

    end



    subgraph qml ["QML Layer (UI)"]

        MAP["Map Display<br/>(60 FPS)"]

        PROP["Property binding"]

        READ["MapManager.lat"]

        RENDER["UI renders"]



        RENDER --> PROP

        PROP --> READ

        READ --> MAP

    end



    SIG -.->|"Signal triggers<br/>QML re-read"| RENDER



    style GPS fill:#e1f5ff

    style BLE fill:#fff4e1

    style EXT fill:#ffe1e1

    style MAPM fill:#fff4e1

    style SIG fill:#ffe1f5

    style MAP fill:#e1ffe1

    style READ fill:#e1ffe1

    style PROP fill:#e1ffe1

    style RENDER fill:#e1ffe1

Performance Result:

GPS processing: < 1μs per update (C++)
UI rendering: ~16ms per frame (QML, GPU accelerated)
Decoupled: GPS processing never blocks UI
Smooth: 60 FPS maintained even at 10 Hz GPS updates

Benchmark Summary¶

Measured performance on typical desktop hardware (Intel Core i5):

Operation	Latency	Throughput
BLE receive + parse	~50μs	20,000 updates/sec
SQTP frame parsing	~20μs	50,000 frames/sec
Extern data access	~5ns	200 million reads/sec
Batched property update	~10μs	100,000 updates/sec
Qt signal emission	~500ns	2 million signals/sec
QML Map rendering	~16ms	60 FPS

Bottleneck Analysis:

GPS device: 100-125ms between updates (8-10 Hz) - System limit
BLE communication: ~50μs processing - 0.05% of budget
Data access: ~5ns - 0.000005% of budget
Signal routing: ~500ns - 0.0005% of budget
Map rendering: ~16ms per frame - UI thread, not data path

Conclusion: Application performance is entirely GPS-limited. All software optimizations ensure we never become the bottleneck.

Theory of Operation¶

RTK GPS Positioning¶

Data Flow Architecture¶

BLE Communication Model¶

SQTP Protocol Details¶

Correction Data Flow¶

Change Detection Optimization¶

Device Modes¶

Real-Time Performance Architecture¶

The 8-10 Hz Challenge¶

Global Extern Variables¶

Batched Properties Pattern¶

Minimalist MapManager¶

Change Detection Optimization¶

C++ vs QML Split Rationale¶

Benchmark Summary¶

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