Matlab Import Tracking Data

Tracking Data Import

Import raw data into MATLAB for use with Sensor Fusion and Tracking Toolbox. Handles ground truth trajectories and sensor detection data. Writes plain MATLAB code.

When to Use

• User has recorded trajectory/position data and wants to replay, tune filters, or evaluate trackers

• User has sensor measurements (radar, IR, lidar, camera, sonar) and wants to feed them to a tracker

• User mentions flight logs, GPS logs, ADS-B, AIS, radar recordings, lidar point clouds, camera detections

• User asks about trackingDataImporter, objectDetection, trackerSensorSpec, dataFormat, or importing data for trackers

When NOT to Use

• User is generating synthetic scenarios from scratch (use trackingScenario)

• User already has data in the correct SFTT format

• User needs to design a tracker or write tracking algorithms (use the multi-object-tracking skill)

• User is working with raw signal processing (waveform design, range-Doppler maps)

Routing: What Kind of Data?

Step 1: Determine data type

Ask: "What kind of data are you importing?"

Inference signals from column inspection:

• Truth-like: continuous position per object ID over time, no noise/accuracy columns

• Sensor-like: measurement quantities (range, azimuth, RCS), accuracy columns, multiple detections per timestep without guaranteed ID continuity

Step 2 (sensor data only): Identify sensor type and target application

Important: If Step 1 determined the data is truth/trajectory (positions per platform over time), stay in the Truth Pathway. Do NOT enter this step just because the user mentions IMM, UKF, or filter tuning — those refer to what the tuner will produce, not how to format the input data. Truth data → timetable. Sensor data → objectDetection or dataFormat struct.

Ask: "What sensor produced this data?" and "What are you tracking?"

Then decide the API internally (do NOT ask the user about APIs):

Use task-oriented path (preferred) when:

• A prebuilt trackerSensorSpec matches, OR

• Measurements fit a sensorMeasurementModel (any combo of az/el/range/rr, position, position-velocity)

• AND user does not need TOMHT/PHD/GridRFS tracker or non-EKF filters

Use legacy objectDetection path when:

• User needs TOMHT, PHD, or GridRFS tracker (task-oriented only supports GNN/JIPDA)

• User needs UKF, CKF, IMM, particle filter (task-oriented uses EKF internally)

• Measurements don't fit any sensorMeasurementModel (TDOA, custom geometry)

• User explicitly requests objectDetection (for trackingFilterTuner or existing code)

Truth Pathway

Truth/trajectory data (positions, velocities per platform over time) always produces timetables or struct arrays — never objectDetection. This applies even when the user mentions filter tuning, IMM, UKF, or other filter types. For tuning, the truth pathway produces timetables with Time (duration) and Position, Velocity columns (or a single State vector). The tuner's detection input must come from separate sensor measurement data — do NOT fabricate objectDetection from truth positions.

Step 1: Ask the User (2 questions only)

1. What output do you need? (recording / tuning data / truth log / converted table)

2. Where is the data? (file path or workspace variable name)

Step 2: Inspect the Data

Read the user's actual file — never generate synthetic data when the user provides a file path. Use readtable or equivalent to load the file, then display columns + sample rows. Infer the data model — do not ask yet:

• Geo vs Cartesian, category, time column & format, platform/class ID columns

• Position, velocity, orientation, dimension columns

• Units (default degrees/meters/m-per-s; adjust if names hint otherwise)

See references/interpreter-categories.md for category selection and column name patterns.

Step 3: Propose Mapping — Let User Confirm/Edit

Always present a data summary before writing any conversion code, even when the mapping is obvious. Include ALL of:

• Column names found in the data

• Detected units (from column name hints or defaults)

• Number of platforms/objects

• Time span (first/last timestamp, total duration)

• Proposed column-to-field mapping table (show unmapped columns)

Present inferred mappings as a table. Iterate until confirmed.

Step 4: Ask About Output Frame (geo data only)

Options: Cartesian ECEF, Cartesian Fixed NED/ENU (needs origin), Geodetic Local NED/ENU. Default: same as input. See references/coordinate-transforms.md.

Step 5: Generate and Run Code

Read references/output-formats.md before generating code — it defines required fields and defaults for missing states. Follow patterns in references/code-patterns.md. Key steps:

1. Read data → extract columns → convert units → parse time

2. Remap platform IDs to sequential integers

3. Transform coordinates if needed

4. Build output structure (see references/output-formats.md)

5. Sort by time before building output

Step 6: Visualize

See references/visualization.md. Geo → trackingGlobeViewer; Non-geo → theaterPlot.

Stop here — do NOT run downstream tools (trackers, trackOSPAMetric, trackingFilterTuner, etc.). The user's data is now in the correct format. Tell the user what they have and show the calling convention for their intended use case.

Sensor Pathway: Task-Oriented (Preferred)

Use when measurements fit a prebuilt or custom trackerSensorSpec. The key insight: dataFormat is dynamic — it changes based on sensor spec properties. Never hardcode the struct; always query it.

Step 1: Select sensor spec

Step 2a: Configure spec properties from the data

Inspect the user's data and set properties that affect dataFormat:

Aerospace monostatic / ESM / IR:

• HasElevation — does data have elevation measurements?

• HasRangeRate — does data have range-rate / Doppler? (radar only)

• IsPlatformStationary — is sensor position fixed or moving? (false adds PlatformPosition/Orientation/Velocity per look)

• MaxNumLooksPerUpdate — max scan dwells per update in the data

• MaxNumMeasurementsPerUpdate — max detections per update in the data

Aerospace bistatic:

• HasElevation, HasRangeRate — as above

• MeasurementMode — "range-angle" or "range-only"

• IsReceiverStationary, IsEmitterStationary — adds platform fields when false

Automotive radar:

• HasElevation, MaxNumMeasurements

• ReferenceFrame — 'ego' (measurements in body frame) or 'global' (ego pose in global frame available)

Automotive camera / lidar:

• MaxNumMeasurements

• ReferenceFrame — 'ego' or 'global'

Step 2b: Custom sensor spec (when no prebuilt fits)

For sensors with standard measurement types but no prebuilt spec (e.g., marine radar, sonar):

sensorSpec = trackerSensorSpec('custom');
sensorSpec.MeasurementModel = sensorMeasurementModel('<modelName>');
sensorSpec.DetectabilityModel = sensorDetectabilityModel('<modelName>');
sensorSpec.ClutterModel = sensorClutterModel('<modelName>');
sensorSpec.BirthModel = sensorBirthModel('<modelName>');

See references/sensor-data-formats.md for the complete model catalogs and property details.

For moving sensors, set UpdateModels = true — this adds Time, TimeVaryingModelData, and MeasurementVaryingModelData to the dataFormat.

Step 3: Inspect data — infer units, time, and reference frame

Read the user's actual file — never generate synthetic data when the user provides a file path. Determine:

1. Time column & format — detect using the same heuristics as truth pathway (see references/time-and-units.md). Convert to elapsed seconds from first timestamp.

2. Measurement units — infer from column name suffixes (_deg, _rad, _km, _kts, etc.) or ask. Target units for the dataFormat:

   • Angles: degrees

   • Ranges: meters

   • Range-rate: m/s

   • Position: meters

   • Velocity: m/s

3. Reference frame — if measurements are NOT in the sensor's native frame, plan a transform:

   • Sensor-native = the frame the sensor naturally reports in (spherical for radar/ESM/IR, body-relative for automotive, image pixels for camera)

   • If user's data is in a world frame (e.g., Cartesian NED positions from a fused tracker) but the sensor spec expects spherical measurements, convert back to sensor-native using sensor pose

   • If user's data is in a different body frame convention (e.g., NED vs ENU), rotate accordingly

   • If already in sensor-native frame (the common case), no transform needed

Step 4: Query dataFormat and propose mapping

fmt = dataFormat(sensorSpec);
disp(fmt)

Present a mapping table for user confirmation:

Your column          →  dataFormat field        Action
"azimuth_deg"        →  LookAzimuth (1×N)      direct (deg→deg)
"range_km"           →  Range (M×N)            convert km→m (×1000)
"doppler_mps"        →  RangeRate (M×N)        direct
"timestamp_epoch"    →  MeasurementTime (1×N)  parse epoch→elapsed sec
"elev_rad"           →  LookElevation (1×N)    convert rad→deg
[unmapped: "snr"]    →  (not used)

Include unit conversions and frame transforms in the "Action" column. Show unmapped columns. Iterate until user confirms.

Step 5: Configure sensor performance properties

Set from user input or use defaults: MountingLocation, MountingAngles, FieldOfView, RangeLimits, DetectionProbability, FalseAlarmRate/NumFalsePositivesPerScan.

Step 6: Generate code

Write code that populates the dataFormat struct per timestep in a loop. The output is an array of structs (one per update) ready to be passed to a tracker. Stop here — do NOT create or run a tracker. Tell the user their data is ready and show them the calling convention: tracker = multiSensorTargetTracker(targetSpec, sensorSpec, algorithm); tracks = tracker(sensorData(iUpdate)).

Rotation matrices from Euler angles: When data has yaw/pitch/roll and you need a 3×3 rotation matrix (e.g., for PlatformOrientation), build it from a quaternion:

R = rotmat(quaternion([yaw pitch roll], 'eulerd', 'ZYX', 'frame'), 'frame');

Preprocessing flags

Sensor Pathway: Legacy objectDetection

Use when task-oriented API doesn't fit (TOMHT/PHD tracker, non-EKF filter, custom measurements, or explicit user need).

Standard sub-path (built-in measurement models)

For measurements that fit cvmeas/cameas/ctmeas — any combo of az/el/range/rr in spherical, or position/velocity in rectangular.

Workflow:

1. Identify measurement elements from data columns

2. Infer units, time, and reference frame (same as task-oriented Step 3):

   • Parse time column → elapsed seconds (see references/time-and-units.md)

   • Detect units from column names → convert angles to degrees, ranges to meters, velocities to m/s

   • If measurements are in a world frame but need to be in sensor-native frame (spherical or rectangular relative to sensor), transform using sensor pose

3. Set Has* flags: HasAzimuth, HasElevation, HasRange, HasVelocity

4. Determine Frame: 'spherical' or 'rectangular'

5. Get sensor pose per timestep (position, velocity, orientation) — or fixed values if stationary

6. Propose mapping table — present columns → objectDetection fields with unit/frame actions. Iterate until user confirms.

7. Build standard MeasurementParameters struct (see references/objectDetection-patterns.md)

8. Construct measurement vector in correct order:

   • Spherical: [az, el, range, rr] with missing elements removed

   • Rectangular: [x, y, z, vx, vy, vz] with missing elements removed

9. Set MeasurementNoise from accuracy columns or user-specified values

10. Generate objectDetection cell array

11. Filter tuning data (when user has sensor measurement data for trackingFilterTuner): The tuner requires detection-to-target association. If the sensor data contains a target ID column (e.g., TargetID, PlatformID, ObjectID):

    • Separate detections per target. Produce a cell array of detection logs (one per platform).

    • The tuner also needs truth timetables — but those come from a separate truth data source via the Truth Pathway. Do NOT fabricate truth from sensor measurements or vice versa.

    • If no target ID column: inform the user that trackingFilterTuner requires detection-to-target association and ask how they want to proceed (provide IDs, or use single-target subset)

12. Stop here — do NOT create or run a tracker. Tell user: "These detections work with built-in filter inits: initcvekf, initcaukf, initctekf, initcvukf, etc."

Custom sub-path (non-standard measurements)

For measurements that don't fit built-in models (TDOA, custom geometry, etc.):

1. Identify non-standard nature of measurements

2. Infer units, time, and reference frame — parse time, convert units, transform to sensor-native frame if needed (same rules as above)

3. Ask: what is your state vector? what motion model?

4. Design custom MeasurementParameters — must carry info needed by measurement function and include a discriminator field if multi-sensor

5. Write custom measurementFcn(state, mp) mapping state → expected measurement

6. Write custom filterInitFcn(detection) using the inverse measurement model

7. Generate objectDetection array with custom MeasurementParameters

8. Deliver measurement function + filter init as a matched pair

9. Stop here — do NOT create or run a tracker. Tell the user their detections are ready.

See references/objectDetection-patterns.md for the standard struct, measurement ordering, and multi-sensor design patterns. See references/custom-measurement-models.md for custom function templates.

Reference Documents

Read on-demand when you need details:

• references/output-formats.md — Truth output struct/table schemas (recording, tuning, truthlog)

• references/code-patterns.md — End-to-end truth import code examples

• references/coordinate-transforms.md — Frame transforms (LLA→ECEF, NED→ECEF, etc.)

• references/visualization.md — trackingGlobeViewer and theaterPlot usage

• references/interpreter-categories.md — Truth data categories, state elements, column name patterns

• references/time-and-units.md — Time parsing and unit conversion

• references/sensor-data-formats.md — Task-oriented: all prebuilt/custom sensor spec properties, model catalogs, dataFormat behavior

• references/objectDetection-patterns.md — Legacy: MeasurementParameters struct, measurement vector ordering, multi-sensor patterns

• references/custom-measurement-models.md — Custom measurementFcn + filterInitFcn templates

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