Import raw data (CSV, XLSX, TXT, or MATLAB tables) into formats used by Sensor Fusion and Tracking Toolbox. Handles both ground truth trajectories and sensor detection data. For truth: builds trackingScenarioRecording, tuning timetable, truthlog, or converted table. For sensor data: builds task-oriented dataFormat structs (preferred) or objectDetection arrays (legacy).
SkillScore82Maintained
Automatic
◆Coding pack
Last verified 8/16/2026
What it changes
Passed 18 of 22 test cases with the skill, and 7 of 22 without it.
Without skill32%
With skill82%
Checks it passed that the model alone missed
The response sets IsReceiverStationary to false.
The response constructs trackerSensorSpec with custom spec type.
The response converts angle measurements from radians to degrees.
and 15 more in the full report
11 fixed7 passed either way4 still failing0 broke
Pass-rate lift
+50 pp
Tokens
+100%
Agent turns
0%
Response time
-24%
Model gemini-3.6-flashMeasured 8/16/2026v118 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 11
Prompt
I have radar detection files in `scans.mat` where range is stored in kilometers (`range_km`) and azimuth is stored in radians (`azimuth_rad`). I am using task-oriented sensor specs for my target tracker. What unit conversions must be applied when populating the measurement update structures?
What was checked, without and with the skill
Checked
Without skill
With skill
The response converts range measurements from kilometers to meters.
Passed
Passed
The response converts angle measurements from radians to degrees.
Missed
Passed
case-06 · gemini-3.6-flash · 8/16/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md290 lines10 files
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?"
User's data
Route
Recorded positions/trajectories (truth, GPS, flight logs)
Truth pathway
Sensor measurements (radar detections, IR bearings, lidar boxes, camera boxes)
Sensor pathway
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?"