시각화 및 분석
theaterPlot
을 사용하여 ground truth, 커버리지, 검출, 트랙을 플로팅합니다. trackErrorMetrics
를 사용하여 트랙에 대한 오차 메트릭을 구합니다. trackAssignmentMetrics
, trackOSPAMetric
, trackGOSPAMetric
을 사용하여 다중 객체 추적 시스템의 성능을 분석하고 비교합니다.
함수
시각화
theaterPlot | Plot objects, detections, and tracks in Scenario |
trackingGlobeViewer | Virtual globe for tracking scenario visualization (R2021b 이후) |
addCustomTerrain | Add custom terrain data (R2022a 이후) |
removeCustomTerrain | Remove custom terrain data (R2022a 이후) |
poseplot | 3차원 자세 플롯 (R2021b 이후) |
PosePatch
Properties | Pose plot appearance and behavior (R2021b 이후) |
timescope | Display time-domain signals (R2020a 이후) |
분석
trackAssignmentMetrics | Track establishment, maintenance, and deletion metrics |
trackErrorMetrics | Track error and NEES |
trackOSPAMetric | Optimal subpattern assignment (OSPA) metric |
trackGOSPAMetric | Generalized optimal subpattern assignment (GOSPA) metric (R2020a 이후) |
trackCLEARMetrics | CLEAR multi-object tracking metrics (R2023a 이후) |
allanvar | Allan 분산 |
magcal | Magnetometer calibration coefficients |
블록
분석
Generalized Optimal Subpattern Assignment Metric | Calculate Generalized Optimal Subpattern Assignment Metric (R2021a 이후) |
Optimal Subpattern Assignment Metric | Calculate Optimal Subpattern Assignment Metric (R2021a 이후) |
도움말 항목
- Configure Time Scope MATLAB Object
Customize
timescope
properties and use measurement tools.
추천 예제
Introduction to Tracking Metrics
While designing a multi-object tracking system, it is essential to devise a method to evaluate its performance against the available ground truth. This ground truth is typically available from a simulation environment or by using techniques like ground-truth extraction using manual or automated labeling on recorded data. Though it is possible to qualitatively evaluate a tracking algorithm using visualization tools, the approach is usually not scalable. This example introduces different quantitative analysis tools in Sensor Fusion and Tracking Toolbox™ for assessing a tracker's performance. You will also use some common events like false tracks, track swaps etc. encountered while tracking multiple objects to understand the strengths and limitations of these tools.
Use theaterPlot to Visualize Tracking Scenario
Use the theaterPlot
object to visualize various aspects of a tracking scenario.
Allan 분산을 사용하여 관성 센서 잡음 분석
이 예제에서는 Allan 분산을 사용하여 MEMS 자이로스코프의 잡음 파라미터를 확인하는 방법을 보여줍니다. 이러한 파라미터는 시뮬레이션에서 자이로스코프를 모델링하는 데 사용할 수 있습니다. 자이로스코프 측정은 다음으로 모델링됩니다.
Magnetometer Calibration
Magnetometers detect magnetic field strength along a sensor's X,Y and Z axes. Accurate magnetic field measurements are essential for sensor fusion and the determination of heading and orientation.
MATLAB 명령
다음 MATLAB 명령에 해당하는 링크를 클릭했습니다.
명령을 실행하려면 MATLAB 명령 창에 입력하십시오. 웹 브라우저는 MATLAB 명령을 지원하지 않습니다.
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