When multiple sensors disagree on a target track, what approach is recommended?

When multiple sensors disagree on a target track, what approach is recommended?

• A. Engage the track with the strongest single sensor reading.
• B. Ignore all sensor data and wait for human input.
• C. Discard all tracks and re-scan.
• D. Rely on sensor fusion to combine data and assess track credibility.

Answer: (D)

When multiple sensors disagree on a target track, rely on sensor fusion to combine data and assess track credibility. Combining inputs from all sensors allows you to weigh each measurement by its known reliability and uncertainty, producing a single, more accurate estimate of the target’s state. Fusion algorithms, often using probabilistic methods like Kalman filtering or Bayesian fusion, also generate a credibility or confidence measure for the track. This helps identify when a track is real versus a potential false alarm or measurement error, and it guides decisions such as whether to continue tracking, request additional scans, or corroborate with other sensors. Engaging only the strongest reading can be misled if sensor biases or clutter affect that sensor; ignoring data or discarding tracks wastes information and hinders timely response. Using fused data with credibility assessment provides robustness and continuity in tracking despite disagreements.

When multiple sensors disagree on a target track, rely on sensor fusion to combine data and assess track credibility. Combining inputs from all sensors allows you to weigh each measurement by its known reliability and uncertainty, producing a single, more accurate estimate of the target’s state. Fusion algorithms, often using probabilistic methods like Kalman filtering or Bayesian fusion, also generate a credibility or confidence measure for the track. This helps identify when a track is real versus a potential false alarm or measurement error, and it guides decisions such as whether to continue tracking, request additional scans, or corroborate with other sensors. Engaging only the strongest reading can be misled if sensor biases or clutter affect that sensor; ignoring data or discarding tracks wastes information and hinders timely response. Using fused data with credibility assessment provides robustness and continuity in tracking despite disagreements.