How does sensor fusion improve target tracking when multiple sensors provide conflicting measurements?

How does sensor fusion improve target tracking when multiple sensors provide conflicting measurements?

• A. It combines data from multiple sensors to produce accurate, continuous target tracks.
• B. It disables conflicting sensors automatically.
• C. It ignores measurements from all sensors.
• D. It reduces the need for firing control.

Answer: (A)

Sensor fusion combines measurements from multiple sensors to estimate the target’s position and motion, giving a single, more accurate and continuous track. When measurements conflict, the fusion process weights each sensor’s data by how reliable it is (and accounts for expected biases and noise), uses a motion model to predict where the target should be, and updates the estimate accordingly. This reconciles discrepancies, reduces the impact of noisy or faulty sensors, and maintains a smooth trajectory even if one sensor provides conflicting information. The other options imply discarding data, ignoring measurements, or focusing on firing control rather than improving the target estimate, which is not how fusion improves tracking.

Sensor fusion combines measurements from multiple sensors to estimate the target’s position and motion, giving a single, more accurate and continuous track. When measurements conflict, the fusion process weights each sensor’s data by how reliable it is (and accounts for expected biases and noise), uses a motion model to predict where the target should be, and updates the estimate accordingly. This reconciles discrepancies, reduces the impact of noisy or faulty sensors, and maintains a smooth trajectory even if one sensor provides conflicting information. The other options imply discarding data, ignoring measurements, or focusing on firing control rather than improving the target estimate, which is not how fusion improves tracking.