Sensor fusion basics in EV Technology - Time & Space Complexity
When combining data from multiple sensors, it is important to understand how the processing time changes as more sensor data is added.
We want to know how the time to fuse sensor data grows as the number of sensors or data points increases.
Analyze the time complexity of the following sensor fusion process.
// Assume sensorsData is a list of sensor readings
fusedData = []
for sensorReading in sensorsData:
processed = process(sensorReading)
fusedData.append(processed)
finalResult = combine(fusedData)
This code processes each sensor reading one by one, then combines all processed data into a final result.
Identify the loops, recursion, array traversals that repeat.
- Primary operation: Loop over each sensor reading to process it.
- How many times: Once for each sensor reading in the input list.
As the number of sensor readings increases, the processing time grows roughly in direct proportion.
| Input Size (n) | Approx. Operations |
|---|---|
| 10 | About 10 processing steps plus one combine step |
| 100 | About 100 processing steps plus one combine step |
| 1000 | About 1000 processing steps plus one combine step |
Pattern observation: The total work grows roughly in a straight line as input size increases.
Time Complexity: O(n)
This means the time to fuse sensor data grows directly with the number of sensor readings.
[X] Wrong: "Processing multiple sensors always takes the same time no matter how many sensors there are."
[OK] Correct: Each sensor reading needs to be processed, so more sensors mean more work and more time.
Understanding how sensor fusion time grows helps you explain system performance clearly and shows you can reason about real-world data processing.
"What if the combine step also processed each fused data item individually? How would the time complexity change?"