Many system developers prefer to use fully integrated "smart sensors" because these devices often provide a digital output that is convenient for users who occasionally use to avoid the risk of tedious analog circuit design challenges. While the motivation to avoid analog circuit design problems can be understood, it is critical for system integrators to understand the important analog characteristics of smart sensors—such as bandwidth and noise—because these factors can affect significant system-level decisions such as data sampling and processing rates. .
Taking the autonomous vehicle (AV) platform as an example, the platform uses the Gyroscope of the ADIS16460 as a feedback detection component in its navigation control system (GNC). If the developer fails to consider the 330Hz bandwidth of these gyroscopes, then it is possible to set the sampling rate in the angular rate feedback loop based entirely on the motion configuration of the AV platform.
For example, assuming that GNC engineers believe that the motion configuration of the AV platform can be limited to 4 Hz spectral components, data acquisition at 40 SPS rates appears to be a conservative approach to the sampling rate of the GNC angular rate feedback loop. practice. Unfortunately, this "conservative" approach would actually undersample the 330 Hz bandwidth without any pre-filtering, which would have many disadvantages. Figure 1 shows one of the disadvantages of reforming the noise energy in the output Nyquist band (20 Hz).
In Figure 1, the green curve represents the natural rate noise density (RND) and the red curve represents the same total noise energy distribution over the narrow 20 Hz bandwidth.
Figure 1. ADIS16460 angular rate noise density
Assuming that the total noise is evenly distributed over the low sampling rate (40 SPS) Nyquist band, it can be predicted by the following relationship that the resulting rate noise density will be approximately 0.017°/sec/√Hz:
This means that any digital filtering of 40 SPS data will result in approximately 4 times more noise than comparable filters when using a full sample rate of 2048 SPS. The fundamental problem is that for system integrators, it is wise to consider the key simulation properties in smart sensors, and by choosing the sampling rate appropriately and designing the digital filter correctly, you have the opportunity to optimize performance.
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