Many datasheets serve both as technical specificationsand promotional collateral. As such, “specsmanship” often makes comparing two sensor components on paper complicated. This article focuses on a few key parameters that system designers should consider.
Measurement range refers to the span of measurement values the sensor can record between the lowest and highest possible values. Dynamic range of a sensor refers to the span between the largest and smallest values two consecutive samples can take. Some sensors have dynamic ranges that cover their whole measurement range; for others, dynamic range is a subset of the total measurement range (see figure). In the latter cases, the bias can be charged either through register settings or be adjusted automatically by an intelligent sensor based on the recent history of the incoming signal levels.
Many sensors offer a number of preset options of measurement and dynamic ranges that designers can select based on the signals they are trying to observe. A previous article gave the recommended range settings for common use cases for mobile devices.
Often designers would talk about using a 12-bit sensor versus an 8-bit sensor as shorthand to represent the sensitivity they wish to deploy. Sensitivity refers to the smallest signal resolvable by a sensor. It is affected by dynamic range, quantization level and the noise of a sensor.
Characterizing a sensor as a 12-bit sensor is to say that it has 4096 (nominally uniform) quantization levels across its dynamic range. Sensor sensitivity is rarely limited solely by the size of its quantization level. The determinant usually lies with the measurement noise of the sensor.
The RMS measurement noise of a sensor is the product of its noise power spectrum density and the square root of its measurement bandwidth. Hence the following equation gives the sensitivity of a sensor.
Sometimes zero-offset drift is simply called “drift” of a sensor. By the nature of their design, the zero reference of a sensor changes slowly over time. Drift occurs due to a combination of reasons stemming from the design of the sensing element and the associated electronics. Drift increases over the life of a sensor so it is important to understand if the drift performance specified in a datasheet represent zero-offset shift over a ten year product life or just the initial value when the device is fresh off the manufacturing line.
Among inertial sensors, drift in gyroscopes are most significant. However, that can be mitigated by intelligent algorithms which, for example, can use magnetometer and accelerometer data to mitigate against gyroscope drift.
Sampling Scheme and Bandwidth
Sensor bandwidth can often be configured through a register. As Nyquist Theorem teaches, a sensor subsystem using a uniform sampling scheme must sample at twice the frequency of the desired bandwidth. Some sensors provide a mechanism for an external device to trigger the taking of a sample. That can be useful when the system sampling scheme requires all sensors to be sampled synchronously. Alternatively, sensors can provide sample at a regularly period based on its own time base. This mode is easier to configure but algorithms and system designs need to allow each sensor to free run on its own time base.
Abstracting over Sensor Component Differences
Understanding datasheet specifications can help designers differentiate among sensors from different vendors. However, designers need the freedom to use sensors from multiple suppliers without materially affecting the user experience of their systems. This challenge can only be answered by intelligent sensor fusion algorithms, like that offered as part of our FreeMotion™ Library.