What keeps the Internet of Things from becoming a tangled jumble of incoming data from various connected devices? And within a single device that feeds information into the IoT, how does sensor fusion work?
Implemented in a wide variety of product categories, sensor fusion is a necessity for applications like wearable gadgets supporting health and fitness and the body motion tracking devices used in the production of advanced CGI movies and gaming. Even products as “simple” as your smartphone require multiple sensors with lots of degrees of freedom and a powerful MCU, to collect, coordinate, process, analyze, filter, and communicate data.
The latest versions of sensor fusion are a set of adaptive prediction and filtering algorithms based on extended Kalman Filter theory that uses quaternion concepts to avoid mathematical singularity and deliver more reliable results. These algorithms “make sense” of all of the complex information coming from multiple sensors, including accelerometers, gyroscopes, compasses, and pressure sensors, by taking each sensor’s measurement data as input, and compensating for drift and other effects and limitations of each individual sensor, to output accurate and responsive dynamic results.
For one example, let’s take a look at the application of pedestrian dead reckoning (PDR) and the fusion of four sensors and five inputs. You’ve got your GPS input as well as your accelerometer, gyro, magnetometer, and pressure-sensor data coupled to an MCU.
Working together, a three-axis accelerometer and three-axis gyroscope function as a strapdown inertial navigation or pedometer-based portable navigation device. The accelerometer provides step detection, and the tilt-compensated compass -- a three-axis magnetometer -- if disturbed, allows the gyro to make heading adjustments. The compass calculates magnetic field and compensates for the gyro’s zero-rate drift over time. Meanwhile, the pressure sensor, working with the accelerometer, acts as an altimeter and conveys floor changes for indoor navigation.
So much data, so little time…
The MCU houses the Kalman Filter, which predicts sensor errors from the equations of inertial navigation. It estimates and compensates for the gyro’s long-term bias drift, magnetic anomalies, and provides data for dead-reckoning applications when GPS info is unavailable.
Et voila! Sensor fusion.
In the next few years, the market pundits project significant growth of sensors fueled by sensor fusion. According to many analysts, that growth in annual sales could increase from the multimillion units recorded in 2012 to reach more than a couple billion by 2016.
This analysis suggests the IoT will be pretty limited to the kinds of applications we’re all familiar with. But if you close your eyes and dream a bit, perhaps you can see, as I do, components of future sensor modules expanding beyond the current fusion of pressure sensors, accelerometers, gyros, and magnetometers to such applications as PDR and cardiac monitoring by including UV index sensors, gas, and volatile organic compound sensing for applications such as safety SmartWear. In my mind, robotic autonomous surgery functioning through sensor fusion is no longer the stuff of science fiction, and bionics, nano-biosensors fusing organic tissues with sensor technology, is the next logical step in sensor fusion.