FormaK Runtime - New FormaK Feature - Building


FormaK aims to combine symbolic modeling for fast, efficient system modelling with code generation to create performant code that is easy to use.

The new feature provides an extension to the fifth of the Five Keys "C++ interfaces to support a variety of model uses" and "Python implementation of the model and supporting tooling" by simplifying how Kalman Filters are managed into a single interface.

After the original Python design and C++ design, the EKF interface from the library provided the wrappers around the math for updating the state and variances and code generation to implement the models but not much else. The code to run the filter looked something like:

double process_dt = options.reading_dt_base + 0.05 - currentTime;
combined = ekf.process_model(process_dt, combined, control);
currentTime = currentTime + process_dt;

featuretest::Simple zero_sensor_reading(SimpleOptions{});
combined = ekf.sensor_model(combined, zero_sensor_reading);

process_dt = options.reading_dt_base + 0.06 - currentTime;
combined = ekf.process_model(process_dt, combined, control);
currentTime = currentTime + process_dt;

featuretest::Simple one_sensor_reading(SimpleOptions{});
combined = ekf.sensor_model(combined, one_sensor_reading);

process_dt = options.reading_dt_base + 0.07 - currentTime;
combined = ekf.process_model(process_dt, combined, control);
currentTime = currentTime + process_dt;

featuretest::Simple two_sensor_reading(SimpleOptions{});
combined = ekf.sensor_model(combined, two_sensor_reading);

process_dt = options.output_dt - currentTime;
combined = ekf.process_model(process_dt, combined, control);
currentTime = currentTime + process_dt;

To simplify the interface down to the essential, a new class was created: the ManagedFilter. This provides a single tick interface to handle both process updates and sensor updates.

mf.tick(options.output_dt, control,
            mf.wrap<Simple>(options.reading_dt_base + 0.05,
            mf.wrap<Simple>(options.reading_dt_base + 0.06,
            mf.wrap<Simple>(options.reading_dt_base + 0.07,

The ManagedFilter supports both FormaK-generated and non-FormaK implementations of a Kalman Filter.

Check out the code or get the latest updates for FormaK on Github.


One area of the filter that I'm proud of is the C++ foo I had to sort out to get the feature working. The ManagedFilter class doesn't use any code generation, but it does interface with generated code from the C++ generation of the Extended Kalman Filter. This means that it needs to deal with both interfaces that have a Control and Calibration type and interfaces that have only one or neither.

In C++, a pattern called SFINAE or Substitution Failure Is Not An Error can be used to achieve this interface without code generation. At a high level, the idea is to template the members of the class (even if they otherwise wouldn't require a template) so that when the type is substituted into the template it can pass (and become part of the interface) or fail (and not be a part of the interface) based on the filter implementation it supports.

Getting the details of SFINAE to work was a large chunk of the time spent on the feature, but in the end I think I've achieved a reasonable solution to the problem and something I can build on and extend for future features. That said, I am only cautiously optimistic I could extend the SFINAE approach further becuase it could become too verbose or go beyond my understanding of how to make it work. We shall see.

What's next?

Filter Improvements

A design for adding innovation filtering is coming soon...

Improved Netcode

With the manipulation of the process updates and sensor updates wrapped into a neat single interface, additional improvements can be made. For example, it becomes easier to optionally include rollback "netcode" into the ManagedFilter to better handle out of order sensor updates.

In the current system, if a sensor reading comes in from before output time of the previous tick, the process update is used to go back in time to that sensor reading before returning forward in time to the new tick time. This adds additional variance that isn't strictly necessary due to exta use of the process update.

With rollback, a history of recent states can be saved and then played forward. For the sensor reading earlier in time, the state would be rolled back to before this sensor reading, then played forward along with interim sensor readings to provide the lowest variance estimate of the state by recreating a monotonically ordered timeline of sensor updates. This approach adds compute overhead and memory overhead for storing and re-running sensor data, sensor updates and process updates but promises the best possible estimator accuracy.