Yet Another Filtering library

YAFL means Yet Another Filtering Library. Our library is in aplha stage. So, if you need some mature lib then you should consider the solutions listed below.

There sre several libraries which implement Kalman filters for, e.g.:

• TinyEKF which is intended for usage on FPU enabled platforms;
• libfixkalman which can be used without FPU.

There are also libraries for python:

• FilterPy;
• pykalman.

The library

Technically speaking all filters in YAFL are adaptive since all of them have at least a measurement noice covariance adjustment. The term Adaptive is used in our docs for Kalman filter variants with H-infinity divergence correction.

In YAFL you can find these Kalman filter variants:

where:

• SUD means Sequential UD-factorized
• UD means UD-factorized
• EKF means Extended Kalman Filter
• UKF means Unscented Kalman Filter
• Basic means basic algorithm
• Adaptive means a Kalman filter with adaptive divergence correction. We use H-infinity filter to correct the divergence
• Robust means Robustified Kalman filter, see West1981

For all EKF variants we have Bierman and Joseph updates. For sequential UD-factorized UKF only Bierman updates have been implemented.

And yes, we can actually use EKF tricks with UKF!

Notes on process and measurement noice covariance adjustments

We used this paper to implement optional Q and R adaptive adjustments. Here are som notes on our implementation:

• All filters in this lib have the optional measurement noice covariance adjustment which can be enabled by setting rff to a small positive number e.g. 1e-4.
• All EKF filters in this lib have the optional process noice covariance adjustment which can be enabled by setting qff to a small positive number e.g. 1e-4.
• None of UKF filters have the optional process noice covariance adjustment as it leads to filters instability.

Notes on implementation

The library is written in C and is intended for embedded systems usage:

• We use static memory allocation
• We use cache-friendly algorithms when available.
• Regularization techniques are used when necessary. The code is numerically stable.
• Depends only on C standard library.

Using with C

To use the library you need to:

• go to our Releases page,
• download and unpack the latest release source code archive,
• add the folowing files to you C/C++ project:
  • src/yafl_math.c
  • src/yafl_math.h
  • src/yafl.c
  • src/yafl.h
• write yafl_config.h file and add it to you project. For Cortex-M4F or similar the file may look ike this:

/*yafl_config.h*/

/*yafl_config.h*/

#ifndef YAFL_CONFIG_H
#define YAFL_CONFIG_H

#include <math.h>
#include <stdint.h>

#ifdef DEBUG
    /*
    In this example we will use standard output.
    You can actually use any printf implementation you want.
    */
#   include <stdio.h>
#   define YAFL_DBG(...) fprintf(stderr, __VA_ARGS__)

    /*
    Using branch speculation may save some clocks...
    */
#   ifdef __GNUC__
#       define YAFL_UNLIKELY(x) __builtin_expect((x), 0)
#   else /*__GNUC__*/
#       define YAFL_UNLIKELY(x) (x)
#   endif/*__GNUC__*/
#else /*DEBUG*/
#   define YAFL_DBG(...) /*Do nothing here*/
    /*
    Here we have "Never" actually, but you can use some of above definitions if you want.
    */
#   define YAFL_UNLIKELY(x) (0)
#endif/*DEBUG*/

#define YAFL_EPS  (1.0e-6)
#define YAFL_SQRT sqrtf
#define YAFL_ABS  fabsf
#define YAFL_EXP  expf
#define YAFL_LOG  logf


typedef float   yaflFloat;
typedef int32_t   yaflInt;

#endif/*YAFL_CONFIG_H*/

• read the C-Manual for usage details,
• write some usefull code which use our library in you project.

Using with Python

We also have a Python extension for prototyping purposes. Python 3.5+ with 64bit is supproted.

To use the extension you need to:

• go to Releases,
• download latest yaflpy-<latest version>.tar.gz,
• install it:

# Cython, numpy, scipy, setuptools, wheel
# are needed at this point
pip install path_to/yaflpy-\<latest version\>.tar.gz

• read the Python-Manual for usage details.
• import the extension:

import yaflpy

• write some code which use the extension.

References

[West1981] M. West, “Robust Sequential Approximate Bayesian Estimation”, J. R. Statist. Soc. B (1981), 43, No. 2, pp. 157-166