yafl

Yet Another Filtering library

YAFL means Yet Another Filtering Library.

The library

Here you can find some Kalman filter variants:

Algorithm family	Basic	Adaptive	Robust	Adaptive robust
SUD EKF	✓	✓	✓	✓
SUD UKF	✓	✓	✓	✓
UD UKF	✓	✓

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 are available.

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

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 used if necessary. The code is numerically stable.
• Depends only on C standard library.

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*/

#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_LOG(...) 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*/

    /*
    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-7)

#define YAFL_SQRT sqrtf
#define YAFL_ABS  fabs

typedef float   yaflFloat;
typedef int32_t   yaflInt;

/* WARNING!!!
Fast UKF SSR updates may give dramatically incorrect results in case of adaptive Bierman filter
*/
//#define YAFL_USE_FAST_UKF

#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: Python 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