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  2. Joint Probabilistic Data Association Filter - Wikipedia

    en.wikipedia.org/wiki/Joint_Probabilistic_Data...

    The sample code in demo2DDataAssociation demonstrates how the algorithms can be used in a simple scenario. Python: The PDAF, JPDAF and other data association methods are implemented in Stone-Soup. [10] A tutorial demonstrates how the algorithms can be used. [11] [12]

  3. MATLAB - Wikipedia

    en.wikipedia.org/wiki/MATLAB

    MATLAB (an abbreviation of "MATrix LABoratory" [18]) is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks.MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.

  4. Conjugate gradient method - Wikipedia

    en.wikipedia.org/wiki/Conjugate_gradient_method

    The above algorithm gives the most straightforward explanation of the conjugate gradient method. Seemingly, the algorithm as stated requires storage of all previous searching directions and residue vectors, as well as many matrix–vector multiplications, and thus can be computationally expensive.

  5. Levenberg–Marquardt algorithm - Wikipedia

    en.wikipedia.org/wiki/Levenberg–Marquardt...

    In mathematics and computing, the Levenberg–Marquardt algorithm (LMA or just LM), also known as the damped least-squares (DLS) method, is used to solve non-linear least squares problems. These minimization problems arise especially in least squares curve fitting .

  6. Least mean squares filter - Wikipedia

    en.wikipedia.org/wiki/Least_mean_squares_filter

    This makes it very hard (if not impossible) to choose a learning rate that guarantees stability of the algorithm (Haykin 2002). The Normalised least mean squares filter (NLMS) is a variant of the LMS algorithm that solves this problem by normalising with the power of the input. The NLMS algorithm can be summarised as:

  7. Nelder–Mead method - Wikipedia

    en.wikipedia.org/wiki/Nelder–Mead_method

    John Burkardt: Nelder–Mead code in Matlab - note that a variation of the Nelder–Mead method is also implemented by the Matlab function fminsearch. Nelder-Mead optimization in Python in the SciPy library. nelder-mead - A Python implementation of the Nelder–Mead method; NelderMead() - A Go/Golang implementation

  8. Block-matching and 3D filtering - Wikipedia

    en.wikipedia.org/wiki/Block-matching_and_3D...

    Block-matching and 3D filtering (BM3D) is a 3-D block-matching algorithm used primarily for noise reduction in images. [1] It is one of the expansions of the non-local means methodology. [2] There are two cascades in BM3D: a hard-thresholding and a Wiener filter stage, both involving the following parts: grouping, collaborative filtering, and ...

  9. Total variation denoising - Wikipedia

    en.wikipedia.org/wiki/Total_variation_denoising

    The regularization parameter plays a critical role in the denoising process. When =, there is no smoothing and the result is the same as minimizing the sum of squares.As , however, the total variation term plays an increasingly strong role, which forces the result to have smaller total variation, at the expense of being less like the input (noisy) signal.