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Diffstat (limited to 'src/modules/mathlib/CMSIS/DSP_Lib/Source/FilteringFunctions/arm_lms_norm_f32.c')
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diff --git a/src/modules/mathlib/CMSIS/DSP_Lib/Source/FilteringFunctions/arm_lms_norm_f32.c b/src/modules/mathlib/CMSIS/DSP_Lib/Source/FilteringFunctions/arm_lms_norm_f32.c deleted file mode 100644 index b2ac45206..000000000 --- a/src/modules/mathlib/CMSIS/DSP_Lib/Source/FilteringFunctions/arm_lms_norm_f32.c +++ /dev/null @@ -1,456 +0,0 @@ -/* ----------------------------------------------------------------------
-* Copyright (C) 2010 ARM Limited. All rights reserved.
-*
-* $Date: 15. February 2012
-* $Revision: V1.1.0
-*
-* Project: CMSIS DSP Library
-* Title: arm_lms_norm_f32.c
-*
-* Description: Processing function for the floating-point Normalised LMS.
-*
-* Target Processor: Cortex-M4/Cortex-M3/Cortex-M0
-*
-* Version 1.1.0 2012/02/15
-* Updated with more optimizations, bug fixes and minor API changes.
-*
-* Version 1.0.10 2011/7/15
-* Big Endian support added and Merged M0 and M3/M4 Source code.
-*
-* Version 1.0.3 2010/11/29
-* Re-organized the CMSIS folders and updated documentation.
-*
-* Version 1.0.2 2010/11/11
-* Documentation updated.
-*
-* Version 1.0.1 2010/10/05
-* Production release and review comments incorporated.
-*
-* Version 1.0.0 2010/09/20
-* Production release and review comments incorporated
-*
-* Version 0.0.7 2010/06/10
-* Misra-C changes done
-* -------------------------------------------------------------------- */
-
-#include "arm_math.h"
-
-/**
- * @ingroup groupFilters
- */
-
-/**
- * @defgroup LMS_NORM Normalized LMS Filters
- *
- * This set of functions implements a commonly used adaptive filter.
- * It is related to the Least Mean Square (LMS) adaptive filter and includes an additional normalization
- * factor which increases the adaptation rate of the filter.
- * The CMSIS DSP Library contains normalized LMS filter functions that operate on Q15, Q31, and floating-point data types.
- *
- * A normalized least mean square (NLMS) filter consists of two components as shown below.
- * The first component is a standard transversal or FIR filter.
- * The second component is a coefficient update mechanism.
- * The NLMS filter has two input signals.
- * The "input" feeds the FIR filter while the "reference input" corresponds to the desired output of the FIR filter.
- * That is, the FIR filter coefficients are updated so that the output of the FIR filter matches the reference input.
- * The filter coefficient update mechanism is based on the difference between the FIR filter output and the reference input.
- * This "error signal" tends towards zero as the filter adapts.
- * The NLMS processing functions accept the input and reference input signals and generate the filter output and error signal.
- * \image html LMS.gif "Internal structure of the NLMS adaptive filter"
- *
- * The functions operate on blocks of data and each call to the function processes
- * <code>blockSize</code> samples through the filter.
- * <code>pSrc</code> points to input signal, <code>pRef</code> points to reference signal,
- * <code>pOut</code> points to output signal and <code>pErr</code> points to error signal.
- * All arrays contain <code>blockSize</code> values.
- *
- * The functions operate on a block-by-block basis.
- * Internally, the filter coefficients <code>b[n]</code> are updated on a sample-by-sample basis.
- * The convergence of the LMS filter is slower compared to the normalized LMS algorithm.
- *
- * \par Algorithm:
- * The output signal <code>y[n]</code> is computed by a standard FIR filter:
- * <pre>
- * y[n] = b[0] * x[n] + b[1] * x[n-1] + b[2] * x[n-2] + ...+ b[numTaps-1] * x[n-numTaps+1]
- * </pre>
- *
- * \par
- * The error signal equals the difference between the reference signal <code>d[n]</code> and the filter output:
- * <pre>
- * e[n] = d[n] - y[n].
- * </pre>
- *
- * \par
- * After each sample of the error signal is computed the instanteous energy of the filter state variables is calculated:
- * <pre>
- * E = x[n]^2 + x[n-1]^2 + ... + x[n-numTaps+1]^2.
- * </pre>
- * The filter coefficients <code>b[k]</code> are then updated on a sample-by-sample basis:
- * <pre>
- * b[k] = b[k] + e[n] * (mu/E) * x[n-k], for k=0, 1, ..., numTaps-1
- * </pre>
- * where <code>mu</code> is the step size and controls the rate of coefficient convergence.
- *\par
- * In the APIs, <code>pCoeffs</code> points to a coefficient array of size <code>numTaps</code>.
- * Coefficients are stored in time reversed order.
- * \par
- * <pre>
- * {b[numTaps-1], b[numTaps-2], b[N-2], ..., b[1], b[0]}
- * </pre>
- * \par
- * <code>pState</code> points to a state array of size <code>numTaps + blockSize - 1</code>.
- * Samples in the state buffer are stored in the order:
- * \par
- * <pre>
- * {x[n-numTaps+1], x[n-numTaps], x[n-numTaps-1], x[n-numTaps-2]....x[0], x[1], ..., x[blockSize-1]}
- * </pre>
- * \par
- * Note that the length of the state buffer exceeds the length of the coefficient array by <code>blockSize-1</code> samples.
- * The increased state buffer length allows circular addressing, which is traditionally used in FIR filters,
- * to be avoided and yields a significant speed improvement.
- * The state variables are updated after each block of data is processed.
- * \par Instance Structure
- * The coefficients and state variables for a filter are stored together in an instance data structure.
- * A separate instance structure must be defined for each filter and
- * coefficient and state arrays cannot be shared among instances.
- * There are separate instance structure declarations for each of the 3 supported data types.
- *
- * \par Initialization Functions
- * There is also an associated initialization function for each data type.
- * The initialization function performs the following operations:
- * - Sets the values of the internal structure fields.
- * - Zeros out the values in the state buffer.
- * \par
- * Instance structure cannot be placed into a const data section and it is recommended to use the initialization function.
- * \par Fixed-Point Behavior:
- * Care must be taken when using the Q15 and Q31 versions of the normalised LMS filter.
- * The following issues must be considered:
- * - Scaling of coefficients
- * - Overflow and saturation
- *
- * \par Scaling of Coefficients:
- * Filter coefficients are represented as fractional values and
- * coefficients are restricted to lie in the range <code>[-1 +1)</code>.
- * The fixed-point functions have an additional scaling parameter <code>postShift</code>.
- * At the output of the filter's accumulator is a shift register which shifts the result by <code>postShift</code> bits.
- * This essentially scales the filter coefficients by <code>2^postShift</code> and
- * allows the filter coefficients to exceed the range <code>[+1 -1)</code>.
- * The value of <code>postShift</code> is set by the user based on the expected gain through the system being modeled.
- *
- * \par Overflow and Saturation:
- * Overflow and saturation behavior of the fixed-point Q15 and Q31 versions are
- * described separately as part of the function specific documentation below.
- */
-
-
-/**
- * @addtogroup LMS_NORM
- * @{
- */
-
-
- /**
- * @brief Processing function for floating-point normalized LMS filter.
- * @param[in] *S points to an instance of the floating-point normalized LMS filter structure.
- * @param[in] *pSrc points to the block of input data.
- * @param[in] *pRef points to the block of reference data.
- * @param[out] *pOut points to the block of output data.
- * @param[out] *pErr points to the block of error data.
- * @param[in] blockSize number of samples to process.
- * @return none.
- */
-
-void arm_lms_norm_f32(
- arm_lms_norm_instance_f32 * S,
- float32_t * pSrc,
- float32_t * pRef,
- float32_t * pOut,
- float32_t * pErr,
- uint32_t blockSize)
-{
- float32_t *pState = S->pState; /* State pointer */
- float32_t *pCoeffs = S->pCoeffs; /* Coefficient pointer */
- float32_t *pStateCurnt; /* Points to the current sample of the state */
- float32_t *px, *pb; /* Temporary pointers for state and coefficient buffers */
- float32_t mu = S->mu; /* Adaptive factor */
- uint32_t numTaps = S->numTaps; /* Number of filter coefficients in the filter */
- uint32_t tapCnt, blkCnt; /* Loop counters */
- float32_t energy; /* Energy of the input */
- float32_t sum, e, d; /* accumulator, error, reference data sample */
- float32_t w, x0, in; /* weight factor, temporary variable to hold input sample and state */
-
- /* Initializations of error, difference, Coefficient update */
- e = 0.0f;
- d = 0.0f;
- w = 0.0f;
-
- energy = S->energy;
- x0 = S->x0;
-
- /* S->pState points to buffer which contains previous frame (numTaps - 1) samples */
- /* pStateCurnt points to the location where the new input data should be written */
- pStateCurnt = &(S->pState[(numTaps - 1u)]);
-
- /* Loop over blockSize number of values */
- blkCnt = blockSize;
-
-
-#ifndef ARM_MATH_CM0
-
- /* Run the below code for Cortex-M4 and Cortex-M3 */
-
- while(blkCnt > 0u)
- {
- /* Copy the new input sample into the state buffer */
- *pStateCurnt++ = *pSrc;
-
- /* Initialize pState pointer */
- px = pState;
-
- /* Initialize coeff pointer */
- pb = (pCoeffs);
-
- /* Read the sample from input buffer */
- in = *pSrc++;
-
- /* Update the energy calculation */
- energy -= x0 * x0;
- energy += in * in;
-
- /* Set the accumulator to zero */
- sum = 0.0f;
-
- /* Loop unrolling. Process 4 taps at a time. */
- tapCnt = numTaps >> 2;
-
- while(tapCnt > 0u)
- {
- /* Perform the multiply-accumulate */
- sum += (*px++) * (*pb++);
- sum += (*px++) * (*pb++);
- sum += (*px++) * (*pb++);
- sum += (*px++) * (*pb++);
-
- /* Decrement the loop counter */
- tapCnt--;
- }
-
- /* If the filter length is not a multiple of 4, compute the remaining filter taps */
- tapCnt = numTaps % 0x4u;
-
- while(tapCnt > 0u)
- {
- /* Perform the multiply-accumulate */
- sum += (*px++) * (*pb++);
-
- /* Decrement the loop counter */
- tapCnt--;
- }
-
- /* The result in the accumulator, store in the destination buffer. */
- *pOut++ = sum;
-
- /* Compute and store error */
- d = (float32_t) (*pRef++);
- e = d - sum;
- *pErr++ = e;
-
- /* Calculation of Weighting factor for updating filter coefficients */
- /* epsilon value 0.000000119209289f */
- w = (e * mu) / (energy + 0.000000119209289f);
-
- /* Initialize pState pointer */
- px = pState;
-
- /* Initialize coeff pointer */
- pb = (pCoeffs);
-
- /* Loop unrolling. Process 4 taps at a time. */
- tapCnt = numTaps >> 2;
-
- /* Update filter coefficients */
- while(tapCnt > 0u)
- {
- /* Perform the multiply-accumulate */
- *pb += w * (*px++);
- pb++;
-
- *pb += w * (*px++);
- pb++;
-
- *pb += w * (*px++);
- pb++;
-
- *pb += w * (*px++);
- pb++;
-
-
- /* Decrement the loop counter */
- tapCnt--;
- }
-
- /* If the filter length is not a multiple of 4, compute the remaining filter taps */
- tapCnt = numTaps % 0x4u;
-
- while(tapCnt > 0u)
- {
- /* Perform the multiply-accumulate */
- *pb += w * (*px++);
- pb++;
-
- /* Decrement the loop counter */
- tapCnt--;
- }
-
- x0 = *pState;
-
- /* Advance state pointer by 1 for the next sample */
- pState = pState + 1;
-
- /* Decrement the loop counter */
- blkCnt--;
- }
-
- S->energy = energy;
- S->x0 = x0;
-
- /* Processing is complete. Now copy the last numTaps - 1 samples to the
- satrt of the state buffer. This prepares the state buffer for the
- next function call. */
-
- /* Points to the start of the pState buffer */
- pStateCurnt = S->pState;
-
- /* Loop unrolling for (numTaps - 1u)/4 samples copy */
- tapCnt = (numTaps - 1u) >> 2u;
-
- /* copy data */
- while(tapCnt > 0u)
- {
- *pStateCurnt++ = *pState++;
- *pStateCurnt++ = *pState++;
- *pStateCurnt++ = *pState++;
- *pStateCurnt++ = *pState++;
-
- /* Decrement the loop counter */
- tapCnt--;
- }
-
- /* Calculate remaining number of copies */
- tapCnt = (numTaps - 1u) % 0x4u;
-
- /* Copy the remaining q31_t data */
- while(tapCnt > 0u)
- {
- *pStateCurnt++ = *pState++;
-
- /* Decrement the loop counter */
- tapCnt--;
- }
-
-#else
-
- /* Run the below code for Cortex-M0 */
-
- while(blkCnt > 0u)
- {
- /* Copy the new input sample into the state buffer */
- *pStateCurnt++ = *pSrc;
-
- /* Initialize pState pointer */
- px = pState;
-
- /* Initialize pCoeffs pointer */
- pb = pCoeffs;
-
- /* Read the sample from input buffer */
- in = *pSrc++;
-
- /* Update the energy calculation */
- energy -= x0 * x0;
- energy += in * in;
-
- /* Set the accumulator to zero */
- sum = 0.0f;
-
- /* Loop over numTaps number of values */
- tapCnt = numTaps;
-
- while(tapCnt > 0u)
- {
- /* Perform the multiply-accumulate */
- sum += (*px++) * (*pb++);
-
- /* Decrement the loop counter */
- tapCnt--;
- }
-
- /* The result in the accumulator is stored in the destination buffer. */
- *pOut++ = sum;
-
- /* Compute and store error */
- d = (float32_t) (*pRef++);
- e = d - sum;
- *pErr++ = e;
-
- /* Calculation of Weighting factor for updating filter coefficients */
- /* epsilon value 0.000000119209289f */
- w = (e * mu) / (energy + 0.000000119209289f);
-
- /* Initialize pState pointer */
- px = pState;
-
- /* Initialize pCcoeffs pointer */
- pb = pCoeffs;
-
- /* Loop over numTaps number of values */
- tapCnt = numTaps;
-
- while(tapCnt > 0u)
- {
- /* Perform the multiply-accumulate */
- *pb += w * (*px++);
- pb++;
-
- /* Decrement the loop counter */
- tapCnt--;
- }
-
- x0 = *pState;
-
- /* Advance state pointer by 1 for the next sample */
- pState = pState + 1;
-
- /* Decrement the loop counter */
- blkCnt--;
- }
-
- S->energy = energy;
- S->x0 = x0;
-
- /* Processing is complete. Now copy the last numTaps - 1 samples to the
- satrt of the state buffer. This prepares the state buffer for the
- next function call. */
-
- /* Points to the start of the pState buffer */
- pStateCurnt = S->pState;
-
- /* Copy (numTaps - 1u) samples */
- tapCnt = (numTaps - 1u);
-
- /* Copy the remaining q31_t data */
- while(tapCnt > 0u)
- {
- *pStateCurnt++ = *pState++;
-
- /* Decrement the loop counter */
- tapCnt--;
- }
-
-#endif /* #ifndef ARM_MATH_CM0 */
-
-}
-
-/**
- * @} end of LMS_NORM group
- */
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