CMSIS DSP library
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Diff: cmsis_dsp/FilteringFunctions/arm_lms_f32.c
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cmsis_dsp/FilteringFunctions/arm_lms_f32.c Wed Nov 28 12:30:09 2012 +0000 @@ -0,0 +1,434 @@ +/* ---------------------------------------------------------------------- +* Copyright (C) 2010 ARM Limited. All rights reserved. +* +* $Date: 15. February 2012 +* $Revision: V1.1.0 +* +* Project: CMSIS DSP Library +* Title: arm_lms_f32.c +* +* Description: Processing function for the floating-point LMS filter. +* +* 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 Least Mean Square (LMS) Filters + * + * LMS filters are a class of adaptive filters that are able to "learn" an unknown transfer functions. + * LMS filters use a gradient descent method in which the filter coefficients are updated based on the instantaneous error signal. + * Adaptive filters are often used in communication systems, equalizers, and noise removal. + * The CMSIS DSP Library contains LMS filter functions that operate on Q15, Q31, and floating-point data types. + * The library also contains normalized LMS filters in which the filter coefficient adaptation is indepedent of the level of the input signal. + * + * An LMS 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 LMS 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 LMS 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 Least Mean Square 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 filter coefficients <code>b[k]</code> are updated on a sample-by-sample basis: + * <pre> + * b[k] = b[k] + e[n] * mu * 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 + * Use of the initialization function is optional. + * However, if the initialization function is used, then the instance structure cannot be placed into a const data section. + * To place an instance structure into a const data section, the instance structure must be manually initialized. + * Set the values in the state buffer to zeros before static initialization. + * The code below statically initializes each of the 3 different data type filter instance structures + * <pre> + * arm_lms_instance_f32 S = {numTaps, pState, pCoeffs, mu}; + * arm_lms_instance_q31 S = {numTaps, pState, pCoeffs, mu, postShift}; + * arm_lms_instance_q15 S = {numTaps, pState, pCoeffs, mu, postShift}; + * </pre> + * where <code>numTaps</code> is the number of filter coefficients in the filter; <code>pState</code> is the address of the state buffer; + * <code>pCoeffs</code> is the address of the coefficient buffer; <code>mu</code> is the step size parameter; and <code>postShift</code> is the shift applied to coefficients. + * + * \par Fixed-Point Behavior: + * Care must be taken when using the Q15 and Q31 versions of the 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 + * @{ + */ + +/** + * @details + * This function operates on floating-point data types. + * + * @brief Processing function for floating-point LMS filter. + * @param[in] *S points to an instance of the floating-point 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_f32( + const arm_lms_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 sum, e, d; /* accumulator, error, reference data sample */ + float32_t w = 0.0f; /* weight factor */ + + e = 0.0f; + d = 0.0f; + + /* S->pState points to state array 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)]); + + 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); + + /* 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 the updating filter coefficients */ + w = e * mu; + + /* 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 = *pb + (w * (*px++)); + pb++; + + *pb = *pb + (w * (*px++)); + pb++; + + *pb = *pb + (w * (*px++)); + pb++; + + *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 = *pb + (w * (*px++)); + pb++; + + /* Decrement the loop counter */ + tapCnt--; + } + + /* Advance state pointer by 1 for the next sample */ + pState = pState + 1; + + /* Decrement the loop counter */ + blkCnt--; + } + + + /* 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) 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; + + /* 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 is stored in the destination buffer. */ + *pOut++ = sum; + + /* Compute and store error */ + d = (float32_t) (*pRef++); + e = d - sum; + *pErr++ = e; + + /* Weighting factor for the LMS version */ + w = e * mu; + + /* Initialize pState pointer */ + px = pState; + + /* Initialize pCoeffs pointer */ + pb = pCoeffs; + + /* Loop over numTaps number of values */ + tapCnt = numTaps; + + while(tapCnt > 0u) + { + /* Perform the multiply-accumulate */ + *pb = *pb + (w * (*px++)); + pb++; + + /* Decrement the loop counter */ + tapCnt--; + } + + /* Advance state pointer by 1 for the next sample */ + pState = pState + 1; + + /* Decrement the loop counter */ + blkCnt--; + } + + + /* Processing is complete. Now copy the last numTaps - 1 samples to the + * start 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 data */ + while(tapCnt > 0u) + { + *pStateCurnt++ = *pState++; + + /* Decrement the loop counter */ + tapCnt--; + } + +#endif /* #ifndef ARM_MATH_CM0 */ + +} + +/** + * @} end of LMS group + */