diff options
Diffstat (limited to 'Drivers/CMSIS/DSP/Source/FilteringFunctions/arm_lms_f32.c')
| -rw-r--r-- | Drivers/CMSIS/DSP/Source/FilteringFunctions/arm_lms_f32.c | 475 |
1 files changed, 289 insertions, 186 deletions
diff --git a/Drivers/CMSIS/DSP/Source/FilteringFunctions/arm_lms_f32.c b/Drivers/CMSIS/DSP/Source/FilteringFunctions/arm_lms_f32.c index e5728b411..4fc6e7e29 100644 --- a/Drivers/CMSIS/DSP/Source/FilteringFunctions/arm_lms_f32.c +++ b/Drivers/CMSIS/DSP/Source/FilteringFunctions/arm_lms_f32.c @@ -3,13 +3,13 @@ * Title: arm_lms_f32.c * Description: Processing function for the floating-point LMS filter * - * $Date: 27. January 2017 - * $Revision: V.1.5.1 + * $Date: 18. March 2019 + * $Revision: V1.6.0 * * Target Processor: Cortex-M cores * -------------------------------------------------------------------- */ /* - * Copyright (C) 2010-2017 ARM Limited or its affiliates. All rights reserved. + * Copyright (C) 2010-2019 ARM Limited or its affiliates. All rights reserved. * * SPDX-License-Identifier: Apache-2.0 * @@ -29,146 +29,143 @@ #include "arm_math.h" /** - * @ingroup groupFilters + @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. - * To do this manually without calling the init function, assign the follow subfields of the instance structure: - * numTaps, pCoeffs, mu, postShift (not for f32), pState. Also set all of the values in pState to zero. - * - * \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. + @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. + To do this manually without calling the init function, assign the follow subfields of the instance structure: + numTaps, pCoeffs, mu, postShift (not for f32), pState. Also set all of the values in pState to zero. + + @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 - * @{ + @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. + @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 */ - +#if defined(ARM_MATH_NEON) void arm_lms_f32( const arm_lms_instance_f32 * S, - float32_t * pSrc, + const float32_t * pSrc, float32_t * pRef, float32_t * pOut, float32_t * pErr, @@ -184,6 +181,9 @@ void arm_lms_f32( float32_t sum, e, d; /* accumulator, error, reference data sample */ float32_t w = 0.0f; /* weight factor */ + float32x4_t tempV, sumV, xV, bV; + float32x2_t tempV2; + e = 0.0f; d = 0.0f; @@ -193,11 +193,6 @@ void arm_lms_f32( blkCnt = blockSize; - -#if defined (ARM_MATH_DSP) - - /* Run the below code for Cortex-M4 and Cortex-M3 */ - while (blkCnt > 0U) { /* Copy the new input sample into the state buffer */ @@ -211,21 +206,27 @@ void arm_lms_f32( /* Set the accumulator to zero */ sum = 0.0f; + sumV = vdupq_n_f32(0.0); - /* Loop unrolling. Process 4 taps at a time. */ + /* 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++); + xV = vld1q_f32(px); + bV = vld1q_f32(pb); + sumV = vmlaq_f32(sumV, xV, bV); + + px += 4; + pb += 4; /* Decrement the loop counter */ tapCnt--; } + tempV2 = vpadd_f32(vget_low_f32(sumV),vget_high_f32(sumV)); + sum = tempV2[0] + tempV2[1]; + /* If the filter length is not a multiple of 4, compute the remaining filter taps */ tapCnt = numTaps % 0x4U; @@ -256,24 +257,21 @@ void arm_lms_f32( /* Initialize coeff pointer */ pb = (pCoeffs); - /* Loop unrolling. Process 4 taps at a time. */ + /* Process 4 taps at a time. */ tapCnt = numTaps >> 2; /* Update filter coefficients */ while (tapCnt > 0U) { /* Perform the multiply-accumulate */ - *pb = *pb + (w * (*px++)); - pb++; + xV = vld1q_f32(px); + bV = vld1q_f32(pb); + px += 4; + bV = vmlaq_n_f32(bV,xV,w); - *pb = *pb + (w * (*px++)); - pb++; - - *pb = *pb + (w * (*px++)); - pb++; + vst1q_f32(pb,bV); + pb += 4; - *pb = *pb + (w * (*px++)); - pb++; /* Decrement the loop counter */ tapCnt--; @@ -307,16 +305,16 @@ void arm_lms_f32( /* Points to the start of the pState buffer */ pStateCurnt = S->pState; - /* Loop unrolling for (numTaps - 1U) samples copy */ + /* Process 4 taps at a time for (numTaps - 1U) samples copy */ tapCnt = (numTaps - 1U) >> 2U; /* copy data */ while (tapCnt > 0U) { - *pStateCurnt++ = *pState++; - *pStateCurnt++ = *pState++; - *pStateCurnt++ = *pState++; - *pStateCurnt++ = *pState++; + tempV = vld1q_f32(pState); + vst1q_f32(pStateCurnt,tempV); + pState += 4; + pStateCurnt += 4; /* Decrement the loop counter */ tapCnt--; @@ -334,9 +332,37 @@ void arm_lms_f32( tapCnt--; } + +} #else +void arm_lms_f32( + const arm_lms_instance_f32 * S, + const 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 */ + float32_t acc, e; /* Accumulator, error */ + float32_t w; /* Weight factor */ + uint32_t numTaps = S->numTaps; /* Number of filter coefficients in the filter */ + uint32_t tapCnt, blkCnt; /* Loop counters */ + + /* Initializations of error, difference, Coefficient update */ + e = 0.0f; + w = 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)]); - /* Run the below code for Cortex-M0 */ + /* initialise loop count */ + blkCnt = blockSize; while (blkCnt > 0U) { @@ -346,85 +372,162 @@ void arm_lms_f32( /* Initialize pState pointer */ px = pState; - /* Initialize pCoeffs pointer */ + /* Initialize coefficient pointer */ pb = pCoeffs; /* Set the accumulator to zero */ - sum = 0.0f; + acc = 0.0f; + +#if defined (ARM_MATH_LOOPUNROLL) + + /* Loop unrolling: Compute 4 taps at a time. */ + tapCnt = numTaps >> 2U; + + while (tapCnt > 0U) + { + /* Perform the multiply-accumulate */ + acc += (*px++) * (*pb++); + + acc += (*px++) * (*pb++); + + acc += (*px++) * (*pb++); + + acc += (*px++) * (*pb++); + + /* Decrement loop counter */ + tapCnt--; + } + + /* Loop unrolling: Compute remaining taps */ + tapCnt = numTaps % 0x4U; - /* Loop over numTaps number of values */ +#else + + /* Initialize tapCnt with number of samples */ tapCnt = numTaps; +#endif /* #if defined (ARM_MATH_LOOPUNROLL) */ + while (tapCnt > 0U) { /* Perform the multiply-accumulate */ - sum += (*px++) * (*pb++); + acc += (*px++) * (*pb++); /* Decrement the loop counter */ tapCnt--; } - /* The result is stored in the destination buffer. */ - *pOut++ = sum; + /* Store the result from accumulator into the destination buffer. */ + *pOut++ = acc; /* Compute and store error */ - d = (float32_t) (*pRef++); - e = d - sum; + e = (float32_t) *pRef++ - acc; *pErr++ = e; - /* Weighting factor for the LMS version */ + /* Calculation of Weighting factor for updating filter coefficients */ w = e * mu; /* Initialize pState pointer */ - px = pState; + /* Advance state pointer by 1 for the next sample */ + px = pState++; - /* Initialize pCoeffs pointer */ + /* Initialize coefficient pointer */ pb = pCoeffs; - /* Loop over numTaps number of values */ - tapCnt = numTaps; +#if defined (ARM_MATH_LOOPUNROLL) + /* Loop unrolling: Compute 4 taps at a time. */ + tapCnt = numTaps >> 2U; + + /* Update filter coefficients */ while (tapCnt > 0U) { /* Perform the multiply-accumulate */ - *pb = *pb + (w * (*px++)); + *pb += w * (*px++); pb++; - /* Decrement the loop counter */ + *pb += w * (*px++); + pb++; + + *pb += w * (*px++); + pb++; + + *pb += w * (*px++); + pb++; + + /* Decrement loop counter */ tapCnt--; } - /* Advance state pointer by 1 for the next sample */ - pState = pState + 1; + /* Loop unrolling: Compute remaining taps */ + tapCnt = numTaps % 0x4U; - /* Decrement the loop counter */ +#else + + /* Initialize tapCnt with number of samples */ + tapCnt = numTaps; + +#endif /* #if defined (ARM_MATH_LOOPUNROLL) */ + + while (tapCnt > 0U) + { + /* Perform the multiply-accumulate */ + *pb += w * (*px++); + pb++; + + /* Decrement loop counter */ + tapCnt--; + } + + /* Decrement 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. */ + /* 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 data */ +#if defined (ARM_MATH_LOOPUNROLL) + + /* Loop unrolling: Compute 4 taps at a time. */ + tapCnt = (numTaps - 1U) >> 2U; - /* Copy the data */ while (tapCnt > 0U) { *pStateCurnt++ = *pState++; + *pStateCurnt++ = *pState++; + *pStateCurnt++ = *pState++; + *pStateCurnt++ = *pState++; - /* Decrement the loop counter */ + /* Decrement loop counter */ tapCnt--; } -#endif /* #if defined (ARM_MATH_DSP) */ + /* Loop unrolling: Compute remaining taps */ + tapCnt = (numTaps - 1U) % 0x4U; + +#else + + /* Initialize tapCnt with number of samples */ + tapCnt = (numTaps - 1U); + +#endif /* #if defined (ARM_MATH_LOOPUNROLL) */ + + while (tapCnt > 0U) + { + *pStateCurnt++ = *pState++; + + /* Decrement loop counter */ + tapCnt--; + } } +#endif /* #if defined(ARM_MATH_NEON) */ /** - * @} end of LMS group - */ + @} end of LMS group + */ |