/* ---------------------------------------------------------------------- * Copyright (C) 2010-2013 ARM Limited. All rights reserved. * * $Date: 17. January 2013 * $Revision: V1.4.1 * * 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 * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * - Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * - Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in * the documentation and/or other materials provided with the * distribution. * - Neither the name of ARM LIMITED nor the names of its contributors * may be used to endorse or promote products derived from this * software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * -------------------------------------------------------------------- */ #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 * blockSize samples through the filter. * pSrc points to input signal, pRef points to reference signal, * pOut points to output signal and pErr points to error signal. * All arrays contain blockSize values. * * The functions operate on a block-by-block basis. * Internally, the filter coefficients b[n] 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 y[n] is computed by a standard FIR filter: *
    
 *     y[n] = b[0] * x[n] + b[1] * x[n-1] + b[2] * x[n-2] + ...+ b[numTaps-1] * x[n-numTaps+1]    
 * 
* * \par * The error signal equals the difference between the reference signal d[n] and the filter output: *
    
 *     e[n] = d[n] - y[n].    
 * 
* * \par * After each sample of the error signal is computed the instanteous energy of the filter state variables is calculated: *
    
 *    E = x[n]^2 + x[n-1]^2 + ... + x[n-numTaps+1]^2.    
 * 
* The filter coefficients b[k] are then updated on a sample-by-sample basis: *
    
 *     b[k] = b[k] + e[n] * (mu/E) * x[n-k],  for k=0, 1, ..., numTaps-1    
 * 
* where mu is the step size and controls the rate of coefficient convergence. *\par * In the APIs, pCoeffs points to a coefficient array of size numTaps. * Coefficients are stored in time reversed order. * \par *
    
 *    {b[numTaps-1], b[numTaps-2], b[N-2], ..., b[1], b[0]}    
 * 
* \par * pState points to a state array of size numTaps + blockSize - 1. * Samples in the state buffer are stored in the order: * \par *
    
 *    {x[n-numTaps+1], x[n-numTaps], x[n-numTaps-1], x[n-numTaps-2]....x[0], x[1], ..., x[blockSize-1]}    
 * 
* \par * Note that the length of the state buffer exceeds the length of the coefficient array by blockSize-1 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, energy, x0, pState. Also set all of the values in pState to zero. * For Q7, Q15, and Q31 the following fields must also be initialized; * recipTable, postShift * * \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 [-1 +1). * The fixed-point functions have an additional scaling parameter postShift. * At the output of the filter's accumulator is a shift register which shifts the result by postShift bits. * This essentially scales the filter coefficients by 2^postShift and * allows the filter coefficients to exceed the range [+1 -1). * The value of postShift 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_FAMILY /* 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_FAMILY */ } /** * @} end of LMS_NORM group */