卡尔曼PSaR结合了著名的抛物面SAR（PSaR）和卡尔曼滤波器的先进平滑功能。这一创新工具旨在通过集成卡尔曼滤波来增强传统的PSaR，从而降低噪声并改善趋势检测。卡尔曼PSaR动态适应价格变动，使其成为一个高度有效的指标，用于发现趋势变化，同时最大限度地减少市场波动引起的错误信号的影响。下面是关于卡尔曼滤波增强PSaR的Python代码，请将其转化为通达信公式：

import numpy as np

N_kRoc = 5

def f_init(pricesource_kRoc):
    """
    Initialize state estimate and error covariance arrays for Kalman filter.
    
    Parameters:
    pricesource_kRoc (float): Initial price source used for Kalman filter.
    
    Returns:
    tuple: Arrays for state estimate and error covariance.
    """
    stateEstimate = np.full(N_kRoc, pricesource_kRoc)
    errorCovariance = np.full(N_kRoc, 100.0)
    return stateEstimate, errorCovariance

def f_kalman(pricesource_kRoc, stateEstimate, errorCovariance, processNoise_kRoc, measurementNoise_kRoc):
    """
    Perform Kalman filter update step.
    
    Parameters:
    pricesource_kRoc (float): Current price source.
    stateEstimate (np.array): State estimate array.
    errorCovariance (np.array): Error covariance array.
    processNoise_kRoc (float): Process noise parameter.
    measurementNoise_kRoc (float): Measurement noise parameter.
    
    Returns:
    float: Updated state estimate.
    """
    predictedStateEstimate = stateEstimate.copy()
    predictedErrorCovariance = errorCovariance + processNoise_kRoc
    kalmanGain = predictedErrorCovariance / (predictedErrorCovariance + measurementNoise_kRoc)
    stateEstimate = predictedStateEstimate + kalmanGain * (pricesource_kRoc - predictedStateEstimate)
    errorCovariance = (1 - kalmanGain) * predictedErrorCovariance
    return stateEstimate[0]

def kalman_PSaR(start, inc, max, processNoise_kRoc, measurementNoise_kRoc, close, high, low):
    """
    Calculate the Kalman PSaR indicator.
    
    Parameters:
    start (float): Initial acceleration factor.
    inc (float): Acceleration increment.
    max (float): Maximum acceleration factor.
    processNoise_kRoc (float): Process noise parameter for Kalman filter.
    measurementNoise_kRoc (float): Measurement noise parameter for Kalman filter.
    close (np.array): Closing prices.
    high (np.array): High prices.
    low (np.array): Low prices.
    
    Returns:
    float: Calculated Kalman PSaR value.
    """
    stateEstimate_close, errorCovariance_close = f_init(close[0])
    stateEstimate_high, errorCovariance_high = f_init(high[0])
    stateEstimate_low, errorCovariance_low = f_init(low[0])

    kalman_close = f_kalman(close[0], stateEstimate_close, errorCovariance_close, processNoise_kRoc, measurementNoise_kRoc)
    kalman_high = f_kalman(high[0], stateEstimate_high, errorCovariance_high, processNoise_kRoc, measurementNoise_kRoc)
    kalman_low = f_kalman(low[0], stateEstimate_low, errorCovariance_low, processNoise_kRoc, measurementNoise_kRoc)

    result = np.nan
    maxMin = np.nan
    acceleration = np.nan
    isBelow = np.nan
    isFirstTrendBar = False

    if len(close) == 1:
        if close > close[0]:
            isBelow = True
            maxMin = kalman_high
            result = kalman_low
        else:
            isBelow = False
            maxMin = kalman_low
            result = kalman_high
        isFirstTrendBar = True
        acceleration = start

    result = result + acceleration * (maxMin - result)

    if isBelow:
        if result > low[0]:
            isFirstTrendBar = True
            isBelow = False
            result = max(kalman_high, maxMin)
            maxMin = kalman_low
            acceleration = start
    else:
        if result < high[0]:
            isFirstTrendBar = True
            isBelow = True
            result = min(kalman_low, maxMin)
            maxMin = kalman_high
            acceleration = start

    if not isFirstTrendBar:
        if isBelow:
            if high[0] > maxMin:
                maxMin = kalman_high
                acceleration = min(acceleration + inc, max)
        else:
            if kalman_low < maxMin:
                maxMin = kalman_low
                acceleration = min(acceleration + inc, max)

    if isBelow:
        result = min(result, kalman_low)
    else:
        result = max(result, kalman_high)

    return result

def ma(src, length, type):
    """
    Calculate the moving average of the given price source.
    
    Parameters:
    src (np.array): Price source array.
    length (int): Length of the moving average.
    type (str): Type of moving average (e.g., 'SMA', 'EMA').
    
    Returns:
    float: Calculated moving average value.
    """
    if type == "SMA":
        return np.mean(src[-length:])
    elif type == "Hull":
        return np.mean(src[-length:])  # Simplified for example
    elif type == "EMA":
        return np.mean(src[-length:])  # Simplified for example
    elif type == "WMA":
        return np.mean(src[-length:])  # Simplified for example
    elif type == "Dema":
        return np.mean(src[-length:])  # Simplified for example
    elif type == "RMA":
        return np.mean(src[-length:])  # Simplified for example
    elif type == "LINREG":
        return np.mean(src[-length:])  # Simplified for example
    elif type == "TEMA":
        return np.mean(src[-length:])  # Simplified for example

# Example usage:
start = 0.02
inc = 0.02
max = 0.2
processNoise_kRoc = 0.001
measurementNoise_kRoc = 0.001
close = np.array([1.0, 1.1, 1.2, 1.3, 1.4])
high = np.array([1.2, 1.3, 1.4, 1.5, 1.6])
low = np.array([0.8, 0.9, 1.0, 1.1, 1.2])

psar = kalman_PSaR(start, inc, max, processNoise_kRoc, measurementNoise_kRoc, close, high, low)
print("Kalman PSaR:", psar)