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js实现滤波

2026-02-01 18:32:05JavaScript

滤波实现方法

在JavaScript中实现滤波通常涉及信号处理或数据平滑。以下是几种常见的滤波方法：

移动平均滤波

移动平均是最简单的滤波方法之一，通过计算数据点周围窗口的平均值来平滑数据。

function movingAverageFilter(data, windowSize) {
  const result = [];
  for (let i = 0; i < data.length; i++) {
    const start = Math.max(0, i - Math.floor(windowSize / 2));
    const end = Math.min(data.length, i + Math.ceil(windowSize / 2));
    const window = data.slice(start, end);
    const avg = window.reduce((sum, val) => sum + val, 0) / window.length;
    result.push(avg);
  }
  return result;
}

指数平滑滤波

指数平滑给予近期数据更大的权重，适用于时间序列数据。

function exponentialSmoothing(data, alpha) {
  const result = [data[0]];
  for (let i = 1; i < data.length; i++) {
    result.push(alpha * data[i] + (1 - alpha) * result[i - 1]);
  }
  return result;
}

中值滤波

中值滤波能有效去除脉冲噪声，通过取窗口中值代替当前值。

function medianFilter(data, windowSize) {
  const result = [];
  const halfWindow = Math.floor(windowSize / 2);
  for (let i = 0; i < data.length; i++) {
    const start = Math.max(0, i - halfWindow);
    const end = Math.min(data.length, i + halfWindow + 1);
    const window = data.slice(start, end);
    window.sort((a, b) => a - b);
    result.push(window[Math.floor(window.length / 2)]);
  }
  return result;
}

卡尔曼滤波

对于更复杂的系统，可以实现简单的卡尔曼滤波器。

js实现滤波

class KalmanFilter {
  constructor(initialState, initialUncertainty, measurementNoise, processNoise) {
    this.state = initialState;
    this.uncertainty = initialUncertainty;
    this.measurementNoise = measurementNoise;
    this.processNoise = processNoise;
  }

  update(measurement) {
    const kalmanGain = this.uncertainty / (this.uncertainty + this.measurementNoise);
    this.state = this.state + kalmanGain * (measurement - this.state);
    this.uncertainty = (1 - kalmanGain) * this.uncertainty + this.processNoise;
    return this.state;
  }
}