do not use es2015 here

third-party/kalman.js

@@ -11,79 +11,78 @@
 
 
 
-class KalmanFilter {
+function KalmanFilter(params) {
+	/**
+	* Create 1-dimensional kalman filter
+	* @param  {Number} options.R Process noise
+	* @param  {Number} options.Q Measurement noise
+	* @param  {Number} options.A State vector
+	* @param  {Number} options.B Control vector
+	* @param  {Number} options.C Measurement vector
+	* @return {KalmanFilter}
+	*/
 
-  /**
-  * Create 1-dimensional kalman filter
-  * @param  {Number} options.R Process noise
-  * @param  {Number} options.Q Measurement noise
-  * @param  {Number} options.A State vector
-  * @param  {Number} options.B Control vector
-  * @param  {Number} options.C Measurement vector
-  * @return {KalmanFilter}
-  */
-  constructor({R = 1, Q = 1, A = 1, B = 0, C = 1} = {}) {
+	this.R = typeof params.R == "undefined" ? 1 : params.R; // noise power desirable
+	this.Q = typeof params.Q == "undefined" ? 1 : params.Q; // noise power estimated
 
-    this.R = R; // noise power desirable
-    this.Q = Q; // noise power estimated
-
-    this.A = A;
-    this.C = C;
-    this.B = B;
-    this.cov = NaN;
-    this.x = NaN; // estimated signal without noise
-  }
-
-  /**
-  * Filter a new value
-  * @param  {Number} z Measurement
-  * @param  {Number} u Control
-  * @return {Number}
-  */
-  filter(z, u = 0) {
-
-    if (isNaN(this.x)) {
-      this.x = (1 / this.C) * z;
-      this.cov = (1 / this.C) * this.Q * (1 / this.C);
-    }
-    else {
-
-      // Compute prediction
-      const predX = (this.A * this.x) + (this.B * u);
-      const predCov = ((this.A * this.cov) * this.A) + this.R;
-
-      // Kalman gain
-      const K = predCov * this.C * (1 / ((this.C * predCov * this.C) + this.Q));
-
-      // Correction
-      this.x = predX + K * (z - (this.C * predX));
-      this.cov = predCov - (K * this.C * predCov);
-    }
-
-    return this.x;
-  }
-
-  /**
-  * Return the last filtered measurement
-  * @return {Number}
-  */
-  lastMeasurement() {
-    return this.x;
-  }
-
-  /**
-  * Set measurement noise Q
-  * @param {Number} noise
-  */
-  setMeasurementNoise(noise) {
-    this.Q = noise;
-  }
-
-  /**
-  * Set the process noise R
-  * @param {Number} noise
-  */
-  setProcessNoise(noise) {
-    this.R = noise;
-  }
+	this.A = typeof params.A == "undefined" ? 1 : params.A;
+	this.C = typeof params.C == "undefined" ? 1 : params.C;
+	this.B = typeof params.B == "undefined" ? 0 : params.B;
+	this.cov = NaN;
+	this.x = NaN; // estimated signal without noise
 }
+
+/**
+* Filter a new value
+* @param  {Number} z Measurement
+* @param  {Number} u Control
+* @return {Number}
+*/
+KalmanFilter.prototype.filter = function (z, u) {
+	if (typeof u == "undefined")
+		u = 0;
+	if (isNaN(this.x)) {
+		this.x = (1 / this.C) * z;
+		this.cov = (1 / this.C) * this.Q * (1 / this.C);
+	}
+	else {
+
+		// Compute prediction
+		const predX = (this.A * this.x) + (this.B * u);
+		const predCov = ((this.A * this.cov) * this.A) + this.R;
+
+		// Kalman gain
+		const K = predCov * this.C * (1 / ((this.C * predCov * this.C) + this.Q));
+
+		// Correction
+		this.x = predX + K * (z - (this.C * predX));
+		this.cov = predCov - (K * this.C * predCov);
+	}
+
+	return this.x;
+}
+
+/**
+* Return the last filtered measurement
+* @return {Number}
+*/
+KalmanFilter.prototype.lastMeasurement = function () {
+	return this.x;
+}
+
+/**
+* Set measurement noise Q
+* @param {Number} noise
+*/
+KalmanFilter.prototype.setMeasurementNoise = function (noise) {
+	this.Q = noise;
+}
+
+/**
+* Set the process noise R
+* @param {Number} noise
+*/
+KalmanFilter.prototype.setProcessNoise = function (noise) {
+	this.R = noise;
+}
+