Skip to content

Commit a8fa271

Browse files
loeslerloesler
authored
Minimal detectable bias (#379)
- condition corrected for estimating minimal detectable bias (cf. #372)
1 parent 117bac8 commit a8fa271

File tree

1 file changed

+70
-76
lines changed

1 file changed

+70
-76
lines changed

‎JAG3D/src/org/applied_geodesy/adjustment/network/NetworkAdjustment.java

Lines changed: 70 additions & 76 deletions
Original file line numberDiff line numberDiff line change
@@ -752,33 +752,32 @@ public void addSubRedundanceAndCofactor2Deflection(Point deflectionPoint, boolea
752752
deflectionY.setMaximumTolerableBias(nabla0.get(1));
753753
}
754754
else {
755-
Vector nabla0 = new DenseVector(nabla, true);
756-
double normNabla = nabla0.norm(Vector.Norm.Two);
757-
if (normNabla < SQRT_EPS) {
758-
for (int j=0; j<dim; j++)
759-
nabla0.set(j, confidenceRegion.getMinimalDetectableBias(j));
760-
}
761-
else {
762-
Qnn.mult(Pv, nabla);
763-
double nablaCoVarNable = Math.abs(nabla.dot(Pv0));
764-
((VerticalDeflectionX)deflectionX).setNablaCoVarNabla( nablaCoVarNable );
755+
Qnn.mult(Pv, nabla);
756+
double normNabla = nabla.norm(Vector.Norm.Two);
757+
double nablaCoVarNable = Math.abs(nabla.dot(Pv0));
758+
((VerticalDeflectionX)deflectionX).setNablaCoVarNabla( nablaCoVarNable );
765759

766-
nabla = nabla.scale(-1.0);
767-
deflectionX.setGrossError(nabla.get(0));
768-
deflectionY.setGrossError(nabla.get(1));
760+
nabla = nabla.scale(-1.0);
761+
deflectionX.setGrossError(nabla.get(0));
762+
deflectionY.setGrossError(nabla.get(1));
769763

770-
//Qll_P.mult(nabla, ep);
764+
//Qll_P.mult(nabla, ep);
771765

772-
Vector subPsubPQvvPNabla = new DenseVector(dim);
773-
subPsubPQvvP.mult(nabla, subPsubPQvvPNabla);
766+
Vector subPsubPQvvPNabla = new DenseVector(dim);
767+
subPsubPQvvP.mult(nabla, subPsubPQvvPNabla);
774768

775-
// Bestimme Nabla auf der Grenzwertellipse mit nabla0*Pnn*nabla0 == 1
776-
Vector PQvvPnabla0 = new DenseVector(nabla0);
777-
PR.mult(nabla0, PQvvPnabla0);
778-
double nQn0 = nabla0.dot(PQvvPnabla0);
769+
// Bestimme Nabla auf der Grenzwertellipse mit nabla0*Pnn*nabla0 == 1
770+
Vector nabla0 = new DenseVector(nabla, true);
771+
Vector PQvvPnabla0 = new DenseVector(nabla0);
772+
PR.mult(nabla0, PQvvPnabla0);
773+
double nQn0 = nabla0.dot(PQvvPnabla0);
774+
if (normNabla < SQRT_EPS || nQn0 <= 0) {
775+
for (int j=0; j<dim; j++)
776+
nabla0.set(j, confidenceRegion.getMinimalDetectableBias(j));
777+
}
778+
else {
779779
for (int j=0; j<dim; j++)
780-
if (nQn0 > 0)
781-
nabla0.set(j, nabla0.get(j)/Math.sqrt(nQn0));
780+
nabla0.set(j, nabla0.get(j)/Math.sqrt(nQn0));
782781
}
783782
//deflectionX.setMinimalDetectableBias(nabla0.get(0));
784783
//deflectionY.setMinimalDetectableBias(nabla0.get(1));
@@ -963,33 +962,31 @@ public void addSubRedundanceAndCofactor2Point(Point point, boolean restoreUncert
963962
point.setInfluenceOnNetworkDistortion(efsp);
964963
}
965964
else {
966-
double efsp = 0;
965+
Qnn.mult(Pv, nabla);
966+
point.setNablaCoVarNabla( Math.abs(nabla.dot(Pv0)) );
967+
nabla = nabla.scale(-1.0);
968+
point.setGrossErrors( Matrices.getArray(nabla) );
969+
Qll_P.mult(nabla, ep);
970+
point.setInfluencesOnPointPosition( Matrices.getArray(ep) );
971+
972+
Vector subPsubPQvvPNabla = new DenseVector(dim);
973+
subPsubPQvvP.mult(nabla, subPsubPQvvPNabla);
974+
double efsp = Math.sqrt(Math.abs(subPsubPQvvPNabla.dot(nabla)));
975+
976+
// Bestimme Nabla auf der Grenzwertellipse mit nabla0*Pnn*nabla0 == 1
967977
Vector nabla0 = new DenseVector(nabla, true);
968-
double normNabla = nabla0.norm(Vector.Norm.Two);
969-
if (normNabla < SQRT_EPS) {
970-
for (int j=0; j<dim; j++)
971-
nabla0.set(j, confidenceRegion.getMinimalDetectableBias(j));
972-
}
973-
else {
974-
Qnn.mult(Pv, nabla);
975-
point.setNablaCoVarNabla( Math.abs(nabla.dot(Pv0)) );
976-
nabla = nabla.scale(-1.0);
977-
point.setGrossErrors( Matrices.getArray(nabla) );
978-
Qll_P.mult(nabla, ep);
979-
point.setInfluencesOnPointPosition( Matrices.getArray(ep) );
980-
981-
Vector subPsubPQvvPNabla = new DenseVector(dim);
982-
subPsubPQvvP.mult(nabla, subPsubPQvvPNabla);
983-
efsp = Math.sqrt(Math.abs(subPsubPQvvPNabla.dot(nabla)));
984-
985-
// Bestimme Nabla auf der Grenzwertellipse mit nabla0*Pnn*nabla0 == 1
986-
Vector PQvvPnabla0 = new DenseVector(nabla0);
987-
PR.mult(nabla0, PQvvPnabla0);
988-
double nQn0 = nabla0.dot(PQvvPnabla0);
989-
for (int j=0; j<dim; j++)
990-
if (nQn0 > 0)
991-
nabla0.set(j, nabla0.get(j)/Math.sqrt(nQn0));
992-
}
978+
Vector PQvvPnabla0 = new DenseVector(nabla0);
979+
PR.mult(nabla0, PQvvPnabla0);
980+
double nQn0 = nabla0.dot(PQvvPnabla0);
981+
double normNabla = nabla.norm(Vector.Norm.Two);
982+
if (normNabla < SQRT_EPS || nQn0 <= 0) {
983+
for (int j=0; j<dim; j++)
984+
nabla0.set(j, confidenceRegion.getMinimalDetectableBias(j));
985+
}
986+
else {
987+
for (int j=0; j<dim; j++)
988+
nabla0.set(j, nabla0.get(j)/Math.sqrt(nQn0));
989+
}
993990
//point.setMinimalDetectableBiases(Matrices.getArray(nabla0));
994991
point.setMaximumTolerableBiases( Matrices.getArray(nabla0) );
995992
point.setInfluenceOnNetworkDistortion(efsp);
@@ -1905,8 +1902,7 @@ else if (mxy >= 0) {
19051902
}
19061903
}
19071904
}
1908-
1909-
1905+
19101906
if (this.congruenceAnalysis && numberOfStrainEquations > 0) {
19111907
row = N.numRows() - numberOfStrainEquations;
19121908

@@ -3049,28 +3045,26 @@ else if (r==2 || dim == 1)
30493045
point.setMaximumTolerableBiases(Matrices.getArray(nabla0));
30503046
}
30513047
else {
3048+
Qnn.mult(BTPv, nabla);
3049+
double normNabla = nabla.norm(Vector.Norm.Two);
3050+
point.setNablaCoVarNabla( Math.abs(nabla.dot(BTPv)) );
3051+
point.setGrossErrors( Matrices.getArray(nabla.scale(-1.0)) );
3052+
3053+
// Bestimme Nabla auf der Grenzwertellipse mit nabla0*Pnn*nabla0 == 1
30523054
Vector nabla0 = new DenseVector(nabla, true);
3053-
double normNabla = nabla0.norm(Vector.Norm.Two);
3054-
if (normNabla < SQRT_EPS) {
3055+
Vector PQvvPnabla0 = new DenseVector(nabla0);
3056+
BTPQvvPB.mult(nabla0, PQvvPnabla0);
3057+
double nQn0 = nabla0.dot(PQvvPnabla0);
3058+
if (normNabla < SQRT_EPS || nQn0 <= 0) {
30553059
for (int j=0; j<dim; j++)
30563060
nabla0.set(j, confidenceRegion.getMinimalDetectableBias(j));
30573061
}
30583062
else {
3059-
Qnn.mult(BTPv, nabla);
3060-
point.setNablaCoVarNabla( Math.abs(nabla.dot(BTPv)) );
3061-
point.setGrossErrors( Matrices.getArray(nabla.scale(-1.0)) );
3062-
3063-
// Bestimme Nabla auf der Grenzwertellipse mit nabla0*Pnn*nabla0 == 1
3064-
Vector PQvvPnabla0 = new DenseVector(nabla0);
3065-
BTPQvvPB.mult(nabla0, PQvvPnabla0);
3066-
double nQn0 = nabla0.dot(PQvvPnabla0);
3067-
if (nQn0 > 0) {
3068-
for (int j=0; j<dim; j++)
3069-
nabla0.set(j, nabla0.get(j)/Math.sqrt(nQn0));
3070-
//point.setMinimalDetectableBiases(Matrices.getArray(nabla0));
3071-
point.setMaximumTolerableBiases(Matrices.getArray(nabla0));
3072-
}
3063+
for (int j=0; j<dim; j++)
3064+
nabla0.set(j, nabla0.get(j)/Math.sqrt(nQn0));
30733065
}
3066+
//point.setMinimalDetectableBiases(Matrices.getArray(nabla0));
3067+
point.setMaximumTolerableBiases(Matrices.getArray(nabla0));
30743068
}
30753069
}
30763070

@@ -3541,7 +3535,7 @@ else if (colP1 < 0) {
35413535
}
35423536

35433537
double nabla[] = new double[dim];
3544-
double grzw[] = new double[dim];
3538+
double mdb[] = new double[dim];
35453539
double normNabla = 0;
35463540
if (dim != 1) {
35473541
nabla[0] = p0.getX() - p1.getX();
@@ -3580,9 +3574,9 @@ else if (colP1 < 0) {
35803574
Vector PxxNabla0 = new DenseVector(nabla0, true);
35813575
tiePxx.mult(nabla0, PxxNabla0);
35823576
double nPn0 = nabla0.dot(PxxNabla0);
3583-
for (int j=0; j<dim; j++) {
3584-
if (nPn0 > 0)
3585-
grzw[j] = sqrtLambda * nabla0.get(j)/Math.sqrt(nPn0);
3577+
if (nPn0 > 0) {
3578+
for (int j=0; j<dim; j++)
3579+
mdb[j] = sqrtLambda * nabla0.get(j)/Math.sqrt(nPn0);
35863580
}
35873581
tie.setTeststatisticValues(tPrio, tPost);
35883582
}
@@ -3598,9 +3592,9 @@ else if (colP1 < 0) {
35983592
tie.setConfidenceRegion(confidenceRegion);
35993593
if (normNabla < SQRT_EPS) { // this.estimationType == EstimationType.SIMULATION) {
36003594
for (int i=0; i<dim; i++) {
3601-
grzw[i] = sqrtLambda*confidenceRegion.getMinimalDetectableBias(i);
3595+
mdb[i] = sqrtLambda*confidenceRegion.getMinimalDetectableBias(i);
36023596
if (this.estimationType != EstimationType.SIMULATION)
3603-
nabla[i] = grzw[i];
3597+
nabla[i] = mdb[i];
36043598
}
36053599
}
36063600
}
@@ -3609,7 +3603,7 @@ else if (colP1 < 0) {
36093603
e.printStackTrace();
36103604
}
36113605

3612-
tie.setMinimalDetectableBiases(grzw);
3606+
tie.setMinimalDetectableBiases(mdb);
36133607
tie.setGrossErrors(nabla);
36143608
tie.setSigma(sigma);
36153609

@@ -3787,10 +3781,10 @@ else if (r==2 || dim == 1)
37873781
Vector PQvvPnabla0 = new DenseVector(nabla0);
37883782
BTPQvvPB.mult(nabla0, PQvvPnabla0);
37893783
double nQn0 = nabla0.dot(PQvvPnabla0);
3790-
for (int j=0; j<dim; j++)
3791-
if (nQn0 > 0)
3784+
if (nQn0 > 0) {
3785+
for (int j=0; j<dim; j++)
37923786
nabla0.set(j, sqrtLambdaTie * nabla0.get(j)/Math.sqrt(nQn0));
3793-
3787+
}
37943788
tie.setMinimalDetectableBiases(Matrices.getArray(nabla0));
37953789
tie.setGrossErrors(Matrices.getArray(nabla));
37963790

0 commit comments

Comments
 (0)