Using example x99, with 2 parabolic tracks, a varying number of missed
target events, a varying number of false detections (Poisson distribution),
a penalty for unused measurements, plus apriori probabilities in the cost
function (P(missed target)=0.1, P(false detection)=Poisson with rate 1),
w(K) is the total cost of the best paths for K=1..4 (using Kn=0.01, S2a=2, L=4):

  w =
       91.848       40.683       85.527       126.94

The minimum cost occurs for K=2, which agrees with the actual input
data used for the simulation (2 tracks).

Examining the best paths for K=4:

[combos(:,path_index(:,1))', n1', n2', n_false_detect']

     1     2     3     4     1     1     2
     1     2     6     6     1     1     1
     1     2     6     6     1     1     1
     1     2     6     6     1     1     2
     1     2     6     6     1     1     0
     6     6     6     6     0     1     1
     1     2     6     6     1     1     3
     1     2     6     6     1     1     0
     1     2     6     6     1     1     2
     1     2     6     6     1     1     1
     1     2     6     6     1     1     0
     1     2     6     6     1     1     0
     1     2     3     6     1     1     1
     1     2     6     6     1     1     0
     1     2     6     6     1     1     0
     1     2     6     6     1     1     0
     1     2     6     6     1     1     1
     1     2     6     6     1     1     0
     1     2     6     6     1     1     0
     1     2     6     6     1     1     1
     1     2     4     6     1     1     2


The last column of the above table shows the number of false detections
generated per unit time.  The n1 and n2 columns show the number of
measurements used for the true tracks.  These were generated randomly,
with P(missed target)=0.1 and in this case only sample #6 for target #1
was generated as a missed detection.

The first four columns show the measurement indices used in the four tracks
as determined by the K-path L-best Viterbi algorithm.  In these columns,
the number 6 represents a missed target event detection.  Numbers 1 to 5
represent selections of the measurements, with 1 and 2 corresponding to
real target measurements (except for row 6 where target #1 had no
measurement) and 3 to 5 corresponding to false detections.  Row 1 is for
time=1, row 2 for time=2, etc.