Description: Implementation of the Murty algorithm to obtain the best K assignments. Includes the implementation of the Jonker-Volgenant algorithm.
Usual applications are multiple target tracking algorithms, Joint Probabilistic Data Association (JPDA), Multiple Hypothesis Tracking (MHT), Global Nearest Neighbours (GNN), etc.
Based on the original code by Jonker-Volgenant, and the Murty Matlab implementation by Eric Trautmann
Can be used both from Java or Matlab.
Time for a 100x100 assignment problem, with k=20:
• Matlab implementation: 54.5 sec
• Java implementation: 1 sec
To Search:
File list (Check if you may need any files):
java-k-best-1.00\src\com\google\code\javakbest\Murty.java
................\...\...\......\....\.........\JVC.java
................\...\...\......\....\.........\PartitionNode.java
................\...\...\......\....\.........\Test.java
................\...\...\......\....\.........\Node.java
................\README.TXT
................\java-k-best.jar
................\findkbest.m
................\LICENSE
................\src\com\google\code\javakbest
................\...\...\......\code
................\...\...\google
................\...\com
................\src
java-k-best-1.00
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