Hick's probabilistic data association method

Claim 1

A method for improving the performance of a radar tracking system which includes means for acquiring measurements of moving targets in the presence of noise, said method comprising the following steps: generating a predicted state for each of a plurality of crossing targets; acquiring actual measurements of said targets along with noise; using actual measurements and predicted states of said targets, to calculate and store the Probabilistic Data Association (PDA) probabilities; sorting the PDA probabilities in a matching matrix; forming a hypothesis for each target using all possible assumptions as to which measurement represent said targets; calculating the probabilistic score for each of the hypotheses by calculating the product of the PDA probabilities; and selecting the probability with the highest product.

Claim 2

A method for improving the performance of a radar tracking system which includes means for acquiring measurements of moving targets in the presence of noise, said method comprising the following steps: generating a predicted state for each of a plurality of crossing targets; acquiring actual measurements of said targets along with noise; using actual measurements and predicted states of said targets, to calculate and store the Probabilistic Data Association (PDA) probabilities; sorting the PDA probabilities in a matching matrix; forming a hypothesis for each target using all possible assumptions as to which measurement represent said targets; calculating the probabilistic score for each of the hypotheses by calculating the product of the PDA probabilities; and selecting the probability with the highest product.

Claim 3

A method for improving the performance of a radar tracking system which includes means for acquiring measurements of moving targets in the presence of noise, said method comprising the following steps: generating a predicted state for each of a plurality of crossing targets; acquiring actual measurements of said targets along with noise; comparing the predicted states of said targets with said actual measurements to calculate and store the Probabilistic Data Association (PDA) probabilities; sorting the PDA probabilities in a matching matrix; forming a hypothesis for each target using all possible assumptions as to which measurement represent said targets; calculating the probabilistic score for each of the hypotheses by calculating the product of the PDA probabilities; and selecting the probability with the highest product.

Claim 4

A method for improving the performance of a radar tracking system which includes means for acquiring measurements of moving targets in the presence of noise, said method comprising the following steps: generating a predicted state for each of a plurality of crossing targets; acquiring actual measurements of said targets along with noise; comparing the predicted states of said targets with said actual measurements to calculate and store the Probabilistic Data Association (PDA) probabilities; sorting the PDA probabilities in a matching matrix; forming a hypothesis for each target using all possible assumptions as to which measurement represent said targets; calculating the probabilistic score for each of the hypotheses by calculating the product of the PDA probabilities; and selecting the probability with the highest product.

Claim 5

A method for improving the performance of a radar tracking system which includes means for acquiring measurements of moving targets in the presence of noise, said method comprising the following steps: Step 1: Calculating and storing PDA probabilities in a matrix in the following manner: ##EQU6## where: t=number of targets m=number of measurements, and m=0 represents the predicted state B ij =PDA association probability for measurement i to target j; Step 2: Sorting and storing probabilities in the following manner: ##EQU7## where the probabilities are sorted such that: B.sub.0k B.sub.1k >. . . B.sub.mk Step 3: Storing measurement indices for each of the probabilities in a matching matrix as follows: ##EQU8## where I ij is the measurement index k corresponding to B k1 ; Step 4: Forming a set of hypotheses through the following procedure: ##STR2## Step 5: Computing a score for each hypothesis by finding the product of each hypothesis' measurement probabilities; i.e.: if the hypotheses matrices are represented by: ##EQU9## Step 6: Finding the highest score and set k to that score's index value such that S k corresponds to the highest score; Step 7. Forming and association matrix, as follows: ##EQU10## Step 8: Returning the association to the tracking program to be used to update the position and velocity state of each target.

Claim 6

A method for improving the performance of a radar tracking system which includes means for acquiring measurements of moving targets in the presence of noise, said method comprising the following steps: Step 1: Calculating and storing PDA probabilities in a matrix in the following manner: ##EQU6## where: t=number of targets m=number of measurements, and m=0 represents the predicted state B ij =PDA association probability for measurement i to target j; Step 2: Sorting and storing probabilities in the following manner: ##EQU7## where the probabilities are sorted such that: B.sub.0k B.sub.1k >. . . B.sub.mk Step 3: Storing measurement indices for each of the probabilities in a matching matrix as follows: ##EQU8## where I ij is the measurement index k corresponding to B k1 ; Step 4: Forming a set of hypotheses through the following procedure: ##STR2## Step 5: Computing a score for each hypothesis by finding the product of each hypothesis' measurement probabilities; i.e.: if the hypotheses matrices are represented by: ##EQU9## Step 6: Finding the highest score and set k to that score's index value such that S k corresponds to the highest score; Step 7. Forming and association matrix, as follows: ##EQU10## Step 8: Returning the association to the tracking program to be used to update the position and velocity state of each target.