Application of Bernoulli filter in bearings-only tracking scenarios

Symbol	Description
	The newborn probability of the target
	The probability density function of the newborn target
	The existence probability of the target (not related to the target state in this paper)
	The Markov transition density of the target
	The priori probability of existence
	The priori probability density function
	The probability density function of the clutter
	The current measurement set
	The target detection probability
	The measurement likelihood function

Algorithm 1. PF implementation of the bearings-only tracking Bernoulli filter
(1) Input: The priori probability of existence , particle set , measurement set
(2) Calculate the predicted probability of existence by (3).
(3) Draw a sample for the particle set by (16), and calculate the predicted weights by (17)
(4) Compute the likelihood ratio using (18)
(5) Compute the posterior probability density function using (19)
(6) Compute the posterior weights using (20) and normalize the weights
(7) Resample and target state estimation
(8) Output: The posterior probability of existence , particle set , target state

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Guoping Shi, Yece Qian. Application of Bernoulli filter in bearings-only tracking scenarios[J]. Infrared and Laser Engineering, 2021, 50(2): 20200343

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