Abstract: The sensor whose information only contains angle information is called bearings-only sensor, and the target tracking based on bearings-only sensor is called bearings-only tracking (BOT). BOT is an important topic in the field of target tracking and will play an important role in passive target tracking surveillance. Bernoulli filter (BF) is the best single target filter within the Bayesian framework. It can obtain the existence probability of the target and the complete posterior probability density function, and judge the appearance and disappearance of the target. The Bernoulli filter was applied to single target tracking in the bearings-only tracking surveillance, and a bearings-only tracking Bernoulli filter was proposed. In the proposed filter, the angle of the target relative to the sensor and its change rate were used as the state vectors to estimate the existence of the target as well as the target state. At the same time, the particle filter (PF) implementation was proposed, too. The simulation results show that, compared with the ordinary Bernoulli filter, the proposed bearings-only tracking Bernoulli filter can judge the existence of the target better, and the error of the target estimation generated by the filter is smaller. Thus, the proposed filter has better tracking performance and higher tracking accuracy, which can be effectively applied to the passive tracking scenarios.