Modified Particle Filtering Algorithm for Single Acoustic Vector Sensor DOA Tracking
نویسندگان
چکیده
The conventional direction of arrival (DOA) estimation algorithm with static sources assumption usually estimates the source angles of two adjacent moments independently and the correlation of the moments is not considered. In this article, we focus on the DOA estimation of moving sources and a modified particle filtering (MPF) algorithm is proposed with state space model of single acoustic vector sensor. Although the particle filtering (PF) algorithm has been introduced for acoustic vector sensor applications, it is not suitable for the case that one dimension angle of source is estimated with large deviation, the two dimension angles (pitch angle and azimuth angle) cannot be simultaneously employed to update the state through resampling processing of PF algorithm. To solve the problems mentioned above, the MPF algorithm is proposed in which the state estimation of previous moment is introduced to the particle sampling of present moment to improve the importance function. Moreover, the independent relationship of pitch angle and azimuth angle is considered and the two dimension angles are sampled and evaluated, respectively. Then, the MUSIC spectrum function is used as the "likehood" function of the MPF algorithm, and the modified PF-MUSIC (MPF-MUSIC) algorithm is proposed to improve the root mean square error (RMSE) and the probability of convergence. The theoretical analysis and the simulation results validate the effectiveness and feasibility of the two proposed algorithms.
منابع مشابه
Three Dimensional Localization of an Unknown Target Using Two Heterogeneous Sensors
Heterogeneous wireless sensor networks consist of some different types of sensor nodes deployed in a particular area. Different sensor types can measure different quantity of a source and using the combination of different measurement techniques, the minimum number of necessary sensors is reduced in localization problems. In this paper, we focus on the single source localization in a heterogene...
متن کاملParticle Filtering in High Clutter Environment
This paper addresses the effect of clutter in direction of arrival (DOA) tracking from a passive acoustic sensor station. DOA signal is characterized by non-linearities arising from the measurement model. Strong dynamics combined with signal deterioration such as clutter can cause filters to diverge. A family of Monte Carlo methods known as particle filters has been used in variety of problems ...
متن کاملA distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network
متن کامل
Vector Sensor Arrays in Underwater Acoustic Applications
Traditionally, ocean acoustic signals have been acquired using hydrophones, which measure the pressure field and are typically omnidirectional. A vector sensor measures both the acoustic pressure and the three components of particle velocity. Assembled into an array, a vector sensor array (VSA) improves spatial filtering capabilities when compared with arrays of same length and same number of h...
متن کاملPareto design of fuzzy tracking control based on the particle swarm optimization algorithm for a walking robot in the lateral plane on slope
Many researchers have controlled and analyzed biped robots that walk in the sagittal plane. Nevertheless, walking robots require the capability to walk merely laterally, when they are faced with the obstacles such as a wall. In walking robot field, both nonlinearity of the dynamic equations and also having a tracking system cause an effective control has to be utilized to address these problems...
متن کامل