Multiple model efficient particle filter based track-before-detect for maneuvering weak targets

Abstract

It is a tough problem to jointly detect and track a weak target, and it becomes even more challenging when the target is maneuvering. The above problem is formulated by using the Bayesian theory and a multiple model (MM) based filter is proposed. The filter presented uses the MM method to accommodate the multiple motions that a maneuvering target may travel under by adding a random variable representing the motion model to the target state. To strengthen the efficiency performance of the filter, the target existence variable is separated from the target state and the existence probability is calculated in a more efficient way. To examine the performance of the MM based approach, a typical track-before-detect (TBD) scenario with a maneuvering target is used for simulations. The simulation results indicate that the MM based filter proposed has a good performance in joint detecting and tracking of a weak and maneuvering target, and it is more efficient than the general MM method.

A Novel 4D Track-before-Detect Approach for Weak Targets Detection in Clutter Regions

“…In S c j and S T j , the distributions of false alarm points in each cell generated by background (26) and clutter regions (27) are the same, as the variation of clutter is quite slow compared with that of the target cell. Therefore, after the subtraction of S c j and S T j (15), only the components of target (25) are reserved in S j , and it is much larger than the residual error of subtraction in (15). Therefore, target detection in S j is quite easy, and the subtraction in each detection bin is the theoretical basis of our improvements.…”

“…Therefore, extended target tracking using a TBD method strategy is a computationally complex task. In the issue of weak trajectory detection, various TBD methods have been developed such as DP-TBD [9][10][11], PF-TBD [12][13][14][15][16], HT-TBD [2,[17][18][19], and some optimization based TBD [20]. In 3D target tracking scenarios, the computation of the TBD methods [9,10,[12][13][14]17,18,20] will sharply increase for a satisfying tracking performance.…”

Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform

“…Therefore, TBD indeed has the advantages of providing a higher probability of detection at the same level of probability of false alarms, while circumventing the troublesome data association problem. TBD has a large family, and many algorithms can fall into this category, such as the Hough transform (HT) [15][16][17][18], maximum likelihood approach (MLA) [19,20], sequential hypothesis testing [21,22], maximum likelihood-probabilistic data association (ML-PDA) [3,4], [23,24], particle filter track-beforedetect (PF-TBD) [25][26][27][28][29], 3D matched filtering [30,31], 4D-TBD [32], dynamic programming (DP) [33][34][35], etc. During the past several decades, many extensive studies of TBD have been published in the available literature.…”

IMM Robust Cardinality Balance Multi‐Bernoulli Filter for Multiple Maneuvering Target Tracking with Interval Measurement

“…[23] based on the IMM algorithm and GLF. Unfortunately, it is also necessary to assume in advance that the target detection probability and clutter rate are known in advance [ 19‐23 ] . In order to solve the problems of interval measurement and maneuvering target tracking in the case of unknown target detection probability and unknown clutter density, a novel Interacting multi‐model R‐CBMeMBer (IMM R‐CBMeMBer) filter with interval measurement is proposed.…”

“…In general, IMM algorithm is embedded into MTT algorithms, so that the traditional MTT algorithm is able to track maneuvering targets, such as the methods in Refs. [19][20][21][22]. Generalized likelihood function [18] (GLF) is a reliable method to solve the problem of interval measurement.…”