An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking

Abstract

In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover, a self-adaptive gain modification coefficient is defined to solve the low accuracy problem caused by the sigma point reduction, and the problem of particle degradation is solved by modifying the weights calculation method. In addition, a new multi-sensor fusion model is proposed, which better integrates radar and infrared sensors. Simulation results show that IUPF effectively improves real-time performance while ensuring the tracking accuracy compared with other algorithms. Besides, compared with the traditional distributed fusion architecture, the proposed new architecture makes better use of the advantages of radar and an infrared sensor and improves the tracking accuracy.

Sensor fusion with high‐order moments constraints using projection‐based neural network

“…We proceed to define the dual cost function of Equation (17) with constraint Equations ( 17)-( 20) by employing the Lagrangian multipliers as max…”

“…Besides, the Kalman filter-based sensor fusion has been extended for non-Gaussian applications [15,16]. Particle filter-based sensor fusion was also proposed to deal with non-Gaussian sensors' data [17]. These methods are optimal in the sense of the fusion PDF.…”

Target‐tracking algorithm based on improved probabilistic data association

“…In the actual calculation, the weights are obtained by finding out the combined set of all possible point traces and track, and calculating the probability of point traces and track-associated set. The difference between JPDA and PDA is that the calculation of probability of probability interconnection is different, so the complexity and calculation amount of the JPDA algorithm are higher than the PDA algorithm [15]. PDA and JPDA are similar in process which is divided into two parts: joint event generation and association probability calculation.…”

A New Pseudolinear Filter for Bearings-Only Tracking without Requirement of Bias Compensation

“…Target tracking has been researched for decades with a wide range of applications in civilian and military areas. It refers to estimate a moving target’s state using the noise-corrupted measurements collected by one or more sensors at fixed locations or on moving platforms [ 1 , 2 , 3 , 4 , 5 ]. The typical measurements include target range, Doppler velocity and bearing angles, while in passive bearings-only tracking (BOT) systems [ 6 , 7 , 8 , 9 , 10 , 11 ], the sensors listen for signals emitted by a target and only acquire the bearing data.…”