A reduced-complexity scheme using message passing for location tracking

Abstract

This article presents a low-complexity and high-accuracy algorithm using message-passing approach to reduce the computational load of the traditional tracking algorithm for location estimation. In the proposed tracking scheme, a state space model for the location-estimation problem can be divided into many mutual-interaction local constraints based on the inherent message-passing features of factor graphs. During each iteration cycle, the message with reliability information is passed efficiently with an adaptive weighted technique and the error propagation law, and then the message-passing approach based on prediction-correction recursion is to simplify the implementation of the Bayesian filtering approach for location-estimation and tracking systems. As compared with a traditional tracking scheme based on Kalman filtering (KF) algorithms derived from Bayesian dynamic model, the analytic result and the numerical simulations show that the proposed forward and one-step backward tracking approach not only can achieve an accurate location very close to the traditional KF tracking scheme, but also has a lower computational complexity.

Location-Aware Mobile Crime Information Framework for Fast Tracking Response to Accidents and Crimes in Big Cities

“…During each iteration cycle, the message with reliable information is passed efficiently with an adaptive weighted technique and the error propagation law, and then the message-passing approach based on prediction-correction recursion is to simplify the implementation of the Bayesian filtering approach for location-estimation and tracking systems [4].…”

Reduced‐complexity tracking scheme based on adaptive weighting for location estimation

“…Fig. 1 shows the undirected graphical model for the location tracking scheme whose unnormalised joint distribution is given by (17). It also shows the flow of messages applied to the concept of the FG in [24].…”

“…For location-estimation techniques using tracking approaches, the Kalman filtering (KF) algorithm is considered optimal for the linear Gaussian model, and it has been introduced to enhance the estimation accuracy of the location-estimation system [8][9][10][11][12][13][14][15][16]. Nevertheless, the high location accuracy in terms of the conventional KF coupling algorithm requires high computational complexity, and direct implementation of the KF equations may be too complex to be applied to practical systems [17]. To improve location-estimation efficiency, it would be useful to develop an algorithm with high location accuracy or low computational complexity.…”

Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation

“…However, software implementations often result in speed delay and cannot provide in-time positioning information for LBS applications [17][18][19][20]. In order to improve the computational speed of positioning systems and accuracy, there already are lots of improved algorithms in literature [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. The calculations of the KF approach are involved in the number of matrix computations, which include addition, multiplication, and inverse operations.…”

“…The calculations of the KF approach are involved in the number of matrix computations, which include addition, multiplication, and inverse operations. The approach is difficult to implement hardware in traditional devices with some practical systems, such as high performances in real-time, flexible and convenient implementation [10,[17][18][19][20][30][31][32]. In addition, the processors of Digital Signal Processing (DSP) adopt sequential program execution.…”