An Asynchronous Data Fusion Algorithm for Target Detection Based on Multi-Sensor Networks

Abstract

The time interval of the observational data changes irregularly because of the difference of sensors' sampling rate, the communication delay and the target leaving observation region of the sensor sometimes. These problems of asynchronous observation data greatly reduce the tracking accuracy of the multi-sensors system. Therefore, asynchronous data fusion system is more practical than synchronous data fusion system, and worthier of study. By establishing an asynchronous track fusion model with irregular time interval of observation data and combining with the Track Quality with Multiple Model (TQMM), an asynchronous track fusion algorithm with information feedback is proposed, and the TQMM is used for weight allocation to improve the performance of the asynchronous multi-sensor fusion system. The simulation result shows that the algorithm has better tracking performance compared with other algorithms, so that this kind of problem of track-to-track fusion for asynchronous sensors is solved effectively. INDEX TERMS Asynchronous fusion, multi sensors, track fusion, track quality with multiple model.

Multi-scale asynchronous fusion algorithm for multi-sensor integrated navigation system

“…Many researchers have deeply studied the information fusion method of multi-sensor integrated navigation system. [1][2][3] The research works are focused on synchronous fusion which considers that the data output rates of each auxiliary navigation sensor are same [4][5][6] and are transmitted synchronously to the fusion center. 7,8 However, due to the performance of auxiliary navigation sensors and other conditions in actual situation, the auxiliary navigation sensors may have different data output rates, which will make the multi-sensor information asynchronously arrive in the fusion center.…”

Asynchronous Multi-Sensor State Estimation for Systems Subject to Multiplicative and Cross-Correlated Noises With Measurement Packet Dropping

“…In association with the wide utilization of multi-sensor configuration like wireless sensor networks, the corresponding filteirng/estiamtion/fusion issues have gained increasingly research attention within a multitudes of academic societies such as signal processing and integrated navigation [11] [12]. It should be pointed out that one of the primary challenges resulting from the application of multiple sensors in a system is the phenomenon of asynchronous data due to the different sampling rate of different sensors [13] [14]. The asynchronous data cannot be directly used to estimate the system state by simply employing the existing approaches, which brings substantial difficulties for the state estimation problem for systems with multiple sensors.…”

“…in time interval [120s, 140s] and time interval[200s, 220s], respectively. The fixed vector[14,10] T is added to the measurements of sensors to describe the abrupt fault. The linear time-varying value is set to 0.5t to simulate ramp fault where t is duration of sensor failure.…”

An optimal strategy with various QoS requirement for data fusion for Internet of Things

“…Most of the previous researches focused on the methods of data fusion processing. 3,4 However, it should not be ignored that the guarantee of data fusion quality is based on the premise of real-time and reliable transmission of sensing data. At the same time, the scheduling problem of data fusion in IoT scenario is not an easy thing.…”

“…Data fusion involves two important aspects: first, data with different time and correlation characteristics are collected by sensor devices and transmitted to the fusion center, and the next step, data from different nodes are fused to support the application. Most of the previous researches focused on the methods of data fusion processing 3,4 . However, it should not be ignored that the guarantee of data fusion quality is based on the premise of real‐time and reliable transmission of sensing data.…”