With the emergence of the Internet of Things (IoT), indoor positioning services have garnered increasing attention. When it comes to target tracking, the uncertainty surrounding velocity poses a challenge in obtaining the initial state, and the setting of this initial state significantly impacts early tracking performance. To address this issue, this paper proposes an extended Kalman filter (EKF) algorithm based on a dual model, aiming to mitigate the influence of this problem. Although the tracking performance of the without velocity constrained (WVC)-EKF model falls short compared to that of the velocity constraint (VC)-EKF model, the WVC-EKF model eliminates the need to grapple with the difficulty of setting the initial velocity. Consequently, the tracking process benefits from the combined strengths of both models. Firstly, the early tracking is accomplished using the WVC-EKF model. Subsequently, the WVC-EKF model serves as an auxiliary component to assist the VC-EKF model in establishing the initial state. Finally, the VC-EKF model takes charge of the subsequent tracking. In this paper, ray-tracing software was employed for simulation purposes, and the obtained results demonstrate that the proposed algorithm enhances tracking accuracy.

We study the average Age of Information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process through two independent channels. Although the double queue parallel transmission is instrumental in reducing AoI, the out of order of data arrivals also imposes a significant challenge to the performance analysis. We consider two settings: the M-M system where the service time of two servers is exponentially distributed; the M-D system in which the service time of one server is exponentially distributed and that of the other is deterministic. For the two dual-queue systems, closed-form expressions of average AoI and PAoI are derived by resorting to the graphic method and state flow graph analysis method. Our analysis reveals that when the two servers have the same service rate, compared with the single-queue system with an exponentially distributed service time, the average PAoI and the average AoI of the M-M system decrease by 33.3% and 37.5%, respectively, and those of the M-D system decrease by 27.7% and 39.7%, respectively. Numerical results show that the two dual-queue systems also outperform the M/M/2 single queue dual-server system with optimized arrival rate in terms of average AoI and PAoI.

We study the average age of information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process. We capture the state transition characteristics of the considered system by establishing a Markov chain. Using the state flow graph analysis method, we derive closed-form expressions of the average peak age of information (PAoI) and the average age of information (AoI) for the dual-queue update system. The numerical results show that compared with the single-queue update system, the average PAoI of the dual-queue update system is reduced by 33.5% and the average AoI dropped by 37.5%.

This work investigates a remote status updating system where the transmission process is modeled as a multi-stream M/G/1/1 non-preemptive system. We derive the closed-form expression of the average AoI of each stream in a heterogeneous case, where the distributions of service time are different for streams. To obtain more insights, we apply the results in a homogeneous system, where the service time distributions are identical, and find that preemption of packets would not always lead to the reduction of AoI, especially when the variance coefficient of the service time is small. We further optimize the generation rate to minimize the sum of average AoI. The results in heterogeneous cases show that given the same average service time for all streams, a higher generation rate should be allocated to the stream with a small service time variance. For the homogeneous cases with different AoI urgency weights for each stream, a higher generation rate should be reserved for the stream with more urgent AoI requirements for timeliness improvement. Besides, a lower bound on sum of average AoI is also provided, which only depends on the service rate and the number of data streams in homogeneous systems. Numerical results validate our theoretical analysis.

The age of information (AoI) becomes a fashion and effective measurement for evaluating the timeliness and freshness of state updates in Internet of Things (IoT). The majority of existing works provide abundant insights for optimizing age through packet management. In this paper, the average AoI of a remote data transmission system in which the transmission process is modeled as a multiple stream M/M/1/1 non-preemptive queue process is considered. We first derive the exact theoretical expression of the average AoI of multiple stream M/M/1/1 non-preemptive queue and then extend this result to more general M/G/1/1 queues. Results suggest that the M/G/1/1 non-preemptive queue strategy can effectively improve the system performance. The comparison of preemption strategy and non-preemption strategy under different service processes shows that preemption of packets does not always lead to reduction of AoI, especially for the system with small coefficient of variance of service time. Moreover, it is found that given the same average service time, the M/G/1/1 queue with small coefficient of variance of service time performs better.