In this paper, bias and CRB analysis for both proposed line-of-sight (LoS) and conventional radio-signal-strength (RSS) based positioning methods are derived. In contrast to the conventional lognormal based analysis, the derived error models take into account the environmental multipath fading effect. The proposed LoS-based scheme improves positioning accuracy by eliminating the effect of non-range dependent portion of powers from the received signal. It suppresses non-line-of-sight (NLoS) disturbance to the positioning result through estimating the LoS component from fading signal. Hence, it is robust to environmental fading disturbance. The derived error models are validated by simulation. Both analytical model and simulation results show that the accuracy and robustness of the proposed LoS-based positioning are superior to that of the conventional RSS-based methods.
Traditional power-based localization methods suffer from low accuracy in the practical application environment. The main challenges are the antenna directivity and fading effect. Conventional methods assume omnidirectional antenna directivity such that the solution is the intersections of multiple circle-shape contours. This strong assumption results in significant localization error in practical non-isotropic antenna applications. In this article, a novel antenna radiation-pattern-aware power-based positioning (ARPAP) scheme is proposed. It reduces the antenna directivity effect by including the antenna pattern into the localization system model. It reduces the bias error that introduced by power measurement through estimating the line-of-sight (LoS) component in received signal strength (RSS). Moreover, the error mode for the proposed ARPAP system, along with the theoretical limit, Cramer-Rao Bound (CRB), and bias of the proposed positioning system are derived. The Pearson correlation coefficient between the proposed error model and simulation result shows a high similarity score. The proposed positioning scheme and analytic error model are instantiated for the cellular network. Both analytical model and simulation results demonstrate the superiority of the proposed method over traditional methods.
Cyber–physical systems (CPSs) consists of a network, computation, and physical process. Embedded networks, which deliver control and sensing signal, can potentially affect CPSs performance. However, the degradation of physical system performance caused by the embedded networks is frequently oversimplified with strong assumptions. The proposed scheme effectively relaxes those assumptions in the existing works that network delays are bounded in a specific range or its distribution is time invariant. Most of the existing works on fault diagnosis and prognosis addressed the physical system fault detection and isolation, and ignore cyber network faults. A novel cyber network fault prognosis scheme is proposed to deal with both of cyber and physical system fault. It can identify when a cyber fault has occurred, and pinpoint the type of fault based on CPS system performance prediction, then, trigger resilience controller at an appropriate time to minimise the computational overhead. Thus, it can guarantee the stability of the entire CPS and substantially reduce computational overhead of the resilience control by triggering it if necessary.
In this paper, a cost efficient fusion scheme, Ubiquitous Tracking with Motion and Location Sensor (UTMLS), is proposed for the accurate localization and tracking in mixed GPS-friendly, GPS-challenging, and GPS-denied scenario. The proposed drift-reduction method in UTMLS addresses the cumulating error issue in the indoor tracking with the consumer grade motion sensor. The proposed hypothesis test method in UTMLS improves the tracking sensor fusion precision by detecting distorted GPS reports and intelligently switching between GPS and inertial sensor based schemes. The proposed scheme is instantiated and implemented on an Android smartphone platform. Experiments have been conducted to evaluate and validate the accuracy. Experimental results show that (1) the proposed drift-reduction method effectively suppresses the non-GPS tracking error accumulation due to the integration of acceleration noise with time (2) UTMLS realizes robust indoor/outdoor seamless tracking, preventing GPS fault estimates introduced tracking error in the conventional Kalman filtering process.
In this paper, bias and Cramer-Rao bound (CRB) analyses for both proposed line of sight (LoS)-based and conventional received signal strength (RSS)-based ranging methods are provided. A minimum bias of LoS-based ranging is derived, which is based on the asymptotic variance (AsV) of the estimator. Mean square error lower bound (MSEB), which is the summation of CRB and the square of minimum bias, is employed to compare with existing RSS-based methods. The existing RSS-based ranging methods are categorized, and corresponding biases are analyzed. The multipath fading effect to the final range estimation has been modeled into the MSEB. The proposed LoS-based ranging method minimizes the effect of non-LoS (NLoS) component (i.e., the multipath fading) in the received signal. The derived model was validated by simulation. Both the analytic model and simulation results show that the performance of the proposed LoS method is superior to that of the conventional RSS-based methods.
This paper presents a novel RSSI based localization scheme that employs an existing cellular network infrastructure to perform trilateration. Traditional localization schemes employ RSSI-based radial distance estimation and trilateration algorithm. However, in a realistic scenario, the RSSI measurements are distorted due to multipath fading thus introducing error in radial distance estimation. Moreover, the selection of the best three anchor cell towers with the lowest localization error is not been explored. The proposed scheme improves localization accuracy using two, novel correcting and evaluating metrics: the Radial Distance Error Indicator (RDEI) and the Localization Error Indicator (LEI). The proposed RDEI metric is derived from the mean square error (MSE) of radial distance estimation in multipath fading channel. In the proposed method, it is employed as a correcting factor for the radial distance estimation. Next, the LEI estimate the combined localization error based on the towers positions, corresponding radial distance estimation and its error. The final position estimation improves localization accuracy as demonstrated through analytical and experimental results.