Fault Detection And Exclusion Research Articles

Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning

<h3>Abstract</h3> This study proposes three novel integrity monitoring algorithms based on Bayesian Receiver Autonomous Integrity Monitoring (BRAIM). Two problems of integrity monitoring for land-based applications for GNSS challenging environments are explored: requirements for sufficient measurement redundancy and the presence of large biases. The need for measurement redundancy was mitigated by using BRAIM. This enabled the employment of a Fault Detection and Exclusion (FDE) algorithm without the required minimum availability of six measurements. To increase the estimated integrity, a Spatial Feature Constraint (SFC) algorithm was implemented to constrain solutions to feasible locations within a road feature. The performance of the proposed FDE+BRAIM, SFC+BRAIM and FDE+SFC+BRAIM algorithms was evaluated for GPS and multi-sensor data. For the non-Gaussian measurement error distribution and under the test conditions, the best achieved probability of misleading information was of the order of magnitude 10⁻⁸ for road-level requirements. The results provide an initial proof-of-concept for non-Gaussian non-linear multi-sensor integrity monitoring algorithms.

Residual based fault detection and exclusion methods applied to Ultra-Wideband navigation

Global Navigation Satellite System (GNSS) has become the main technology in terms of navigation technologies, as it ensures a worldwide absolute outdoor positioning. The transportation sector employs this technology to obtain a position, velocity and time solution for the corresponding outdoor application. When talking about indoor positioning, nevertheless, GNSS becomes an unreliable navigation technology, as the below-noise signals get obstructed. In these cases, the Ultra-Wideband (UWB) technology can be used as a navigation solution, as its anchor trilateration based radiofrequency positioning resembles GNSS’s principle and, depending on the anchor location, it can be used for indoor positioning. However, just like other radiofrequency based technologies, UWB is vulnerable to interferences and the multipath effect. With the aim of overcoming these drawbacks, this article discusses how to apply Fault Detection and Exclusion (FDE) techniques to avoid using faulty anchors when employing UWB in indoor/urban environments such as tunnels or train stations.

Enhanced fault detection and exclusion based on Kalman filter with colored measurement noise and application to RTK

With the development of high-precision safety–critical applications using global navigation satellite systems (GNSS), fault detection and exclusion (FDE) is indispensable to guaranteeing the integrity of a GNSS positioning and navigation system. Many FDE algorithms have been developed based on the standard Kalman filter (KF), assuming that GNSS measurements come with Gaussian uncorrelated white noise. The existence of colored noise in GNSS measurements, which is typical for positioning with low-cost receivers and in challenging environments will, however, degrade the performance of KF-based FDE algorithms. We proposed an FDE scheme based on improved KF considering colored noise (CKF) as a first-order autoregressive model to improve the FDE performance. The performance of the proposed CKF-based FDE algorithm was evaluated with an application to real-time kinematic positioning using a low-cost receiver. A CKF-based fault detection test, a fault identification test, a minimum detectable bias (MDB), error distribution, and positioning results were examined. The results showed that the CKF-based FDE can obtain realistic statistical information to improve integrity monitoring reliability. The fault detection test achieved a 17.83% improvement in FDE performance and a reduction in the false alarm rate, from 23.33 to 5.50%, compared with KF-based FDE. The tests also indicated that the CKF-based FDE can detect multiple faults with zero-miss detection. The fault identification test had an average improvement of 32.14%, and a more realistic MDB was obtained. The results of this study contribute to making objective decisions for the integrity monitoring of practical, precise GNSS positioning.

Identification of Multi-Faults in GNSS Signals using RSIVIA under Dual Constellation

This publication presents the development of integrity monitoring and fault detection and exclusion (FDE) of pseudorange measurements, which are used to aid a tightly-coupled navigation filter. This filter is based on an inertial measurement unit (IMU) and is aided by signals of global navigation satellite system (GNSS). Particularly, the GNSS signals include global positioning system (GPS) and Galileo. By using GNSS signals, navigation systems suffer from signal interferences resulting in large pseudorange errors. Further, a higher number of satellites with dual-constellation increases the possibility that satellite observations contain multiple faults. In order to ensure integrity and accuracy of the filter solution, it is crucial to provide sufficient fault-free GNSS measurements for the navigation filter. For this purpose, a new hybrid strategy is applied, combining conventional receiver autonomous integrity monitoring (RAIM) and innovative robust set inversion via interval analysis (RSIVIA). To further improve the performance, as well as the computational efficiency of the algorithm, the estimated velocity and its variance from the navigation filter is used to reduce the size of the RSIVIA initial box. The designed approach is evaluated with recorded data from an extensive real-world measurement campaign, which has been carried out in GATE Berchtesgaden, Germany. In GATE, up to six Galileo satellites in orbit can be simulated. Further, the signals of simulated Galileo satellites can be manipulated to provide faulty GNSS measurements, such that the fault detection and identification (FDI) capability can be validated. The results show that the designed approach is able to identify the generated faulty GNSS observables correctly and improve the accuracy of the navigation solution. Compared with traditional RSIVIA, the designed new approach provides a more timely fault identification and is more computational efficient.

Fast Multiple Fault Detection and Exclusion (FM-FDE) Algorithm for Standalone GNSS Receivers

Numerous applications and devices use Global Navigation Satellite System (GNSS)-provided position, velocity and time (PVT) information. However, unintentional interference and malicious attacks render GNSS-provided information unreliable. Receiver Autonomous Integrity Monitoring (RAIM) is considered an effective and lightweight protection method when a subset of the available satellite measurements is affected. However, conventional RAIM Fault Detection and Exclusion (FDE) can be computationally expensive, due to iterative search to exclude faulty signals, in case of many faults and more so for multi-constellation GNSS receivers. Therefore, we propose a fast multiple fault detection and exclusion (FM-FDE) algorithm, to detect and exclude multiple faults for both single and multi-constellation receivers. The novelty is that FM-FDE can effectively exclude faults without an iterative search for faulty signals. FM-FDE calculates position distances of any subset pairs with max{3 + P, 2P} measurements, where P is the number of constellations. Then, the algorithm utilizes statistical testing to examine the distances and identify faulty measurements to exclude from the computation of the resultant PVT solution. We evaluate FM-FDE with synthesized faulty measurements in a collected data set; it shows that FM-FDE is practically equally effective as the conventional Solution Separation (SS) FDE in a single constellation receiver. The computational advantage of FM-FDE is more pronounced in a multi-constellation setting, e.g., being more efficient for GPS-Galileo receivers facing more than 2 faults across both constellations. The trade-off is that FM-FDE slightly degrades performance in terms of detection and false alarm probabilities with small errors, compared to the conventional SS FDE.

GPS Vector Tracking Loop with Fault Detection and Exclusion

In this paper, both the integrity monitoring and fault detection and exclusion (FDE) mechanisms are incorporated into the vector tracking loop (VTL) architecture of the Global Positioning System (GPS) receiver for reliability enhancement. For the VTL, the tasks of signal tracking and navigation state estimation no longer process separately and a single extended Kalman filter (EKF) is employed to simultaneously track the received signals and estimate the receiver’s position, velocity, etc. In contrast to the scalar tracking loop (STL) which utilizes the independent parallel tracking loop approach, the VTL technique is beneficial from the correlation of each satellite signal and user dynamics. The VTL approach provides several important advantages. One of the merits is that the tracking loop can be assisted for overcoming the problem of signal blockage. Although the VTL architectures provide several important advantages, they suffer some fundamental drawbacks. For example, the errors in the navigation solutions may degrade the tracking accuracy. The most significant drawback is that failure of tracking in one channel may affect the entire tracking loop and possibly lead to loss of lock. For reliability enhancement, the EKF based integrity monitoring and FDE algorithms are developed to prevent the error from spreading into the entire tracking loop. The integrity monitoring is utilized to check the possible fault in the pseudorange and the pseudorange rate, followed by the FDE mechanism employed to exclude the abnormal satellite signals. Performance assessment and evaluation for the proposed approach will be presented.

GPS Vector Tracking Receivers with Rate Detector for Integrity Monitoring

In this paper, the integrity monitoring algorithm based on a Kalman filter (KF) based rate detector is employed in the vector tracking loop (VTL) of the Global Positioning System (GPS) receiver. In the VTL approach, the extended Kalman filter (EKF) simultaneously tracks the received signals and estimates the receiver’s position, velocity, etc. In contrast to the scalar tracking loop (STL) that uses the independent parallel tracking loop approach, the VTL technique uses the correlation of each satellite signal and user dynamics and thus reduces the risk of loss lock of signals. Although the VTL scheme provides several important advantages, the failure of tracking in one channel may affect the entire system and lead to loss of lock on all satellites. The integrity monitoring algorithm can be adopted for robustness enhancement. In general, the standard integrity monitoring algorithm can timely detect the step type erroneous signals. However, in the presence of ramp type slowly growing erroneous signals, detection of such type of error takes much more time since the error cannot be detected until the cumulative exceeds the specified threshold. The integrity monitoring based on the rate detector possesses good potential for resolving such problem. The test statistic based on the pseudorange residual in association with the EKF is applied for determination of whether the test statistic exceeds the allowable threshold values. The fault detection and exclusion (FDE) mechanism can then be employed to exclude the hazardous erroneous signals for the abnormal satellites to assure normal operation of GPS receivers. Feasibility of the integrity monitoring algorithm based on the EKF based rate detector will be demonstrated. Performance assessment and evaluation will be presented.

Fault Detection and Exclusion Method for a Deeply Integrated BDS/INS System.

The Inertial Navigation System (INS) is often fused with the Global Navigation Satellite System (GNSS) to provide more robust and superior navigation service, especially in degraded signal environments. Compared with loosely and tightly coupled architectures, the Deep Integration (DI) architecture has better tracking and positioning performance. Information is shared among channels, and the assistant information from INS helps to reduce the dynamic stress of tracking loops. However, this vector tracking architecture may result in easy propagation of errors among tracking channels. To solve this problem, a Fault Detection and Exclusion (FDE) method for the deeply integrated BeiDou Navigation Satellite System (BDS)/INS navigation system is proposed in this paper. This method utilizes pre-filters’ outputs and integration filter’s estimations to form test statistics. These statistics can help to detect and exclude both step errors and Slowly Growing Errors (SGEs) correctly. The monitoring capability of the method was verified by a simulation which was based on a software receiver. The simulation results show that the proposed FDE method works effectively. Additionally, the method is convenient to be implemented in real-time applications because of its simplicity.

Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction.

To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.

GNSS Fault Detection and Exclusion Based on Virtual Pseudorange‐Based Consistency Check Method

Although there have been many implementations of Global navigation satellite system (GNSS) in rail transport systems, the integrity assurance of GNSS positioning is still not highly concerned by the users. Integrity is a significant issue for specific safety-relevant GNSS railway applications, especially the railway signaling system which requires a critical safety level. Classical integrity monitoring solutions require an acceptable satellite visibility level to carry out Fault detection and exclusion (FDE), and this restriction leads to a degraded availability of FDE and final position computation. In order to break the limitation by satellite visibility, a consistency check method for FDE is proposed based on the virtual satellite pseudorange data, which is established based on the track database. Results from simulations show that the presented method can detect and exclude different types of faults effectively. Compared with classical residualbased GNSS integrity monitoring algorithm, the presented method achieves a higher FDE availability and makes better use of the satellite measurements even under the limited GNSS observing conditions.

Object-Detection-Aided GNSS and Its Integration With Lidar in Highly Urbanized Areas

Positioning is a key function for autonomous vehicles that requires globally referenced localization information. Lidarbased mapping, which refers to simultaneous localization and mapping (SLAM), provides continuous positioning in diverse scenarios. However, SLAM error can accumulate through time. Besides, only relative positioning is provided by SLAM. The Global Navigation Satellite System (GNSS) receiver is one of the significant sensors for providing globally referenced localization, and it is usually integrated with lidar in autonomous driving. However, the performance of the GNSS is severely challenged due to the reflection and blockage caused by buildings in superurbanized cities, including Hong Kong, China; Tokyo; and New York, resulting in the notorious non-line-of-sight (NLOS) receptions. Moreover, the uncertainty of the GNSS positioning is ambiguous, leading to the incorrect tuning of its weight during GNSS?lidar integration. This article innovatively employs lidar to identify the NLOS measurement of the GNSS receiver using point-cloud-based object detection. Measurements from satellites suffering from NLOS reception will be excluded based on the proposed fault detection and exclusion (FDE) algorithm. Then, GNSS-weight least-square positioning is conducted based on the surviving measurements from FDE. The noise covariance of the GNSS positioning is calculated by considering the potential location errors caused by the NLOS and the remaining LOS measurements. The improved GNSS result and its corresponding noise covariance are integrated with lidar through a graph-based SLAM-integration framework. Experimental results indicate that the proposed GNSS?lidar integration can obtain improved positioning accuracy in a highly urbanized area in Hong Kong.

Robust and Reliable Multi-Sensor Navigation Filter for Maritime Application

This publication describes the further development of a navigation concept especially designed for maritime application and its recent integration on the research ship DENEB of the German Federal Maritime and Hydrographic Agency (BSH). The proposed navigation concept consists of a tightly-coupled navigation filter, which bases on quantities of an inertial measurement unit (IMU), is aided by a Doppler velocity log (DVL), and Global Navigation Satellite System (GNSS) dual constellation signals of two antennas. Integrity monitoring with fault detection and exclusion (FDE), ensures the reliability of the GNSS observables. A new approach for integrating low quality and biased DVL data without endangering the state estimation accuracy and preciseness, but enhancing it’s robustness is introduced. A new integration of real-time sea level data from an online service in the filter improves robustness in addition. The navigation system is evaluated in an extensive measurement campaign with DENEB in harbor of Rostock, Germany. Basically, two different advantages of the proposed navigation concept are investigated. Firstly, evaluation proves that integration of the low quality and biased data of the vessels DVL is possible without lowering the navigation filter accuracy significantly. Secondly, the robustness of the concept against sensor failure is shown. Therefore, by means of post-processing the recorded data, GNSS and DVL outage is investigated. Evaluation verifies, that the multi-sensor fusion and the integration of real-time sea level data improves the robustness of the navigation solution and therefore is qualified for autonomous application.

Global Navigation Satellite Systems Fault Detection and Exclusion: A Parameterized Quadratic Programming Approach

In this article, the problem of detecting and excluding faulty global navigation satellite systems (GNSSs) measurements at the receiver end is formulated as a parameterized quadratic programming (PQP) problem. Compared to the existing fault detection and exclusion (FDE) methods, which mostly rely on exhaustive search, the PQP method is computationally efficient for finding the outliers even when the number of outliers is moderate or large. In the context of multiconstellation GNSS where the probability of multiple simultaneous faults is increased, the PQP method is ideal for the task of fault exclusion since the computation time does not increase with the number of fault hypotheses. It is noted that this article addresses the computational load due to the exclusion function only. The integrity risk bound and continuity risk bound are of fundamental importance to assess the performance of FDE algorithms for safety-critical applications. With the aim to benefit safety-critical applications, the PQP method is integrated with the integrity risk bound and the continuity risk bound derived for FDE using Chi-squared receiver autonomous integrity monitoring (RAIM). It is emphasized that the integration of the PQP method and the integrity and continuity risk bounds do not make the PQP method a practical integrity monitoring method. This is because the computation of the integrity risk bound is still combinatorial and the resulting integrity risk bound is rather conversative. Instead, the integration allows for the opportunity to refine the PQP method so that it can be considered a practical integrity monitoring method. In particular, improvements to reduce the computational load for the Chi-squared RAIM FDE integrity risk bound calculation can readily be applied to the integrity risk bound calculation for the PQP method. Also, further research on PQP parameter tuning can be pursued to tighten the integrity risk bound.

An integrated fault detection and exclusion scheme to support aviation navigation

This paper proposes a novel integrity monitoring scheme against global navigation satellite systems (GNSS) fault for civil aviation navigation. The main contributions are (a) developing an efficient user algorithm that integrates fault detection and exclusion (FDE) functions, and (b) deriving the analytical methods to quantify its corresponding integrity risk. The intended application of the new scheme is advanced receiver autonomous integrity monitoring (ARAIM), which is proposed by the United States (U.S.) and European Union (E.U.), and will serve as the next generation of the main aviation navigation means. In this new approach, the exclusion decision-making process is unified into the first layer detection step, thereby dramatically improving efficiency. The principle of this method is utilizing the multi-dimensional parity vector projections in parity space to extract the information of faults. In this work, we derive the projection matrix for single satellite failure modes, establish the mechanism for determining exclusion subset based on the projection magnitudes, and rigorously account for the false exclusion probabilities in the integrity risk quantification. The feasibility of the algorithm is verified and validated using Monte-Carlo simulations, and the performance is analyzed by evaluating the integrity risk. It is shown that the new FDE scheme can efficiently and effectively exclude the faulty satellites as desired, while achieving promising navigation performance.

On detection of observation faults in the observation and position domains for positioning of intelligent transport systems

Intelligent transportation systems (ITS) depend on global navigation satellite systems (GNSS) as a major positioning sensor, where the sensor should be able to detect and exclude faulty observations to support its reliability. In this article, two fault detection and exclusion (FDE) approaches are discussed. The first is its application in the observation domain using Chi-square test in Kalman filter processing. The second approach discusses FDE testing in the positioning domain using the solution separation (SS) method, where new FDE forms are presented that are tailored for ITS. In the first form, the test is parameterized along the direction of motion of the vehicle and in the cross-direction, which are relevant to applications that require lane identification and collision alert. A combined test is next established. Another form of the test is presented considering the maximum possible positioning error, and finally a direction-independent test. A new test that can be implemented in the urban environment is presented, which takes into account multipath effects that could disrupt the zero-mean normal distribution assumption of the positioning errors. Additionally, a test is presented to check that the position error resulting from the remaining measurements lies within acceptable limits. The proposed methods are demonstrated through a kinematic test run in various environments that may be experienced in ITS.

An Enhanced RAIM Method for Satellite-Based Positioning Using Track Constraint

Integrity of global navigation satellite system (GNSS) positioning is one major concern for the GNSS-based railway train control systems due to critical safety requirements. In this paper, we propose a map-enhanced receiver autonomous integrity monitoring (RAIM) method for satellite-based positioning of railway trains, which could benefit from the autonomy of fault detection and exclusion (FDE) without relying on additional sensors in a location determination system (LDS). With the proposed RAIM solution, the spatial constraint from the fixed railway tracks is employed to predict the measurements from invisible satellites, which makes it possible of performing FDE even when insufficient satellites are visible to enable the conventional RAIM operation. We demonstrate that different faults in raw GNSS measurements can be identified and isolated by extracting immediate information from the state estimator. With a virtual satellite pseudorange generation mechanism, two-stage FDE architecture is proposed. A pseudorange consistency check logic in the measurement domain is adopted at a local stage. Furthermore, a filter-innovation-based RAIM strategy is utilized to realize a global test in the positioning domain under a cubature Kalman filter (CKF)-based sensor data fusion framework. The proposed method is verified by the field experiment and simulations and the results show the improvement to the availability and safety of GNSS-based positioning.

Informational Framework for Minimalistic Visual Odometry on Outdoor Robot

In an unknown environment, assessing the robot trajectory in real time is one of the key issues for a successful robotic mission. In such an environment, the absolute measurements, such as the GPS data, may be unavailable. Moreover, estimating the position using only proprioceptive sensors, such as encoders and inertial measurement units (IMUs), will generate errors that increase over time. This paper presents a multisensor fusion approach between IMU and ground optical flow used to estimate the position of a mobile robot while ensuring high integrity localization. The data fusion is done through the informational form of the Kalman filter, namely, information filter (IF). A fault detection and exclusion (FDE) step is added in order to exclude the erroneous measurements from the fusion procedure by making it fault tolerant and to ensure a high localization performance. The approach is based on the use of the IF for the state estimation and tools from the information theory for the FDE. Our proposed approach evaluates the quality of a measurement based on the amount of information it provides using informational metrics such as the Kullback–Leibler divergence. The approach is validated on data obtained from experiments performed in outdoor environments under various conditions, including high-dynamic-range lighting and different ground textures.

An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers.

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. The pre-filter can be divided into two categories: coherent and non-coherent. This paper focuses on the performance improvements of non-coherent pre-filter. Firstly, the signal characteristics of coherent and non-coherent integration—which are the basis of tracking error estimation—are analyzed in detail. After that, the probability distribution of estimation noise of four-quadrant arctangent (ATAN2) discriminator is derived according to the mathematical model of coherent integration. Secondly, the statistical property of observation noise of non-coherent pre-filter is studied through Monte Carlo simulation to set the observation noise variance matrix correctly. Thirdly, a simple fault detection and exclusion (FDE) structure is introduced to the non-coherent pre-filter design, and thus its effective working range for carrier phase error estimation extends from (−0.25 cycle, 0.25 cycle) to (−0.5 cycle, 0.5 cycle). Finally, the estimation accuracies of discriminator, coherent pre-filter, and the enhanced non-coherent pre-filter are evaluated comprehensively through the carefully designed experiment scenario. The pre-filter outperforms traditional discriminator in estimation accuracy. In a highly dynamic scenario, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, when compared with coherent pre-filter. The enhanced non-coherent pre-filter outperforms the coherent pre-filter in code phase error estimation when carrier-to-noise density ratio is less than 28.8 dB-Hz, in carrier frequency error estimation when carrier-to-noise density ratio is less than 20 dB-Hz, and in carrier phase error estimation when carrier-to-noise density belongs to (15, 23) dB-Hz (26, 50) dB-Hz.

Multi-sensor fusion approach with fault detection and exclusion based on the Kullback–Leibler Divergence: Application on collaborative multi-robot system

This paper presents a multi-sensor fusion strategy able to detect the spurious sensors data that must be eliminated from the fusion procedure. The used estimator is the informational form of the Kalman Filter (KF) namely Information Filter (IF). In order to detect the erroneous sensors measurements, the Kullback–Leibler Divergence (KLD) between the a priori and a posteriori distributions of the IF is computed. It is generated from two tests: One acts on the means and the other deals with the covariance matrices. Optimal thresholding method based on a Kullback–Leibler Criterion (KLC) is developed and discussed in order to replace classical approaches that fix heuristically the false alarm probability.Multi-robot systems became one of the major fields of study in the indoor environment where the environmental monitoring and the response to crisis must be ensured. Consequently, the robots required to know precisely their positions and orientations in order to successfully perform their mission. Fault detection and exclusion (FDE) play a crucial role in enhancing the integrity of localization of the multi-robot team. The main contributions of this paper are: - developing a new method of sensors data fusion that tackle the erroneous data issues, - developing a Kullback–Leibler based criterion for the threshold optimization, - Validation with real experimental data from a group of robots.

Fault tolerant multi-sensor fusion based on the information gain

In the last decade, multi-robot systems are used in several applications like for example, the army, the intervention areas presenting danger to human life, the management of natural disasters, the environmental monitoring, exploration and agriculture. The integrity of localization of the robots must be ensured in order to achieve their mission in the best conditions. Robots are equipped with proprioceptive (encoders, gyroscope) and exteroceptive sensors (Kinect). However, these sensors could be affected by various faults types that can be assimilated to erroneous measurements, bias, outliers, drifts,… In absence of a sensor fault diagnosis step, the integrity and the continuity of the localization are affected. In this work, we present a muti-sensors fusion approach with Fault Detection and Exclusion (FDE) based on the information theory. In this context, we are interested by the information gain given by an observation which may be relevant when dealing with the fault tolerance aspect. Moreover, threshold optimization based on the quantity of information given by a decision on the true hypothesis is highlighted.