2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)最新文献

{"title":"Detecting GNSS spoofing of ADS-B equipped aircraft using INS","authors":"Birendra Kujur, S. Khanafseh, B. Pervan","doi":"10.1109/PLANS46316.2020.9109966","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9109966","url":null,"abstract":"In this paper, we develop a novel method to detect Global Navigation Satellite Systems (GNSS) spoofing for an Automatic Dependent Surveillance-Broadcast (ADS-B) equipped aircraft. The Federal Aviation Administration (FAA) has mandated [18] all civil aircraft to be ADS-B Out equipped by January 1, 2020. The ADS-B Out broadcast sent to Air Traffic Control (ATC) consists of the aircraft's position, velocity, and other aircraft-specific information, all of which being unencrypted, poses a serious integrity threat. With readily available ADS-B trackers [19], [20], a spoofer can accurately track an aircraft to generate a spoofed trajectory that can go undetected [11]. We propose a novel method to modulate the ADS-B Out position broadcast such that a spoofed trajectory generated using the modulated ADS-B will be detectable by comparing Inertial Navigation System (INS) positions against those obtained using the spoofed GNSS signal. The amplitude of ADS-B modulation is selected to exceed the nominal INS error covariance so that spoofing is observable. In this work, we analytically quantify the magnitude of ADS-B modulation that will be sufficient for spoofing detection. During scenarios of GNSS signal jamming and spoofing, reauthentication of a reacquired GNSS signal is necessary to maintain integrity. During a GNSS outage, given enough time, the INS solution drift will grow large enough such that the spoofed GNSS solution might be within the INS error covariance envelope and go undetected. For GNSS outage scenarios we propose continuous modulation of ADS-B position and analytically quantify the magnitude such that spoofing of the GNSS signal is detectable after signal is re-acquired.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131634256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UWB ranging aided pedestrian geolocation with GPB-based Filtering for LoS and NLoS Measurement processing","authors":"Jianan Zhu, Solmaz S. Kia","doi":"10.1109/PLANS46316.2020.9110175","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110175","url":null,"abstract":"This paper considers the global localization of a pedestrian via an ultra-wideband (UWB) ranging aided inertial navigation system (INS) and aims to address the challenges involved in proper processing of UWB range measurements. Even though UWB offers a decimeter level accuracy in line-of-sight (LoS) ranging, its accuracy degrades significantly in non-line-of-sight (NLoS). This drop in accuracy is due to a significant unknown positive bias in the NLOS range measurements. Therefore, the measurement models used in UWB LoS and NLoS ranging conditions are different, and proper processing of NLoS measurements requires a bias compensation measure. Previous work on bias compensation for UWB ranging that is used to aid an INS based localization assumes that the LoS and NLoS measurements are identified and distinguished from each other with absolute certainty. However, in practice, this assumption is hard to satisfy, and identifiers that determine the type of UWB range measurements deliver their results with only some level of certainty. To take into account the probabilistic nature of the NLoS identifiers, in this paper, we propose an adaptive localization based on the first-order generalized pseudo Bayesian (GPB) method to seamlessly handle the measurement model switching between LoS and NLoS UWB range measurements. The effectiveness of our proposed method is demonstrated via an experiment for pedestrian geolocation using a shoe-mounted INS system aided by UWB range measurements with respect to beacons with known locations.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128465833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic Modeling of Gravity Compensation Error in GNSS-Aided Inertial Navigation Systems","authors":"Timothy G. Needham, M. Braasch","doi":"10.1109/PLANS46316.2020.9110195","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110195","url":null,"abstract":"GNSS-aided inertial navigation systems (INS) are used in a variety of aerospace applications as robust sources of position, velocity, attitude, and time. Due to the safety-critical nature of civil aviation applications, navigation systems must be rigorously tested to ensure they will not output hazardously misleading information (HMI). RTCA, a standards-making organization, develops documents containing requirements and testing guidance for manufacturers of equipment installed on aircraft. RTCA Special Committee 159 has organized a working group (WG-2C) to develop a new Minimum Operational Performance Standard (MOPS) that addresses GNSS-aided INS. The new MOPS will accommodate a wide performance range of inertial sensors (e.g., tactical as well as navigation grade), and it will cover accuracy and integrity coasting performance during GNSS outages. As part of the focus on coasting performance during GNSS outages, WG-2C is investigating the effects of gravity modeling error on the aided-inertial solution. The portion of gravity deflection not accounted for in the navigation system by a model or database results in horizontal acceleration error that integrates to become velocity and position error. Previous research by the authors [1] has shown that unmodeled gravity deflections can induce up to 200 meters of position error, which is a non-negligible portion of an RNP 0.3 or less lane-width. Additional work by the authors [2] showed that even if a worldwide high fidelity model such as EGM2008 is used, the commission and omission errors of the model still remain in the acceleration estimates after compensation. This paper first presents deflection of the vertical (DOV) profiles for real RNP airport approaches to demonstrate realistic severe situations for GNSS-coasting. Next, low and high order gravity models are implemented and the statistics of the residuals are studied. Finally, system identification techniques are used to analyze the gravity model residuals and develop stochastic processes that are statistically representative of these residuals. Second-order autoregressive models are identified, and model parameters are dynamically drawn from the numerically derived statistical distributions to account for the non-stationary nature of the residuals.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129249831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection","authors":"Anna Guerra, Francesco Guidi, D. Dardari, P. Djurić","doi":"10.1109/PLANS46316.2020.9110163","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110163","url":null,"abstract":"In this paper, we study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown environment. The goal is to optimize its trajectory with the purpose of maximizing the mapping accuracy and, at the same time, to avoid areas where measurements might not be sufficiently informative from the perspective of a target detection. This problem is formulated as a Markov decision process (MDP) where the UAV is an agent that runs either a state estimator for target detection and for environment mapping, and a reinforcement learning (RL) algorithm to infer its own policy of navigation (i.e., the control law). Numerical results show the feasibility of the proposed idea, highlighting the UAV's capability of autonomously exploring areas with high probability of target detection while reconstructing the surrounding environment.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116424455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of geometric displacements of odometers in a GNSS/inertial navigation system installed on a land vehicle","authors":"N. Vasilyuk, D. Tokarev","doi":"10.1109/PLANS46316.2020.9110193","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110193","url":null,"abstract":"An identification of geometric displacements of an odometer is a real-time determination of the odometer's lever arm vector relative to an IMU, determination of the IMUs frame attitude relative to a vehicle's body frame, and a refinement of an odometer's scale factor. Odometers measurements models for steering front wheels and for uncontrolled rear wheels have been developed in detail. Observability conditions of the geometric displacements on trajectories specific for a land vehicle have been considered. It is shown that by setting the odometer's lever arm components relative to the vehicle body, the observability of all three IMU's attitude angles on trajectories of a certain shape may be achieved. The partial observability of the geometric displacements in the absence of information about a steering angle of the front wheels has been described. Secondary observable combinations of the geometric displacements have been identified. Odometers measurement models have been reformulated in terms of these combinations. An iterative least-squares procedure has been developed for evaluation of these observable combinations in real time. Observation equations have been obtained for incorporating the odometer's measurements into an observation vector of a navigation loose-coupling Extended Kalman Filter. Experimental results obtained with the odometer, attached on both the front and rear wheels have been presented.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116677552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model Predictive Control for Vision-Based Quadrotor Guidance","authors":"Karsten Mueller, Michael Fennel, G. Trommer","doi":"10.1109/PLANS46316.2020.9110137","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110137","url":null,"abstract":"A model predictive control algorithm for autonomous approaches of quadrotor helicopters to a window is presented in this paper. The target is selected by an operator in a reference image which is sent to the vehicle. A wide baseline image matching algorithm is used to obtain the target position in the current image. The vehicle guidance for the approach is realized by a nonlinear model predictive control algorithm which calculates the desired thrust and angular velocities while taking the goals of approaching the target orthogonally and keeping the target visible in the camera image into account. Moreover, two novel methods for the estimation of the orthogonal distance and orientation to the façade plane in which the target window is located are presented. The first method relies on laser rangefinder measurements while the second method is based on the decomposition of the estimated homography matrix between the façade plane in the reference and the live image. Monte Carlo simulations of approaches to a house show the accuracy and robustness of the vehicle guidance. The trajectories converge even from difficult start positions, for example approaches with acute angles between the façade and the quadrotor's orientation. As desired, the window is approached perpendicularly towards the end of the approaches.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116777200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Assisted Direct Visual Odometry with Virtual Wide Field-of- View Tracking","authors":"Ruihang Miao, Peilin Liu, Fei Wen, Zheng Gong, Wuyang Xue, R. Ying","doi":"10.1109/PLANS46316.2020.9110147","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110147","url":null,"abstract":"Visual odometry systems are usually categorized as feature-based visual odometry systems and direct visual odometry systems. The feature-based visual odometry systems spend time on calculating descriptors while direct visual odometry systems directly calculate residuals. The optimization of direct visual odometry systems is sensitive with initial states while feature-based visual odometry systems did not suffer from this problem. This paper presents a features assisted stereo direct visual odometry system with virtual wide field-of-view tracking. It combines the advantages of feature-based methods and direct methods. It is achieved by incorporating a feature assisted predicting module and a virtual wide field-of-view tracking module into stereo direct sparse odometry (SDSO) framework. The feature-based matching is combined in direct tracking module for efficient and robust data association. Furthermore, before data association, a virtual wide field-of-view inverse depth frame is created in case of tracking lost in aggressive rotating conditions. Evaluation results on the EuRoC datasets and the KITTI dataset show that, with the aid of the new tracking module, the stereo visual odometry becomes more robust and more accurate.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115470644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate Position and Attitude Determination in a Severe Multipath Environment Using an Uncalibrated Multi-Antenna-System","authors":"S. Zorn, Christian Siebert, M. Niestroj, Marius Brachvogel, M. Meurer","doi":"10.1109/PLANS46316.2020.9110151","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110151","url":null,"abstract":"A novel proposal is made for the accurate determination of position and antenna attitude of a multi-antenna GNSS receiver in a multipath environment. In contrast to previous work no calibration of the gain and phase mismatches and crosstalk effects, that occur in the RF-Frontend, are necessary. This allows for the usage of heavily miniaturized RF-Frontends with highly integrated systems with non-negligible crosstalks and tolerances. The novel algorithm treats the estimation of antenna attitude, the multipath detection and mitigation as well as the calibration as a coupled problem and estimates the relevant unknowns in a joint procedure. Due to this approach a reliable position estimate can be determined even in challenging conditions without the need for very accurately calibrated - or nearly “ideal” hardware components.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114893466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Cost Validation for Complementary Filter-Based AHRS","authors":"Pavlo Vlastos, G. Elkaim, R. Curry","doi":"10.1109/PLANS46316.2020.9109965","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9109965","url":null,"abstract":"This paper discusses a procedure to validate a complementary filter-based attitude and heading reference system for an off-the-shelf sensor-head using a prototype, low-cost validation apparatus and comparing course over ground measurements from GPS. A variation of an existing complementary filter design is implemented for validation. The apparatus costs less than $100 to build and shows promising validation potential for small, lightweight vehicles with budget restrictions. A small surface vehicle is used to log sensor-head and GPS data to show performance of the validated complementary filter. Comparing the attitude estimate from the complementary filter to an encoder attached to the validation apparatus, the attitude estimate matches the truth measurement to mean of less than 0.3°and standard deviation of less than 1.3°. The match between GPS course over ground to estimated yaw is more error prone, but still shows good agreement with sub-3° means and a standard deviation of less than 12°. The GPS comparison to yaw has some large errors due to lags in the measurement, but even so shows very good performance.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127714671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PPP: Perhaps the natural processing mode for precise GNSS PNT","authors":"S. Bisnath","doi":"10.1109/PLANS46316.2020.9110150","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110150","url":null,"abstract":"Until recently, GNSS PNT accuracy had a bimodal existence: meter-level positioning performance for almost all users, and decimeter- to centimeter-level positioning performance for a small class of high-performance (or professional) users. The recent introduction of mass-market, dual-frequency, multi-constellation GNSS chips and antennas has blurred this distinction. The PPP GNSS measurement processing mode promises to further reduce the gap between high-performance and mass-market GNSS solutions for emerging applications in automotive, cellular, drone, IoT, etc. applications. This work summarizes the current usage of PPP augmentation and its limitations. The latest in GNSS PPP geodetic-quality performance is presented and contrasted with mass-market hardware performance. The benefit of atmospheric constraining is explored. PPP multi-sensor integration, including with network RTK is assessed. All of these developments are leading to the reduction of current PPP limitations, the introduction of new constellation-based services, and, potentially, the near-term use of PPP augmentation at the core of precise GNSS PNT for a wide breath of applications.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123418785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}