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This paper describes the development, implementation, and testing of a GNSS jammer localizer using power measurement profiles collected during un-crewed aerial system (UAS) fly-bys. A linearized measurement equation based on the Friis power transmission formula is derived in which RF channel propagation parameters are grouped into a single parameter for estimation. Synchronized power and UAS position measurements are processed in a batch-type sequential non-linear least squares algorithm for simultaneous estimation of static jammer position and received power model parameters. We develop a low size, weight, power, and cost (SWAP-C) quad-rotor UAS test bed that can collect and time-stamp power measurements with UAS position. Since GNSS jamming is illegal, a LoRa 868 MHz transmitter is used as a surrogate GNSS jammer during field testing – providing Received Signal Strength Indicator (RSSI) measurements to the LoRa receiver onboard the UAS. Testing is conducted at the Virginia Tech Kentland Experimental Aerial Systems Lab, where emitter localization is evaluated for three different trajectories. Experimental performance analysis suggests that meter-level localization accuracy is achievable with prior knowledge on source location and by accounting for antenna gain pattern variations over time in the estimation process with a first order Gauss Markov Process. 

The problem of quantifying robot localization safety in the presence of undetected sensor faults is critical when preparing for future applications where robots may interact with humans in life-critical situations; however, the topic is only sparsely addressed in the robotics literature. In response, this work leverages prior work in aviation integrity monitoring to tackle the more challenging case of evaluating localization safety in Global Navigation Satellite System (GNSS)-denied environments. Localization integrity risk is the probability that a robot’s pose estimate lies outside pre-defined acceptable limits while no alarm is triggered. In this article, the integrity risk (i.e., localization safety) is rigorously upper bounded by accounting for both nominal sensor noise and other non-nominal sensor faults. An extended Kalman filter is employed to estimate the robot state, and a sequence of innovations is used for fault detection. The novelty of the work includes (1) the use of a time window to limit the number of monitored fault hypotheses while still guaranteeing safety with respect to previously occurring faults and (2) a new method to account for faults in the data association process. 

This paper describes the derivation, analysis and implementation of a new data association method that provides a tight bound on the risk of incorrect association for LiDAR feature-based localization. Data association (DA) is the process of assigning currently-sensed features with ones that were previously observed. Most DA methods use a nearest-neighbor criterion based on the normalized innovation squared (NIS). They require complex algorithms to evaluate the risk of incorrect association because sensor state prediction, prior observations, and current measurements are uncertain. In contrast, in this work, we derive a new DA criterion using projections of the extended Kalman filter's innovation vector. The paper shows that innovation projections (IP) are signed quantities that not only capture the impact of an incorrect association in terms of its magnitude, but also of its direction. The IP-based DA criterion also leverages the fact that incorrect associations are known and well-defined fault modes. Thus, as compared to NIS, IPs provide a much tighter bound on the predicted risk of incorrect association. We analyze and evaluate the new IP method using simulated and experimental data for autonomous inertial-aided LiDAR localization in a structured lab environment. 

Monitoring localization safety will be necessary to certify the performance of robots that operate in life-critical applications, such as autonomous passenger vehicles or delivery drones because many current localization safety methods do not account for the risk of undetected sensor faults. One type of fault, misassociation, occurs when a feature extracted from a mapped landmark is associated to a non-corresponding landmark and is a common source of error in feature-based navigation applications. This paper accounts for the probability of misassociation when quantifying landmark-based mobile robot localization safety for fixed-lag smoothing estimators. We derive a mobile robot localization safety bound and evaluate it using simulations and experimental data in an urban environment. Results show that localization safety suffers when landmark density is relatively low such that there are not enough landmarks to adequately localize and when landmark density is relatively high because of the high risk of feature misassociation. 

This paper presents a new method to efficiently monitor localization safety in mobile robots. Localization safety is quantified by measuring the system's integrity risk, which is a well-known aviation performance metric. However, aviation integrity monitoring solutions almost exclusively rely on the Global Navigation Satellite System (GNSS) while robot navigation usually needs the additional information provided by a state evolution model and/or relative positioning sensors, which makes previously established approaches impractical. In response, this paper develops an efficient integrity monitoring methodology applicable to Kalman Filter-based localization. The work is intended for life-or mission-critical operations such as co-robot applications where ignoring the impact of faults can jeopardize human safety. 

This paper presents a new methodology to quantify robot localization safety by evaluating integrity risk, a performance metric widely used in open-sky aviation applications that has been recently extended to mobile ground robots. Here, a robot is localized by feeding relative measurements to mapped landmarks into an Extended Kalman Filter while a sequence of innovations is evaluated for fault detection. The main contribution is the derivation of a sequential chi-squared integrity monitoring methodology that maintains constant computation requirements by employing a preceding time window and, at the same time, is robust against faults occurring prior to the window. Additionally, no assumptions are made on either the nature or shape of the faults because safety is evaluated under the worst possible combination of sensor faults. 

In this paper, a new safety risk evaluation method is developed, simulated, and tested for laser-based navigation algorithms using feature extraction (FE) and data association (DA). First, at FE, we establish a probabilistic measure of separation between features to quantify the sensor's ability to distinguish landmarks. Then, an innovation-based DA process is designed to evaluate the impact on integrity risk of incorrect associations, while considering all potential measurement permutations. The algorithm is analyzed and tested in a structured environment.