Interactive multiple model sensor analysis for Unmanned Aircraft Systems (UAS) Detect and Avoid (DAA)

Demands for access of Unmanned Aircraft Systems (UAS) to the United States National Airspace System (NAS) continue to increase. A vital safety component of manned aircraft in the NAS is the onboard pilot’s ability to see and avoid other aircraft. UAS must possess the capability of remaining safely separated from other aircraft without an onboard pilot. Detect and Avoid (DAA) systems are needed for this purpose, but requirements for the systems have not been determined to date. To fill this gap, RTCA, Inc., Special Committee 228 (SC-228) is currently working to develop Minimum Operational Performance Standards (MOPS) for UAS that will include a proposed quantitative well-clear definition and DAA system requirements. This paper discusses a study of the sensitivity of the DAA timeline contribution of UAS maneuver performance requirements associated with the well-clear definition in the case of high-speed encounters. Initial time and range requirements derived in accordance with the quantified DAA Well Clear definition and statute speed limitations may be overly restrictive considering current on-board radar needed to detect aircraft not equipped with a transponder. In order to facilitate NAS-wide integration, a trade space analysis has been performed investigating the derivation of performance requirements based on lower, yet statistically relevant, aircraft speeds. An initial assessment of the associated risks was also investigated. Some of the results are presented herein; more detailed results were made available to SC-228.

A Detect and Avoid (DAA) system is an essential enabler for integrating Unmanned Aircraft Systems (UAS) into the U.S. National Airspace System. A DAA system uses a suite of sensors, trackers and alerting and guidance algorithms to assist a remote pilot in maintaining separation from airborne traffic. The initial minimum performance standards focused on en route operations; in order to facilitate operations in and around the airport environment, this paper presents the derivation of the early alert threshold for UAS terminal operations.

A consortium of government, industry and academia is currently working to establish minimum operational performance standards for Detect and Avoid (DAA) and Control and Communications (C2) systems in order to enable broader integration of Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS). One subset of these performance standards will need to address the DAA display requirements that support an acceptable level of pilot performance. From a pilot's perspective, the DAA task is the maintenance of self separation and collision avoidance from other aircraft, utilizing the available information and controls within the Ground Control Station (GCS), including the DAA display. The pilot-in-the-loop DAA task requires the pilot to carry out three major functions: 1) detect a potential threat, 2) determine an appropriate resolution maneuver, and 3) execute that resolution maneuver via the GCS control and navigation interface(s). The purpose of the present study was to examine two main questions with respect to DAA display considerations that could impact pilots' ability to maintain well clear from other aircraft. First, what is the effect of a minimum (or basic) information display compared to an advanced information display on pilot performance? Second, what is the effect of display location on UAS pilot performance? Two levels of information level (basic, advanced) were compared across two levels of display location (standalone, integrated), for a total of four displays. The authors propose an eight-stage pilot-DAA interaction timeline from which several pilot response time metrics can be extracted. These metrics were compared across the four display conditions. The results indicate that the advanced displays had faster overall response times compared to the basic displays, however, there were no significant differences between the standalone and integrated displays. Implications of the findings on understanding pilot performance on the DAA task, the development of DAA display performance standards, as well as the need for future research are discussed.

An integrated encounter modeling and simulation tool for a robust detect-and-avoid system evaluation

This paper presents a simple yet efficient adaptive Kalman filter for tracking targets expected to perform varying turn maneuvers. The process noise covariance level of a second order Kalman filter is adjusted at each time step according to the calculated turn rate. The turning rate is estimated from the magnitude of the calculated acceleration divided by the estimated speed of the target. At each scan the previous and current velocity estimates are used to calculate the acceleration. The comparison of the performance of the proposed algorithm is made with that of an interacting multiple model (IMM) algorithm, employing three models with different levels of process noise covariance and also to that of a second order Kalman filter. Two different assumptions have been made for selecting the process noise values for the the IMM and Kalman filter algorithms, in the first case it was assumed that there was no prior information about the target motion whereas in the second case it was assumed that the largest turn rate that the target of interest could perform was known. The IMM algorithm utilizing three models gives slightly better estimates during the nonmaneuvering periods, but the proposed algorithm is superior to the IMM algorithm during maneuvering periods in terms of estimation errors. Also the proposed algorithm requires 78% less computation, almost the same number of calculations required by a single fixed process noise Kalman filter, than the IMM algorithm.

The Interacting Multiple Model (IMM) algorithm has been used in various applications with good performance. However, to the best of our knowledge, stability conditions of the IMM algorithm have not been shown in the control literature. This technical note presents a sufficient condition for the exponential stability of the IMM algorithm for a class of Markov jump linear systems. We derive a lower bound and an upper bound for the error covariances of the IMM algorithm, and then derive the stability condition by the Lyapunov approach.

In order to provide for the safe integration of unmanned aircraft systems (UAS) into the National Airspace System, the control and non-payload communications (CNPC) link connecting the ground-based pilot with the unmanned aircraft must be highly reliable and robust, based upon standards that enable certification. Both line-of-sight (LOS) links using terrestrial-based communications and beyond-line-of-sight (BLOS) links using satellite communications are required to support UAS operations. The development of standards has been undertaken by RTCA Special Committee 228 (SC-228), with supporting technical data developed by NASA under the UAS in the National Airspace (NAS) Project. As a result of this work minimum operational performance standards (MOPS) have been completed and published for the LOS CNPC system. The second phase of work, for both NASA and RTCA involves the BLOS CNPC systems. The development of technical data to support MOPS development for UAS BLOS satellite-based CNPC links has now been initiated by NASA, and RTCA SC-228 has organized itself to begin the MOPS development process. This paper will provide an overview of the work that has been completed to date by the Communications Subproject in support of LOS C2 communications for UAS followed by an update of plans and progress for the BLOS phase of the project, with the focus on the UAS C2 spectrum aspects.

Hybrid systems consist of both continuous state evolutions and discrete state (or mode) transitions. In many hybrid systems, mode transitions are governed by guard conditions that are dependent on the continuous state. These kind of mode transitions can be described by continuous-state-dependent mode transition probabilities. However, most multiple model Kalman filter based algorithms, such as the well-known interacting multiple model (IMM) algorithm, assume constant mode transition probabilities. We propose a hybrid estimation algorithm based on the IMM approach for the stochastic hybrid systems with continuous-state-dependent transitions. We first develop two models to describe the guard conditions for various stochastic hybrid systems. From the models of the guard conditions, we derive the corresponding continuous-state-dependent mode transition probabilities, which are expressed in terms of Gaussian probability density functions (pdfs) or Gaussian cumulative density functions (cdfs). We then propose a hybrid estimation algorithm for the stochastic hybrid systems based on the IMM algorithm. Like the IMM algorithm, our algorithm utilizes Gaussian distributions to represent the continuous state pdfs which are updated analytically at each time step. We also use analytical integration methods to reduce the computational cost of updating the mode probabilities. As a result, the proposed algorithm is computationally efficient and is applicable to high-dimensional problems. The performance of the proposed algorithm is illustrated with two examples in air traffic control tracking applications.

This paper considers the decline problem of tracking accuracy caused by acceleration estimation inaccuracy in hybrid grid multiple model (HGMM) algorithm. A new improved tracking algorithm of maneuvering targets based on CSHGMM is proposed. Grid partition is performed by estimation of the acceleration mean in current statistic (CS) model, where a fading factor is adopted to improve tracking accuracy by adjusting the gain matrix under the condition of the abrupt change of acceleration. Then possible model sets of the target are generated online, and interactive multiple model algorithm is adopted to estimate the target state. The proposed algorithm performance is tested and compared in general and strong maneuvering scenarios, and simulation results demonstrate that the proposed algorithm not only improves the accuracy for maneuvering target tracking, but also can be applied to most of the tracking scenarios.

Detect-and-Avoid (DAA) systems are essential to the safe operations of Unmanned Aircraft Systems, and have the objectives of mitigating collisions with and remaining Well Clear of manned aircraft. This paper analyzes four candidate DAA Well Clear definitions for non-cooperative aircraft using mitigated performance metrics of DAA systems. These DAA Well Clear definitions were proposed in previous work based on their unmitigated collision risk and maneuver initiation range. In this work they are evaluated using safety and operational suitability metrics computed from a large number of representative encounters. Results suggest that although the four candidate DAA Well Clear definitions provide comparable safety, the alerting characteristics give preference for the DAA Well Clear definition without a temporal parameter.

Combining the interacting multiple model (IMM) and the unscented particle filter (UPF), a new multiple model filtering algorithm is presented. Multiple models can adapt to targets’ high maneuvering. Particle filter can deal with the nonlinear or non-Gaussian problems and the unscented Kalman filter (UKF) may improve the approximate accuracy. Compared with other interacting multiple model algorithms in the target tracking simulations, the results demonstrate the validity of the new filtering method, that is, particle filter with the UKF proposal.

Variable Structure Multiple Model (VSMM) estimation generalizes Multiple Model (MM) estimation by assuming that the set of models used for MM estimation is time varying. By using VSMM estimation, a large model set which may cover all possible target maneuvers can be used without significant increase of computational load, while maintaining a reasonable estimation accuracy. Various implementable VSMM algorithms, like Model Group Switching (MGS), Likely Model Set (LMS) and Minimal Sub-Model-Set Switching (MSMSS) using the Interacting Multiple Model (IMM) algorithm with a sub-model-set adaptation logic have appeared in the recent literature. However, the use of these algorithms for tracking maneuvering target in clutter has not been explored. In presence of clutter, one need to use data association technique to differentiate target originated measurement from clutter. The probabilistic data association (PDA) has been popularly adopted to many algorithms for tracking in clutter. In this paper, we integrate PDA technique with MSMSS and propose a VSIMM-PDA algorithm for maneuvering target tracking in clutter. A new grating technique to account for potential model errors is used. A numeric example via multiple Monte Carlo runs, which compares the performance of the new algorithm to a standard IMM-PDA in terms of Root Mean Squared error (RMS) and percentage of track loss, is presented.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The generalized Shiryayev sequential probability ratio test (SSPRT) is applied to linear dynamic systems for singlefault isolation and estimation. The algorithm turns out to be the multiple model (MM) algorithm considering all thepossible model trajectories. In real application, this algorithm must be approximated due to its increasing computationcomplexity and the unknown parameters of the fault severeness. The Gaussian mixture reduction is employed toaddress the problem of computation complexity. The unknown parameters are estimated in real time by modelaugmentation based on maximum likelihood estimation (MLE) or expectation. Hence, the system state estimation,fault identification and estimation can be fulfilled simultaneously by a multiple model algorithm incorporating these twotechniques. The performance of the proposed algorithm is demonstrated by Monte Carlo simulation. Although ouralgorithm is developed under the assumption of single fault, it can be generalized to deal with the case of (infrequent)sequential multiple faults. The case of simultaneous faults is more complicated and will be considered in future work.

The grid points of the Adaptive Grid Interactive Multiple Model (AGIMM) algorithm change slowly at the target high maneuvering and fluctuate when the target is moving at a constant speed. The paper proposes an improved AGIMM algorithm based on maneuverability of target. Firstly, divide the Maneuverability zones by Filter residuals. Secondly, according to different zones, the grid points are updated in different ways to speed up the convergence of the model when the target is high maneuvering, and reduce the fluctuation of the model when the target is low maneuvering. Realize the model update method according to the target maneuverability adaptive adjustment. The simulation shows that the improved algorithm significantly reduces the tracking error regardless of whether the target turns or not.

Recently, targets tracking plays an important role in the rapidly expanding field of autonomous driving. With the presentation of vehicle-to-vehicle (V2V) communication standards and the improvement of Global Navigation Satellite System (GNSS) technology, the accuracy of cooperative vehicle tracking should be greatly improved. However, high-precision positioning suffers from a number of vulnerabilities and is unstable in clutter environment. In this paper, a modified approach using particle filter (PF) integrated with Interacting Multiple Models (IMM) algorithm is proposed to improve the accuracy of cooperative vehicle tracking in changing V2V environment. This paper focuses on a novel framework for cooperative vehicle tracking and implementation of data fusion originating from positioning information receiving from radars and connected vehicles. The simulations on scenarios of single vehicle and multi-vehicle tracking were implemented by utilizing Matlab. The performance of the proposed modified IMM-PF algorithm was evaluated. The results demonstrated that the proposed approach can adapt to a changing accuracy of positioning information and significantly reduce the tracking errors by approximately 50% compared with traditional methods. Consequently, the proposed method can effectively enhance the security and reliability of autonomous driver assistance systems, and its application has potential to support urban planning for smart cities.