Ownship Maneuver Research Articles

Formally Verified Next-generation Airborne Collision Avoidance Games in ACAS X

The design of aircraft collision avoidance algorithms is a subtle but important challenge that merits the need for provable safety guarantees. Obtaining such guarantees is nontrivial given the unpredictability of the interplay of the intruder aircraft decisions, the ownship pilot reactions, and the subtlety of the continuous motion dynamics of aircraft. Existing collision avoidance systems, such as TCAS and the Next-Generation Airborne Collision Avoidance System ACAS X, have been analyzed assuming severe restrictions on the intruder’s flight maneuvers, limiting their safety guarantees in real-world scenarios where the intruder may change its course. This work takes a conceptually significant and practically relevant departure from existing ACAS X models by generalizing them to hybrid games with first-class representations of the ownship and intruder decisions coming from two independent players, enabling significantly advanced predictive power. By proving the existence of winning strategies for the resulting Adversarial ACAS X in differential game logic, collision-freedom is established for the rich encounters of ownship and intruder aircraft with independent decisions along differential equations for flight paths with evolving vertical/horizontal velocities. We present three classes of models of increasing complexity: single-advisory infinite-time models, bounded time models, and infinite time, multi-advisory models. Within each class of models, we identify symbolic conditions and prove that there then always is a possible ownship maneuver that will prevent a collision between the two aircraft.

Doppler-bearing Passive Target Tracking Using a Parameterized Unscented Kalman Filter

Target motion analysis (TMA) using conventional passive bearing together with frequency measurements is explored. This approach offers one tactical advantage over the classical bearings-only TMA. It makes the ownship maneuver superfluous. In this paper, TMA is carried out using an unscented Kalman filter (UKF). The inclusion of range, course, and speed parameterization is proposed in the UKF target state vector to obtain the convergence of the solution fast. Finally the results of one scenario in Monte-Carlo simulation are presented.

Bearings-only tracking of maneuvering targets using a batch-recursive estimator

We present a new batch-recursive estimator for tracking maneuvering targets from bearings-only measurements in clutter (i.e., for low signal-to-noise ratio (SNR) targets), Standard recursive estimators like the extended Kalman Iter (EKF) suffer from poor convergence and erratic behavior due to the lack of initial target range information, On the other hand, batch estimators cannot handle target maneuvers. In order to rectify these shortcomings, we combine the batch maximum likelihood-probabilistic data association (ML-PDA) estimator with the recursive interacting multiple model (IMM) estimator with probabilistic data association (PDA) to result in better track initialization as well as track maintenance results in the presence of clutter. It is also demonstrated how the batch-recursive estimator can be used for adaptive decisions for ownship maneuvers based on the target state estimation to enhance the target observability. The tracking algorithm is shown to be effective for targets with 8 dB SNR.

Passive sonar fusion for submarine C/sup 2/ systems

The most important sensors for gathering target information onboard a submarine are passive sonars. Problems concerning fusion of these passive sonars are discussed. Three typical passive sonars-passive noise sonar, passive ranging sonar and acoustic pulse surveillance sonar-constitute a passive sonar system for data fusion. This paper is concerned mainly with problems of significance in system development, such as tactical application background, special fusion techniques and own-ship maneuver considerations.

TMA from bearings and multipath time delays

A novel approach for target motion analysis (TMA), which uses conventional passive bearing together with multipath time-delay measurements is examined. This so-called Multipath TMA offers two tactical advantages over the classical bearings-only TMA: no requirement for any ownship maneuver, and a good performance in terms of estimation error achieved in a shorter time. Both known and unknown multipath cases are addressed. Finally, Monte-Carlo simulations and at-sea trials demonstrate the practical efficiency of such a multipath TMA.

Bearings-Only Tracking for Maneuvering Targets in Clutter

In recursive bearings-only tracking, the Cartesian coordinate Extended Kalman Filter or the modified polar coordinate filter provide poor convergence and erratic behavior due to the lack of initial target range information required for proper filter initialization. While the advantage of a recursive estimator is that it allows for models which account for accelerations of maneuvering targets, it cannot be relied upon because of initialization problems. The maximum likelihood estimator combined with the probabalistic data association, ML/PDA, has been shown to be an effective estimator for tracking non-maneuvering targets in clutter. This batch method is shown to provide the accurate initialization required for recursive filters once the range observability is sufficient following an ownship manuever. Furthermore, the capability of handling maneuvers and clutter is obtained by using a recursive Interacting Multiple Model Probabalistic Data Association with Amplitude Information (IMMPDAFAI) following the batch ML/PDA initialization. The resulting filter, called the Batch/Recursive IMMPDAFAI (BRIMM) is shown to provide reliable track initialization as well as accurate state estimation through target maneuvers in the presence of clutter. Subsequently, it is demonstrated how the IMMPDAFAI can be used for adaptive decisions about ownship maneuvers based upon the target state estimation to enhance the target observability after a target maneuver has occurred.

Observability Requirements for Three-Dimensional Tracking via Angle Measurements

Observability requirements previously established for bearings-only tracking in two dimensions are extended to a class of three-dimensional estimation algorithms capable of processing any pairwise combination of azimuth bearing, conical bearing, and depth/elevation angle measurements. Although these algorithms are intrinsically nonlinear, it is shown that they can be analyzed in a linear framework without sacrificing mathematical rigor. A simplified observability criterion, applicable to both autonomous and nonautonomous linear systems, is presented and utilized to specify conditions on own-ship motion which are both necessary and sufficient for a unique tracking solution. Further analysis reveals that observability dependence on own-ship maneuvers for the three-dimensional algorithms considered here parallels the concomitant two-dimensional requirements. An interesting difference, however, is that under certain conditions, a unique tracking solution can be obtained in three dimensions for unaccelerated own-ship motion.

Biased Estimation Properties of the Pseudolinear Tracking Filter

Estimation bias in the pseudolinear filter applied to bearings-only target tracking is discussed. Approximate expressions for the pertinent error terms are developed and subsequently used to predict tracking performance under realistic operating conditions. It is shown that once own-ship executes a maneuver, only the estimated range vector remains biased; the corresponding velocity vector becomes asymptotically unbiased. Further investigation reveals that this range bias is highly dependent upon geometry and can be altered by additional own-ship maneuvers. Experimental data are presented to support these findings.