Murali Yeddanapudi

A generalized S-D assignment algorithm for multisensor-multitarget state estimation

We develop a new algorithm to associate measurements from multiple sensors to identify the real targets in a surveillance region, and to estimate their states at any given time. The central problem in a multisensor-multitarget state estimation problem is that of data association-the problem of determining from which target, if any, a particular measurement originated. The data association problem is formulated as a generalized S-dimensional (S-D) assignment problem, which is NP-hard for S/spl ges/3 sensor scans (i.e., measurement lists). We present an efficient and recursive generalized S-D assignment algorithm (S/spl ges/3) employing a successive Lagrangian relaxation technique, with application to the localization of an unknown number of emitters using multiple high frequency direction finder sensors (S=3, 5, and 7).

IMM estimation for multitarget-multisensor air traffic surveillance

This paper deals with the design and implementation of an algorithm for track formation and maintenance in a multisensor air traffic surveillance (ATS) scenario. The major contribution of the present work is the development of the combined likelihood function that enables the replacement of the Kalman filter (KF) with the much more versatile interacting multiple model (IMM) estimator which accounts for the various motion modes of the aircraft. This likelihood function defines the objective function used in the measurement to track assignment algorithm. Data from two FAA radars are used to evaluate the performance of this algorithm. The use of the IMM estimator yields considerable noise reduction during uniform motion, while maintaining the accuracy of the state estimates during maneuver. Overall, the mean square prediction error (to the next observation time) is reduced by 30% and the RMS errors in the altitude rate estimates are reduced by a factor of 3 over the KF. The usefulness of the tracker presented here is also demonstrated on a non-cooperative target.

Trajectory and launch point estimation for ballistic missiles from boost phase LOS measurements

This paper addresses the problem of estimating the trajectory and the launch point of a tactical ballistic missile using line of sight (LOS) measurements from one or more passive sensors (typically satellite-borne), The major difficulties of this problem include the ill-conditioning of the estimation problem due to poor observability of the target motion via LOS measurements, the estimation of the unknown launch time, and the incorporation of inaccurate target thrust profiles to model the target dynamics during the boost phase. We present a maximum likelihood (ML) estimator based on the Levenberg-Marquardt algorithm that provides both the target state estimate and the associated error covariance, taking into consideration the complications mentioned above. One important consideration in the defense against tactical ballistic missiles (TBM) is the determination of the target position and error covariance at the acquisition range of a surveillance radar located in the vicinity of the impact point. We present a systematic procedure to propagate the target state and covariance to a nominal time, when it is within the detection range of a surveillance radar to obtain a cueing region. We also provide an estimate and the error covariance of the (two dimensional) launch position, which can be used to search for the missile launch site. Monte Carlo simulation studies on typical single and multiple sensor scenarios indicate that the proposed algorithms are accurate in terms of the estimates and that the estimator calculated covariances are consistent with the errors.

MATSurv: multisensor air traffic surveillance system

This paper deals with the design and implementation of MATSurv 1--an experimental Multisensor Air Traffic Surveillance system. The proposed system consists of a Kalman filter based state estimator used in conjunction with a 2D sliding window assignment algorithm. Real data from two FAA radars is used to evaluate the performance of this algorithm. The results indicate that the proposed algorithm provides a superior classification of the measurements into tracks (i.e., the most likely aircraft trajectories) when compared to the aircraft trajectories obtained using the measurement IDs (squawk or IFF code).

Ballistic missile track initiation from satellite observations

This paper presents an algorithm to initiate tracks of a ballistic missile in the initial exoatmospheric phase, using line of sight measurements from one or more moving platforms (typically satellites). The major feature of this problem is the poor target motion observability which results in a very ill-conditioned estimation problem.

A generalized S-dimensional algorithm for multisensor multitarget state-estimation

Presents a fast near-optimal assignment algorithm to solve a generalized multidimensional assignment problem. Such problems arise in surveillance systems estimating the position of an unknown number of targets. The central problem in a multisensor-multitarget state estimation problem is that of data association-the problem of determining from which target, if any, a particular measurement originated. The data-association problem is formulated as a generalized S-dimensional (S-D) assignment problem, which is NP-hard for 3 or more sensor scans (S/spl ges/3). In this paper, the authors present an efficient and recursive generalized S-D assignment algorithm (S/spl ges/3) with application to the localization of unknown number of emitters using multiple high frequency direction finders.<<ETX>>

Trajectory prediction for ballistic missiles based on boost-phase LOS measurements

This paper addresses the problem of the estimation of the trajectory of a tactical ballistic missile using line of sight (LOS) measurements from one or more passive sensors (typically satellites). The major difficulties of this problem include: the estimation of the unknown time of launch, incorporation of (inaccurate) target thrust profiles to model the target dynamics during the boost phase and an overall ill-conditioning of the estimation problem due to poor observability of the target motion via the LOS measurements. We present a robust estimation procedure based on the Levenberg-Marquardt algorithm that provides both the target state estimate and error covariance taking into consideration the complications mentioned above. An important consideration in the defense against tactical ballistic missiles is the determination of the target position and error covariance at the acquisition range of a surveillance radar in the vicinity of the impact point. We present a systematic procedure to propagate the target state and covariance to a nominal time, when it is within the detection range of a surveillance radar to obtain a cueing volume. Mont Carlo simulation studies on typical single and two sensor scenarios indicate that the proposed algorithms are accurate in terms of the estimates and the estimator calculated covariances are consistent with the errors.