An Adaptive Interference Canceling Array Utilizing Hybrid Techniques

The steady state properties of an adaptive array utilizing prior knowledge of both approximate signal arrival direction and signal characteristics are presented. The method combines the features of a directionally constrained array and an array with a self-generated reference signal. Explicit results are obtained for output signal, interference, and noise powers assuming a single interferer is present. The inclusion of a self-generated reference circuit is shown to reduce the sensitivity to pointing error typical of arrays utilizing a zero order directional constraint, the improvement being a consequence of the reduction of the desired signal component fed back to the sidelobe canceling circuit. A relationship between the degree of sensitivity reduction and the quality of the reference signal is developed. Results of computations of signal to interference plus noise ratios for a 7-element 10-wavelength nonuniformly spaced array as a function of pointing error are presented. These results show the behavior with one interferer inside and outside the beamwidth of the quiescent array and with multiple interferers for various degrees of perfection of the reference generating circuit. In all cases the computations confirm that the otherwise severe effects of small pointing errors are substantially reduced.     

Track labeling and PHD filter for multitarget tracking

Multiple target tracking requires data association that operates in conjunction with filtering. When multiple targets are closely spaced, the conventional approaches (as, e.g., MHT/assignment) may not give satisfactory results. This is mainly because of the difficulty in deciding what the number of targets is. Recently, the probability hypothesis density (PHD) filter has been proposed and particle filtering techniques have been developed to implement the PHD filter. In the particle PHD filter, the track labeling problem is not considered, i.e., the PHD is obtained only for a frame at a time, and it is very difficult to perform the multipeak extraction, particularly in high clutter environments. A track labeling method combined with the PHD approach, as well as considering the finite resolution, is proposed here for multitarget tracking, i.e., we keep a separate tracker for each target, use the PHD in the resolution cell to get the estimated number and locations of the targets at each time step, and then perform the track labeling ("peak-to-track" association), whose results can provide information for PHD peak extraction at the next time step. Besides, by keeping a separate tracker for each target, our approach provides more information than the standard particle PHD filter. For example, in group target tracking, if we are interested in the motion of a specific target, we can track this target, which is not possible for the standard particle PHD filter, since the standard particle PHD filter does not keep track labels. Using our approach, multitarget tracking can be performed with automatic track initiation, maintenance, spawning, merging, and termination     

Critical Issues on Magnetic Reconnection in Space Plasmas

The idea of expedient energy transformation by magnetic reconnection (MR) has generated much enthusiasm in the space plasma community. The early concept of MR, which was envisioned for the solar flare phenomenon in a simple two-dimensional (2D) steady-state situation, is in dire need for extension to encompass three-dimensional (3D) non-steady-state phenomena prevalent in space plasmas in nature like in the magnetosphere. A workshop was organized to address this and related critical issues on MR. The essential outcome of this workshop is summarized in this review. After a brief evaluation on the pros and cons of existing definitions of MR, we propose essentially a working definition that can be used to identify MR in transient and spatially localized phenomena. The word “essentially” reflects a slight diversity in the opinion on how transient and localized 3D MR process might be defined. MR is defined here as a process with the following characteristics: (1) there is a plasma bulk flow across a boundary separating regions with topologically different magnetic field lines if projected on the plane of MR, thereby converting magnetic energy into kinetic particle energy, (2) there can be an out-of-the-plane magnetic field component (the so-called guide field) present such that the reconnected magnetic flux tubes are twisted to form flux ropes, and (3) the region exhibiting non-ideal MHD conditions should be localized to a scale comparable to the ion inertial length in the direction of the plasma inflow velocity. This definition captures the most important 3D aspects and preserves many essential characteristics of the 2D case. It may be considered as the first step in the generalization of the traditional 2D concept. As a demonstration on the utility of this definition, we apply it to identify MR associated with plasma phenomena in the dayside magnetopause and nightside magnetotail of the Earth’s magnetosphere. How MR may be distinguished from other competing mechanisms for these magnetospheric phenomena are then discussed.This revised version was published online in July 2005 with a corrected cover date.     

IMM estimator with out-of-sequence measurements

In multisensor tracking systems that operate in a centralized information processing architecture, measurements from the same target obtained by different sensors can arrive at the processing center out of sequence. In order to avoid either a delay in the output or the need for reordering and reprocessing an entire sequence of measurements, such measurements have to be processed as out-of-sequence measurements (OOSMs). Recent work developed procedures for incorporating OOSMs into a Kalman filter (KF). Since the state of the art tracker for real (maneuvering) targets is the interacting multiple model (IMM) estimator, the algorithm for incorporating OOSMs into an IMM estimator is presented here. Both data association and estimation are considered. Simulation results are presented for two realistic problems using measurements from two airborne GMTI sensors. It is shown that the proposed algorithm for incorporating OOSMs into an IMM estimator yields practically the same performance as the reordering and in-sequence reprocessing of the measurements. Also, it is shown how the range rate from a GMTI sensor can be used as a linear velocity measurement in the tracking filter.     

Mixed H/sub 2//H/sub /spl infin// filtering for uncertain systems with regional pole assignment

The mixed H/sub 2//H/sub /spl infin// filtering problem for uncertain linear continuous-time systems with regional pole assignment is considered. The purpose of the problem is to design an uncertainty-independent filter such that, for all admissible parameter uncertainties, the following filtering requirements are simultaneously satisfied: 1) the filtering process is asymptotically stable; 2) the poles of the filtering matrix are located inside a prescribed region that compasses the vertical strips, horizontal strips, disks, or conic sectors; 3) both the H/sub 2/ norm and the H/sub /spl infin// norm on the respective transfer functions are not more than the specified upper bound constraints. We establish a general framework to solve the addressed multiobjective filtering problem completely. In particular, we derive necessary and sufficient conditions for the solvability of the problem in terms of a set of feasible linear matrix inequalities (LMIs). An illustrative example is given to illustrate the design procedures and performances of the proposed method.     

IMM estimator versus optimal estimator for hybrid systems

The special feature of the interacting multiple model (TMM) estimator that distinguishes it from other suboptimal multiple model (MM) estimators is the "mixing/interaction" between its "mode-matched" base state filtering modules at the beginning of each cycle. This note shows that the same feature is exactly what it has in common with the optimal estimator for hybrid (MM) systems and this can be seen as the main reason for its success.     

THEMIS Ground-Based Magnetometers

The THEMIS mission includes a comprehensive ground-based measurement network that adds two additional dimensions to the information gained in the night magnetosphere by the five THEMIS spacecraft. This network provides necessary correlative data on the strength and extent of events, enables their onsets to be accurately timed, and provides an educational component in which students have an active participation in the program. This paper describes the magnetometers installed to obtain these ground-based North American magnetic measurements, including the magnetometers installed as part of the educational effort, and the support electronics provided by UCLA for the ground-based observatories. These magnetometers measure the Earth’s magnetic field with high resolution, and with precise timing provided by the Global Positioning System. They represent UCLA’s next generation of low-cost, ground-based magnetometers using an inexpensive personal computer for data collection, storage and distribution. These systems can be used in a stand-alone mode requiring only AC power. If there is Internet connectivity, they can be configured to provide near real-time data over the web. These data are provided at full resolution to the entire scientific community over the web with minimal delay.     

The THEMIS Magnetic Cleanliness Program

The five identical THEMIS Spacecraft, launched in February 2007, carry two magnetometers on each probe, one DC fluxgate (FGM) and one AC search coil (SCM). Due to the small size of the THEMIS probes, and the short length of the magnetometer booms, magnetic cleanliness was a particularly complex task for this medium sized mission. The requirements leveled on the spacecraft and instrument design required a detailed approach, but one that did not hamper the development of the probes during their short design, production and testing phase. In this paper we describe the magnetic cleanliness program’s requirements, design guidelines, program implementation, mission integration and test philosophy and present test results, and mission on-orbit performance.     

Interacting multiple model methods in target tracking: a survey

The Interacting Multiple Model (IMM) estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The main feature of this algorithm is its ability to estimate the state of a dynamic system with several behavior modes which can “switch” from one to another. In particular, the IMM estimator can be a self-adjusting variable-bandwidth filter, which makes it natural for tracking maneuvering targets. The importance of this approach is that it is the best compromise available currently-between complexity and performance: its computational requirements are nearly linear in the size of the problem (number of models) while its performance is almost the same as that of an algorithm with quadratic complexity. The objective of this work is to survey and put in perspective the existing IMM methods for target tracking problems. Special attention is given to the assumptions underlying each algorithm and its applicability to various situations