Hydra — A 3-dimensional electron and ion hot plasma instrument for the POLAR spacecraft of the GGS mission

HYDRA is an experimental hot plasma investigation for the POLAR spacecraft of the GGS program. A consortium of institutions has designed a suite of particle analyzers that sample the velocity space of electron and ions between 2 keV/q – 35 keV/q in three dimensions, with a routine time resolution of 0.5 s. Routine coverage of velocity space will be accomplished with an angular homogeneity assumption of 16°, appropriate for subsonic plasmas, but with special 1.5° resolution for electrons with energies between 100 eV and 10 keV along and opposed to the local magnetic field. This instrument produces 4.9 kilobits s^–1 to the telemetry, consumes on average 14 W and requires 18.7 kg for deployment including its internal shielding. The scientific objectives for the polar magnetosphere fall into four broad categories: (1) those to define the ambient kinetic regimes of ions and electrons; (2) those to elucidate the magnetohydrodynamic responses in these regimes; (3) those to assess the particle populations with high time resolution; and (4) those to determine the global topology of the magnetic field. In thefirst group are issues of identifying the origins of particles at high magnetic latitudes, their energization, the altitude dependence of the forces, including parallel electric fields they have traversed. In thesecond group are the physics of the fluid flows, regimes of current, and plasma depletion zones during quiescent and disturbed magnetic conditions. In thethird group is the exploration of the processes that accompany the rapid time variations known to occur in the auroral zone, cusp and entry layers as they affect the flow of mass, momentum and energy in the auroral region. In thefourth class of objectives are studies in conjunction with the SWE measurements of the Strahl in the solar wind that exploit the small gyroradius of thermal electrons to detect those magnetic field lines that penetrate the auroral region that are directly open to interplanetary space where, for example, the Polar Rain is observed.     

Multiposition alignment of strapdown inertial navigation system

The authors demonstrate that the stationary alignment of strapdown inertial navigation system (SDINS) can be improved by employing the multiposition/technique. Using an observability analysis, it is shown that an optimal two-position alignment not only satisfies complete observability conditions but also minimizes alignment errors. This is done by analytic rank testing of the stripped observability matrix and numerical calculation of the error covariance. It is also shown that an optimal three-position alignment accelerates the convergence of the alignment error compared with two-position alignment     

Optimum gain control for A/D conversion using digitized I/Q data inquadrature sampling

Calculation of optimum gain for minimum distortion due to A/D (analog-to-digital) conversion requires the estimation of the input signal strength. To use a common AGC (automatic gain control) for both the I/Q (in-phase and quadrature) signals, it is efficient to estimate the input signal strength using the quantized A/D output from both channels. Assuming a Gaussian input, the relationship between σ of the input of the A/D converter and E(|x|+|y|) and E(max(|x|,|y|)+1/2 min (|x|,|y|)) for t quantized I/Q output x and y is derived. Numerical results obtained using the derived expression and the statistical data obtained through simulation show excellent agreement. It is concluded that, because of its simplicity, the cubic equation obtained by fitting the numerical results should be useful     

Comments on `Tracking a maneuvering target using input estimation''

P.L. Boglar (see ibid., vol.AES-23, no.3, p.298-310, May 1987) developed a recursive formulation of the Chan, Hu, and Plant (CHP) algorithm, and the results of the simulation verified the performance improvements. The commenter claims that Bogler obtained an incorrect recursive formulation of input estimation because he incorrectly used the formulation of the Kalman filter. The commenter presents calculations that aim to demonstrate this claim     

Adaptive Tracking Filter for Maneuvering Targets

A general method of continually restructuring an optimum Bayes-Kalman tracking filter is proposed by conceptualizing a growing tree of filters to maintain optimality on a target exhibiting maneuver variables. This tree concept is then constrained from growth by quantizing the continuously sensed maneuver variables and restricting these to a small value from which an average maneuver is calculated. Kalman filters are calculated and carried in parallel for each quantized variable. This constrained tree of several parallel Kalman filters demands only modest om; puter time, yet provides very good performance. This concept is implemented for a Doppler tracking system and the performance is compared to an extended Kalman filter. Simulation results are presented which show dramatic tracking improvement when using the adaptive tracking filter.     

Angular Accuracy of a Scanning Radar Employing a Two-Pole Filter

A two-pole filter is proposed as a detector for a scanning radar. The optimum values of the filter coefficients are found and are approximated by a simple expression. The optimum two-pole filter requires a 0.15-dB increase in signal-to-noise ratio in order to provide the same detection capability as the optimum detector, and yields azimuth estimates whose standard deviation are within 15 percent of the Cramér-Rao lower bound. The estimator is simple to implement, avoiding the storage requirements of the moving window detector and the bias complications of the feedback integrator.     

On Optimal Control of Linear Systems in the Presence of Multiplicative Noise

This correspondence considers the problem of optimal regulator design for discrete time linear systems subjected to white state-dependent and control-dependent noise in addition to additive white noise in the input and the observations. A pseudo-deterministic problem is first defined in which multiplicative and additive input disturbances are present, but noise-free measurements of the complete state vector are available. This problem is solved via discrete dynamic programing. Next is formulated the problem in which the number of measurements is less than that of the state variables and the measurements are contaminated with state-dependent noise. The inseparability of control and estimation is brought into focus, and an "enforced separation" solution is obtained via heuristic reasoning in which the control gains are shown to be the same as those in the pseudo-deterministic problem. An optimal linear state estimator is given in order to implement the controller.     

Ground Testing of Inertial Navigators to Improve Dynamic Performance

A ground testing method has been devised to evaluate the dynamic errors of an inertial navigation system. A trimmed stationary inertial system in the navigation mode can be subjected to programmed platform orm drift rates, and generate position outputs which are compared with those of a perfect navigator. The linearized error equations for this testing mode are derived and the resulting position error propagation is analyzed using Kalman filtering in order to identify the error sources. A simulated platform with a fixed set of error sources is analyzed to evaluate this testing concept. An example is presented to show that the gyro and accelerometer scale factor and misalignment error coefficients can be estimated.