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This article addresses the opportunistic use of satellite downlink attenuation measurements for the site-specific estimation of rainfall rate. In particular, this article proposes a regression model characterized by the introduction of correction elements extracted from the frequency-domain analysis of the attenuation time series, and the use of a simple linear regression function with a small modification, which allows to using a negative intercept with the goal of minimizing the maximum error (large underestimation/overestimation). The performance, assessed using attenuation measurements of a satellite downlink in Q/V band shows that the proposed model allows to estimate the rainfall rate with low average error and low maximum error with respect to a more standard ITU model, so as to offer more reliable estimations. [ABSTRACT FROM AUTHOR]

Polynomial interpolation is widely used in channel modeling for aerospace tracking, telemetry, and command channel simulators, to accurately regenerate the satellite motion parameters with a high sampling rate from the available low sampling rate motion data. In this paper, a novel piecewise osculating interpolation approach is proposed to improve modeling precision. It uses the available distance and its higher-order derivatives at each pair of adjacent knots to construct the piecewise interpolation polynomial, thus achieving a better precision and continuity of the resulting motion parameters. Moreover, it is efficiently implemented with an interpolation filter based on a modified Farrow structure, and the designed filter can interpolate the motion data obtained both through uniform or nonuniform sampling. Additionally, a sampling optimization algorithm is proposed. It optimizes the motion data sampling by minimizing the maximum interpolation error given the average sampling interval and interpolation polynomial order, and therefore, improves the precision further without increasing the costs. The validity of the proposed approach is verified by numerical simulation, and its advantages are demonstrated by comparison with existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we study the level set estimation of a spatial-temporally correlated random field by using a small number of spatially distributed sensors. The level sets of a random field are defined as regions where data values exceed a certain threshold. The identification of the boundaries of such sets is an important theoretical problem with a wide range of applications such as spectrum sensing, urban sensing, and environmental monitoring, etc. We propose a new active sparse sensing and inference scheme, which can achieve rapid and accurate extraction of level sets in a large random field by using a small number of data samples strategically and sparsely selected from the field. A Gaussian process (GP) prior model is used to capture the spatial–temporal correlations inherent in the random field. It is first shown that the optimal level set estimation can be achieved by performing a GP regression with all data samples and then thresholding the regression results. We then investigate the active sparse sensing scheme, where a central controller dynamically selects a small number of sensing locations according to the information revealed from past measurements, with the objective to minimize the expected level set estimation error probability. The expected estimation error probability is explicitly expressed as a function of the selected sensing locations, and the results are used to formulate the optimal sensing location selection problem as a combinatorial problem. Two low complexity greedy algorithms are developed by using analytical upper bounds of the expected estimation error probability. Both simulation and experiment results demonstrate that the greedy algorithms can achieve significant performance gains over baseline passive sensing algorithms and the GP upper confidence bound level set estimation algorithm. [ABSTRACT FROM PUBLISHER]

In the direct sequence spread spectrum (DSSS) system, multiple-access interference mitigation is critical to improving the acquisition performance. Therefore, in this paper, a fast cross-correlation mitigation algorithm based on the minimum mean-square error criterion is proposed. The algorithm exploits the matched filter outputs; hence, it is applicable to existing DSSS receivers based on direct pseudorandom noise code correlation. Essentially, the proposed scheme is an adaptive filter that optimizes each individual uncertain code phase delay. The algorithm employs both the code sequence information of the desired signal and that of the multiple-access interference signal, which enables it to outperform the standard matched filter, conventional least-mean-squares algorithm, and the pulse compression repair method. Furthermore, the proposed algorithm decreases the computational load compared with the existing multistatic adaptive pulse compression method by reducing the stage-number of the adaptive filter; this results in a slight degradation in performance. Numerical results verify the validity of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper describes the sensitivity analysis concerning time-to-go estimation error for the class of optimal impact angle control guidance laws under a stationary or a slowly moving target. First, in this paper, the closed-form solutions of those guidance laws for a lag-free system are determined. Then, we derive the explicit expressions of the terminal errors (i.e., miss distance and impact angle error) in the presence of a constant positive time-to-go estimation error. Based on these results, the guidance parameters that can help reduce the sensitivity to time-to-go estimation error are identified. Additionally, the terminal performance of the presented optimal impact angle guidance laws is evaluated from the standpoint of robustness against the time-to-go estimation errors. Numbers of linear homing simulations are also performed to confirm the analysis results. [ABSTRACT FROM AUTHOR]

Attitude control is investigated for a rigid spacecraft using single gimbal control moment gyros (SGCMGs) as actuators. By taking spacecraft and SGCMGs as an integrated system, the combined dynamics of SGCMGs and spacecraft are fully considered. The singularity problem of the SGCMGs is converted into the problem of state constraints and input constraints, and by so doing, the actuation capacity of the SGCMGs is fully utilized. To ensure accurate real-time computation, the Gauss pseudospectral and sequential quadratic programming methods are used to solve the attitude path planning of the system. Considering the uncertainties of the external disturbances and the estimation error of the inertia matrix, feedback control of the system is designed by using nonlinear model predictive control. Finally, dual-mode model predictive control is used to ensure the stability of the system, and the H8 controller and the estimated hyperellipsoid region of attraction of the linearized system are given. Simulation results show good tracking performance and robustness of the designed controller. The modeling method and the control method given in the paper provide a solution to the singularity problem of the SGCMGs. [ABSTRACT FROM AUTHOR]

The problem of estimating the angular rate of a satellite is considered. A nonlinear observer based on the state-dependent Riccati equation method is proposed. A sufficient stability condition for the convergence of the estimation error is presented. This condition is related to a state-dependent algebraic Riccati equation. The Riccati equation is derived by transforming nonlinear error dynamics into a Lipschitz nonlinearity. Observer gains are obtained from this state-dependent algebraic Riccati equation. Numerical simulations are presented to demonstrate the proposed method. [ABSTRACT FROM PUBLISHER]

This paper presents a sensor-control method for choosing the best next state of the sensors that provide accurate estimation results in a multitarget tracking application. The proposed solution is formulated for a multi-Bernoulli filter and works via minimization of a new estimation-error-based cost function. Simulation results demonstrate that the proposed method can outperform the state-of-the-art methods in terms of computation time and robustness to clutter while delivering similar accuracy. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of locating a target node (TN) moving along a corridor in a large industrial environment by means of ultrawide band signaling from fixed anchor nodes (ANs) uniformly positioned at the same height on both sides of the corridor. For a representative geometry of a large indoor (industrial) scenario, we formulate an analytical approach to the optimized placement (in terms of internode distance) of ANs using the criterion of minimizing the average mean square error (MSE) in the time-difference-of-arrival-based estimated positions of the TN. Under the assumption of a fixed variance of the range estimation error, we derive a simple closed-form expression for the optimal inter-AN distance in terms of the corridor width and the height of the ANs. The effectiveness of the analytical approach is confirmed by simulations. We also show that the proposed approach allows the MSE in the TN position estimates to reach the Cramer Rao lower bound. [ABSTRACT FROM AUTHOR]

The work presented in this paper makes two contributions for exploiting Doppler (range rate) measurements in tracking systems. First, a new linear filter, the converted Doppler measurement Kalman filter (CDMKF), is presented to extract nonlinear pseudostates from converted Doppler measurements (i.e., the product of the range measurements and Doppler measurements). The pseudostates are constructed from the converted Doppler and its derivatives. The linearly evolving equations of the pseudostates are derived for common target motion models. The second contribution of this paper is using the CDMKF along with the converted position measurement Kalman filter (CPMKF), in which only the position measurements are used, to establish a new filtering structure, statically fused converted measurement Kalman filters (SF-CMKF). The resulting states of CPMKF and CDMKF are combined by a static minimum mean squared error (MMSE) estimator, where the nonlinearity and correlation between the pseudostates and the Cartesian states are handled simultaneously, to yield the final state estimates. The dynamic nonlinear estimation problem is converted into dynamic linear estimation followed by static nonlinear fusion. The estimation accuracy can be enhanced by incorporating the Doppler measurements via the linear CDMKF, while the filtering stability can be improved by dealing with nonlinearity outside the filtering recursions. Monte Carlo simulations and comparison with the posterior Cramer-Rao bound demonstrate the effectiveness of the CDMKF and SF-CMKF. [ABSTRACT FROM PUBLISHER]

The accurate determination of the steering vector of a sensor array that corresponds to a desired signal is often hindered by uncertainties due to array imperfections, such as the presence of a direction-of-arrival (DOA) estimation error, mutual coupling, array sensor gain/phase uncertainties, and senor position perturbations. Consequently, the performance of conventional beamforming algorithms that use the nominal steering vector may be significantly degraded. A new method for recursively correcting possible deterministic errors in the estimated steering vector is proposed here. It employs the subspace principle and estimates the desired steering vector by using a convex optimization approach. We show that the solution can be obtained in closed form by using the Lagrange multiplier method. As the proposed method is based on an extended version of the conventional orthonormal PAST (OPAST) algorithm, it has low implementation complexity, and moving sources can be handled. In addition, a robust beamformer with a new error bound that uses the proposed steering vector estimate is derived by optimizing the worst case performance of the array after taking the uncertainties of the array covariance matrix into account. This gives a diagonally loaded Capon beamformer, where the loading level is related to the bound of the uncertainty in the array covariance matrix. Numerical results show that the proposed algorithm performs well, especially at high signal-to-noise ratio (SNR) and in the presence of deterministic sensor uncertainties. [ABSTRACT FROM PUBLISHER]