A simple fixed-lag algorithm for tracking frequency rate-of-change

A family of simple fixed-lag frequency smoothing algorithms which provide good estimates of both frequency and frequency rate-of-change is reported. The smoothers were developed from a fixed-gain αβ tracker by replacing the recursive derivative estimator with a finite-impulse-response (FIR) differentiator. Simulation results are presented which show that the smoothing algorithms provide frequency estimates with a similar variance to those produced by the αβ filter but with greatly improved frequency-rate estimates. The smoothing algorithms and the αβ filter are also compared on the basis of the bias and delay introduced in the frequency-rate estimates. Although the results presented are for frequency estimation, the smoothing algorithms can be used in any single-input tracking application where some lag in the estimates is allowable     

Estimators of Log-Normal Distribution Parameters

The maximum likelihood estimates of the median m and mean-to-median ratio ? for a log-normally distributed variate are derived, together with the asymptotic variances of these estimates. Using a Monte Carlo simulation, it is shown that these asymptotic variances of the estimates are reasonable when the number of sample points is N ? 50forp ? 8.     

False alarm control using Doppler estimation

A technique which uses maximum-likelihood estimates (MLEs) of target Doppler and target amplitude is developed for rejecting clutter residues. Multiple estimates are made and consistency checks are applied to the estimates. Simulation results indicate that for large clutter-to-noise ratios (C/N⩾55 dB) the probability of false alarm from clutter residues is reduced from 1.0 to below 0.01     

Impact of Geoid Improvement on Ocean Mass and Heat Transport Estimates

One long-standing difficulty in estimating the large-scale ocean circulation is the inability to observe absolute current velocities. Both conventional hydrographic measurements and altimetric measurements provide observations of currents relative to an unknown velocity at a reference depth in the case of hydrographic data, and relative to mean currents calculated over some averaging period in the case of altimetric data. Space gravity missions together with altimetric observations have the potential to overcome this difficulty by providing absolute estimates of the velocity of surface oceanic currents. The absolute surface velocity estimates will in turn provide the reference level velocities that are necessary to compute absolute velocities at any depth level from hydrographic data. Several studies have been carried out to quantify the improvements expected from ongoing and future space gravity missions. The results of these studies in terms of volume flux estimates (transport of water masses) and heat flux estimates (transport of heat by the ocean) are reviewed in this paper. The studies are based on ocean inverse modeling techniques that derive impact estimates solely from the geoid error budgets of forthcoming space gravity missions. Despite some differences in the assumptions made, the inverse modeling calculations all point to significant improvements in estimates of oceanic fluxes. These improvements, measured in terms of reductions of uncertainties, are expected to be as large as a factor of 2. New developments in autonomous ocean observing systems will complement the developments expected from space gravity missions. The synergies of in situ and satellite observing systems are considered in the conclusion of this paper. This revised version was published online in August 2006 with corrections to the Cover Date.     

Multitarget detection using Kalman-based average displacementestimator

The anomaly in the displacement estimates obtained from a low-level Kalman-based average displacement estimator is used to detect multiple targets in a forward-looking infrared (FLIR) scene. The displacement estimates originating from the targets could be associated with the detected multiple targets and an updated estimated position of the target could be obtained. This procedure could be repeated for all targets in the scene to obtain multitarget tracking. The behavior of the expected value of the displacement estimates as a function of the number of iterations is investigated. The behavior of the displacement error covariance matrix and the Kalman gain matrix are discussed as functions of the number of iterations     

On the resolvability of sinusoidal parameter estimates [andapplication to SAR target features]

The resolvability of 2-D (two-dimensional) sinusoidal parameter estimates is studied. These sinusoids describe the target features in SAR (synthetic aperture radar) applications. We analyze the resolvability by considering the frequency estimates of the sinusoids. Our results may be used by target classification algorithms to better classify radar targets in SAR applications     

A Fault-Tolerant Multisensor Navigation System Design

The problem of soft-failure tolerant estimation in navigationsystems composed of multiple inertial measurement clusters and oneor more reference sensors is addressed. A new approach ispresented that achieves containment of failed sensor data, andisolates the historic good data provided by the unfailed sensors.Multiple (local) estimates are computed where the estimates areconditioned on different subsets of the sensors. A statistical overlaptest is used to determine the validity of the local estimates, and afailed sensor can be identified from analysis of the invalid localestimates. After the time of detection the most accurate estimatebased on all but the failed sensor is identified. The results areapplied to a dual-inertial/Doppler radar navigation system andsimulation results are presented.     

Interacting acceleration compensation algorithm for trackingmaneuvering targets

The two-stage Kalman estimator has been studied for state estimation in the presence of random bias and applied to the tracking of maneuvering targets by treating the target acceleration as a bias vector. Since the target acceleration is considered a bias, the first stage contains a constant velocity motion model and estimates the target position and velocity, while the second stage estimates the target acceleration when a maneuver is detected, the acceleration estimate is used to correct the estimates of the first stage. The interacting acceleration compensation (IAC) algorithm is proposed to overcome the requirement of explicit maneuver detection of the two-stage estimator. The IAC algorithm is viewed as a two-stage estimator having two acceleration models: the zero acceleration of the constant velocity model and a constant acceleration model. The interacting multiple model (IMM) algorithm is used to compute the acceleration estimates that compensate the estimate of the constant velocity filter. Simulation results indicate the tracking performance of the IAC algorithm approaches that of a comparative IMM algorithm while requiring approximately 50% of the computations     

A non-Bayesian segmenting tracker for highly maneuvering targets

The segmenting track identifier (STI) is introduced as a new methodology for tracking highly maneuvering targets. This nonBayesian approach dynamically partitions a target track into a sequence of track segments, making hard estimates of when the target's maneuvering mode transitions occur, and then estimates the parameters of the target model for each segment. STI is compared with two variable structures interacting multiple model (VS-IMM) algorithms through simulations, where it is shown to have a three fold performance advantage in median absolute turn rate estimation errors, as well as better position estimation for very highly maneuvering targets. STI is also shown to outperform a Rauch-Tung-Striebel (RTS) fixed-interval smoother when estimates are retrospectively derived, and STI accurately characterize the temporal pattern of maneuvers.     

Absolute Electron Density Measurement Techniques in Hypersonic Wakes

New techniques have been evolved for the application of Langmuir probes to the measurement of electron densities in the wakes of hypersonic projectiles flown in ballistic ranges. These techniques concern probe cleanliness, minimization of flow disturbance, minimization of reflected shocks, and the effect of plasma potential. Electron density level estimates obtained with the probes in sphere wakes were in good agreement with electron density estimates derived from simultaneous measurements with a microwave interferometer.     

阵元位置误差有源估计方法研究

阐述了对阵列天线阵元位置误差进行的研究,首先在天线阵列所在平面设置一个校正源,利用该校正源对阵元位置误差引起的阵列流形误差进行估计,然后将同一校正源放在不同方位角对阵列流形误差进行估计,最后经过矩阵运算估计出阵元位置误差。仿真证明此方法的有效性。     

Angle Estimation in Amplitude Comparison Monopulse Systems

The problem of estimation of angle of arrival in amplitude comparison monopulse (ACM) radars in the presence of internally generated thermal noise is considered. A pulse-type radar is assumed and angular noise is postulated to be absent. In the treatment of the problem, explicit inclusion of various simple models for the pulse returns is made to draw a relationship between the a priori statistics of the echo signals and the ability to estimate the angle of arrival in ACM radars. A maximum likelihood analysis is made to determine the form of the estimates of angle of arrival for the various cases and the accuracy of these estimates is evaluated as a function of the signal-to-noise ratio per hit and of the number of hits. Although most of the estimates considered have already received attention, many of the results are believed to be new.     

Stability of Parameter Estimates for a Gaussian Process

Maximum-likelihood estimates for the levels of the mean value function and the covariance function of a Gaussian random process are investigated. The stability of these estimates is examined as the actual covariance function of the process deviates from the form assumed in the estimators. It is found that the time-bandwidth product for stationary processes represents an upper bound on the number of estimator terms that can be safely used when estimating with uncertainty about the process covariance function. This result is consistent with other interpretations of the time-bandwidth product and tempers the conclusion that, in principle, an infinite number of estimator terms can be used to obtain a perfect estimate of the covariance level. In practice, the estimate of the level can never be perfect, and the accuracy of the estimate depends on the observation interval. Finally, conditions are established to ensure asymptotic stability of the estimates and physical interpretations are presented.     

Observer-type Kalman innovation filter for uncertain linear systems

An observer-type of Kalman innovation filtering algorithm to find a practically implementable "best" Kalman filter, and such an algorithm based on the evolutionary programming (EP) optima-search technique, are proposed, for linear discrete-time systems with time-invariant unknown-but-hounded plant and noise uncertainties. The worst-case parameter set from the stochastic uncertain system represented by the interval form with respect to the implemented "best" filter is also found in this work for demonstrating the effectiveness of the proposed filtering scheme. The new EP-based algorithm utilizes the global optima-searching capability of EP to find the optimal Kalman filter and state estimates at every iteration, which include both the best possible worst case Interval and the optimal nominal trajectory of the Kalman filtering estimates of the system state vectors. Simulation results are included to show that the new algorithm yields more accurate estimates and is less conservative as compared with other related robust filtering schemes     

Bounds on Elevation Error Estimates of a Target in Multipath

The Cramer-Rao bound for an unbiased estimate of the elevation angle of a target in the presence of multipath is calculated for the symmetric (target and image symmetric about the elevation symmetry plane of antenna) and nonsymmetric cases for an antenna consisting of 21 elements. These bounds are compared to the maximum likelihood estimates and it is found that the rms error of the maximum likelihood estimate (which has a bias) is below the Cramer-Rao bound for unbiased estimates.     

Statistical estimation of navigation errors

An algorithm for estimating navigation system accuracy without the use of so-called ground truth data is presented. The estimates are expressed in terms of system covariance matrices and relative bias vectors; if one of the systems is a dead-reckoning (DR) or inertial system, the algorithm also estimates drift rate parameters. A summary of tests with US Navy ships backs up the results of Monte Carlo testing. The algorithm should also have applications outside the field of navigation     

Comparison of motion and stereo methods in passive ranging systems

The authors compare the estimates in passive ranging systems using motion and stereo approaches. It is shown that an integrated approach is necessary to provide better range estimates over a field-of-view (FOV) of interest in helicopter flight. The recursive approach for processing a sequence of stereo images, described together with a recursive motion algorithm (RMA), provides the basis for an integrated method to provide more accurate range information. Results based on motion sequences of stereo images are presented     

A quantization architecture for track fusion

Many practical multi-sensor tracking systems are based on some form of track fusion, in which local track estimates and their associated covariances are shared among sensors. Communication load is a significant concern, and the goal of this paper is to propose an architecture for low-bandwidth track fusion. The scheme involves intelligent scalar and vector quantization of the local state estimates and of the associated estimation error covariance matrices. Simulation studies indicate that the communication saving can be quite significant, with only minor degradation of track accuracy.     

Exact multisensor dynamic bias estimation with local tracks

An exact solution is provided for the multiple sensor bias estimation problem based on local tracks. It is shown that the sensor bias estimates can be obtained dynamically using the outputs of the local (biased) state estimators. This is accomplished by manipulating the local state estimates such that they yield pseudomeasurements of the sensor biases with additive noises that are zero-mean, white, and with easily calculated covariances. These results allow evaluation of the Cramer-Rao lower bound (CRLB) on the covariance of the sensor bias estimates, i.e., a quantification of the available information about the sensor biases in any scenario. Monte Carlo simulations show that this method has significant improvement in performance with reduced rms errors of 70% compared with commonly used decoupled Kalman filter. Furthermore, the new method is shown to be statistically efficient, i.e., it meets the CRLB. The extension of the new technique for dynamically varying sensor biases is also presented.     

Instrumental variable adaptive array processing

An instrumental variable (IV) approach is presented for estimating the weights of an adaptive antenna array. Theoretical analysis of the IV method shows that the antenna gain weights are independent of finitely correlated noise, so that unbiased estimation of signal arrival angles is possible. Only matrix inversions are required to compute the weight estimates. In this sense, the IV method provides performance comparable with eigenvector techniques but with lower computational burden. Both minimal and overdetermined IV estimators are derived. The overdetermined estimators give the same theoretical array weights as minimal estimators, but yield more accurate weight estimates in real data situations. Simulation results are presented to compare these IV methods with one another and with conventional matrix inversion weight estimators. In these examples it is seen that IV methods are able to resolve closely spaced interference sources when conventional matrix inversion techniques cannot. It is also shown that overdetermined methods are capable of providing weight estimates with lower variances than those of minimal methods