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Provided that one keeps in mind the Cram?er-Rao bound (CRB) limitations, that is, to become an overly optimistic lower bound when the observation conditions degrades, the CRB is a lower bound of great interest for analysis and design of a system of measurement in the asymptotic region. As a contribution, we introduce an original framework taking into account most (and possibly all) of the factors impacting the asymptotic estimation performance of the parameters of interest via equality constraints, leading to direct algebraic computations of constrained CRB. For complex systems, derivation of analytical expression of CRB is either impossible or inefficient. For application to active systems of measurement such as radar, we provide the general form of the Fisher information matrix (FIM) for multiple conditional models, which generally precludes the derivation of an analytical expression of the CRB for scenarios including interference and sensors modelling errors. We show that the proposed framework can also be used efficiently to generate new closed-form expressions of CRB, although this is not its main aim. [ABSTRACT FROM AUTHOR]

Estimation of the direction of arrival (DOA) of a signal source by means of a monopulse estimation scheme is one of the oldest and most widely used high-precision techniques in operational tracking systems. Although the statistical performance of this estimation technique has been extensively investigated, it has never been analyzed from the viewpoint of the joint detection-estimation problem. As a consequence the historical form of the (detector-angle estimator) solution has become the usual form implemented in operational tracking systems and may restrict their accessible performance. Indeed, the application of the optimal detection theory reveals alternative (detector-angle estimator) solutions providing other relevant trade-offs in detection-estimation performances that are worth considering to optimize the performance of the tracking system according to the scenario. Both stochastic signal model (Rayleigh-type signal source) and deterministic signal model (signal source of unknown amplitude) have been investigated, leading to a novel but equally simple (detector-angle estimator) solution through which analytical statistical prediction has been derived. Comparison of detection-estimation performances of new and usual solutions is presented. [ABSTRACT FROM AUTHOR]

It is well known that the ML estimator exhibits a threshold effect, i.e., a rapid deterioration of estimation accuracy below a certain signal-to-noise ratio (SNR) or number of snapshots. This effect is caused by outliers and is not captured by standard tools such as the Cramér-Rao bound (CRB). The search of the SNR threshold value (where the CRB becomes unreliable for prediction of maximum likelihood estimator variance) can be achieved with the help of the Barankin bound (BB), as proposed by many authors. The major drawback of the BB, in comparison with the CRB, is the absence of a general analytical formula, which compels one to resort to a discrete form, usually the Mcaulay-Seidman bound (MSB), requesting the search of an optimum over a set of test points. In this paper, we propose a new practical BB discrete form that provides, for a given set of test points, an improved SNR threshold prediction in comparison with existing approximations (MSB, Abel bound, Mcaulay-Hofstetter bound) at the expense of the computational complexity increased by a factor ⩽ (P + 1)³, where P is the number of unknown parameters. We have derived its expression for the general Gaussian observation model to be used in place of existing approximations. [ABSTRACT FROM AUTHOR]

This paper proposes a novel statistical prediction of monopulse errors [1] for a radar Swerling III-IV target embedded in noise or noise jamming where multiple observations are available. First, the study of the maximum likelihood estimator (MLE) of the complex monopulse ratio for a Swerling III-IV target embedded in spatially white noise allows us to extend the use of the MLE practical approximate form introduced by Mosca [2] for Swerling 0-I-II cases. Afterward, we derive analytical formulas for both the mean and variance of the MLE in approximate form conditioned by the usual detection step performed on the sum channel of a monopulse antenna. Last, we provide a comparison of target direction of arrival (DOA) estimation performance based on monopulse ratio estimation as a function of the Swerling model in the context of a multifunction radar. [ABSTRACT FROM AUTHOR]

A wide variety of actual processing requires a detection step, whose main effect is to restrict the set of observations available for parameter estimation. Therefore, as a contribution to the theoretical formulation of the joint detection and estimation problem, we address the derivation of lower bounds for deterministic parameters conditioned by a binary hypothesis testing problem. The main result is the introduction of a general scheme—detailed in the particular case of CRB—enabling the derivation of conditional deterministic MSE lower bounds. To prove that it is meaningful, we also show, with the help of a fundamental application, that the problem of lower bound tightness at low SNR may arise from an incorrect lower bound formulation that does not take into account the true nature of the problem under investigation: a joint detection-estimation problem. [ABSTRACT FROM AUTHOR]

State estimation is a fundamental task in a plethora of applications. Two important classes of linear minimum mean square error (MMSE) estimators are Wiener filters (WFs) and Kalman filters (KFs), the latter being the recursive form of the former for linear discrete state-space (LDSS) dynamic models. The minimum set of uncorrelation conditions regarding states and measurements which allows to formulate a general KF form has been recently introduced in the literature, but for both WFs and KFs a standard key assumption is that the measurement noise covariance is invertible. In this contribution we provide i) an alternative derivation of the WF in the case of a non-invertible measurement covariance matrix, and ii) the proof that under the minimum set of uncorrelation conditions lately released, the KF can always be formulated irrespective of the measurement covariance matrix rank. Notice that these results also apply for Kalman predictors and smoothers, linearly constrained WFs and minimum variance distortionless response (MVDR) filters, therefore being a remarkable result of broad interest. [ABSTRACT FROM AUTHOR]

Performance lower bounds are known to be a fundamental design tool in parametric estimation theory. A plethora of deterministic bounds exist in the literature, ranging from the general Barankin bound to the well-known Cramér-Rao bound (CRB), the latter providing the optimal mean square error performance of locally unbiased estimators. In this contribution, we are interested in the estimation of mixed real- and integer-valued parameter vectors. We propose a closed-form lower bound expression leveraging on the general CRB formulation, being the limiting form of the McAulay-Seidman bound. Such formulation is the key point to take into account integer-valued parameters. As a particular case of the general form, we provide closed-form expressions for the Gaussian observation model. One noteworthy point is the assessment of the asymptotic efficiency of the maximum likelihood estimator for a linear regression model with mixed parameter vectors and known noise covariance matrix, thus complementing the rather rich literature on that topic. A representative carrier-phase based precise positioning example is provided to support the discussion and show the usefulness of the proposed lower bound. [ABSTRACT FROM AUTHOR]