Adaptive detection of a signal known only to lie on a line in a known subspace, when primary and secondary data are partially homogeneous

This paper deals with the problem of detecting a signal, known only to lie on a line in a subspace, in the presence of unknown noise, using multiple snapshots in the primary data. To account for uncertainties about a signal's signature, we assume that the steering vector belongs to a known linear subspace. Furthermore, we consider the partially homogeneous case, for which the covariance matrix of the primary and the secondary data have the same structure but possibly different levels. This provides an extension to the framework considered by Bose and Steinhardt. The natural invariances of the detection problem are studied, which leads to the derivation of the maximal invariant. Then, a detector is proposed that proceeds in two steps. First, assuming that the noise covariance matrix is known, the generalized-likelihood ratio test (GLRT) is formulated. Then, the noise covariance matrix is replaced by its sample estimate based on the secondary data to yield the final detector. The latter is compared with a similar detector that assumes the steering vector to be known

Detection of an unknown rank-one component in white noise

We consider the detection of an unknown and arbitrary rank-one signal in a spatial sector scanned by a small number of beams. We address the problem of finding the maximal invariant for the problem at hand and show that it consists of the ratio of the eigenvalues of a Wishart matrix to its trace. Next, we derive the generalized-likelihood ratio test (GLRT) along with expressions for its probability density function (pdf) under both hypotheses. Special attention is paid to the case m= 2, where the GLRT is shown to be a uniformly most powerful invariant (UMPI). Numerical simulations attest to the validity of the theoretical analysis and illustrate the detection performance of the GLRT

Adaptive subspace detectors

Includes bibliographical references.In this paper, we use the theory of generalized likelihood ratio tests (GLRTs) to adapt the matched subspace detectors (MSDs) of [1] and [2] to unknown noise covariance matrices. In so doing, we produce adaptive MSDs that may be applied to signal detection for radar, sonar, and data communication. We call the resulting detectors adaptive subspace detectors (ASDs). These include Kelly's GLRT and the adaptive cosine estimator (ACE) of [6] and [19] for scenarios in which the scaling of the test data may deviate from that of the training data. We then present a unified analysis of the statistical behavior of the entire class of ASDs, obtaining statistically identical decompositions in which each ASD is simply decomposed into the nonadaptive matched filter, the nonadaptive cosine or t-statistic, and three other statistically independent random variables that account for the performance-degrading effects of limited training data.This work was supported by the Office of Naval Research under Contracts N00014-89-J-1070 and N00014-00-1-0033, and by the National Science Foundation under Contracts MIP-9529050 and ECS 9979400

CFAR adaptive subspace detector is a scale-invariant GLRT, The

Includes bibliographical references.The constant false alarm rate (CFAR) matched subspace detector (CFAR MSD) is the uniformly most-powerful-invariant test and the generalized likelihood ratio test (GLRT) for detecting a target signal in noise whose covariance structure is known but whose level is unknown. Recently, the CFAR adaptive subspace detector (CFAR ASD), or adaptive coherence estimator (ACE), was proposed for detecting a target signal in noise whose covariance structure and level are both unknown and whose covariance structure is estimated with a sample covariance matrix based on training data. We show here that the CFAR ASD is GLRT when the test measurement is not constrained to have the same noise level as the training data. As a consequence, this GLRT is invariant to a more general scaling condition on the test and training data than the well-known GLRT of Kelly.This work was supported by the Office of Naval Research under Contract N00014-89-J-1070 and by the National Science Foundation under Contract MIP-9529050

Robust Altitude Estimation For Over-The-Horizon Radar Using A

In previous work, a matched-field estimate of aircraft altitude from multiple over-the-horizon radar dwells was presented. This approach exploits the altitude dependence of direct and surface reflected returns off the aircraft and the relative phase changes of these micro-multipath arrivals across radar dwells. Since this previous approach assumed high dwell-to-dwell predictability, it is sensitive to mismatch between modeled versus observed micromultipath phase and amplitude changes from dwell-to-dwell. In this paper, a generalized matched-field altitude estimate is presented based on a state-space model that accounts for random ionospheric and target-motion effects which degrade the dwell-to-dwell predictability of target returns. The new formulation results in an efficient, robust recursive maximum likelihood altitude estimate. Simulation and real data results suggest that the proposed technique can achieve an accuracy within 5,000 ft. using 10-20 dwells, even with relatively high levels of uncertainty in modeling of dwell-to-dwell changes in the target return

Click Here for Full Article Recursive Bayesian electromagnetic refractivity

[1] Estimation of the range- and height-dependent index of refraction over the sea surface facilitates prediction of ducted microwave propagation loss. In this paper, refractivity estimation from radar clutter returns is performed using a Markov state space model for microwave propagation. Specifically, the parabolic approximation for numerical solution of the wave equation is used to formulate the refractivity from clutter (RFC) problem within a nonlinear recursive Bayesian state estimation framework. RFC under this nonlinear state space formulation is more efficient than global fitting of refractivity parameters when the total number of range-varying parameters exceeds the number of basis functions required to represent the height-dependent field at a given range. Moreover, the range-recursive nature of the estimator can be easily adapted to situations where the refractivity modeling changes at discrete ranges, such as at a shoreline. A fast range-recursive solution for obtaining range-varying refractivity is achieved by using sequential importance sampling extensions to state estimation techniques, namely, the forward and Viterbi algorithms. Simulation and real data results from radar clutter collected off Wallops Island, Virginia, are presented which demonstrate the ability of this method to produce propagation loss estimates that compare favorably with ground truth refractivity measurements

GMTI MIMO radar

Multiple-input multiple-output (MIMO) extensions to radar systems enable a number of advantages compared to traditional approaches. These advantages include improved angle estimation and target detection. In this paper, MIMO ground moving target indication (GMTI) radar is addressed. The concept of coherent MIMO radar is introduced. Comparisons are presented comparing MIMO GMTI and traditional radar performance. Simulations and theoretical bounds for MIMO GMTI angle estimation and minimum detectable velocity are presented. The simulations are evaluated in the time domain, enabling waveform design studies. For some applications, these results indicate significant potential improvements in clutter-mitigation SINR loss and reduction in angle-estimation error for slow-moving targets.United States. Dept. of the Air Force (contract FA8721-05-C-0002