Analysis of an adaptive CFAR detector in non-Gaussian interference

Coherent signal detection in non-Gaussian interference is presently of interest in adaptive array applications. Conventional array detection algorithms inherently model the interference with a multivariate Gaussian random vector. However, non-Gaussian interference models are also under investigation for applications where the Gaussian assumption may not be appropriate. We analyze the performance of an adaptive array receiver for signal detection in interference modeled with a non-Gaussian distribution referred to as a spherically invariant random vector (SIRV). We first motivate this interference model with results from radar clutter measurements collected in the Mountain Top Program. Then we develop analytical expressions for the probability of false alarm and the probability of detection for the adaptive array receiver. Our analysis shows that the receiver has constant false alarm rate (CFAR) performance with respect to all the interference parameters. Some illustrative examples are included that compare the detection performance of this CFAR receiver with a receiver that has prior knowledge of the interference parameters     

A method for estimating parameters of K-distributed clutter

A method for estimating the parameters of K-distributed clutter when the available sample size of the data is limited is proposed. In this method, the arithmetic mean and geometric mean of the given data are used to estimate the model parameters. Expressions characterizing the performance of the proposed estimator are presented, along with some simulation results. For spiky clutter, simulations show that parameter estimates obtained from the arithmetic and geometric mean are approximately equal to the numerically evaluated maximum-likelihood (ML) estimates. The method is also used to estimate the parameter of the Weibull density     

CFAR detection in clutter with unknown correlation properties

We develop a constant false-alarm rate (CFAR) approach for detecting a random N-dimensional complex vector in the presence of clutter or interference modeled as a zero mean complex Gaussian vector whose correlation properties are not known to the receiver. It is assumed that estimates of the correlation properties of the clutter/interference may be obtained independently by processing the received vectors from a set of reference cells. We characterize the detection performance of this algorithm when the signal to be detected is modeled as a zero-mean complex Gaussian random vector with unknown correlation matrix. Results show that for a prescribed false alarm probability and a given signal-to-clutter ratio (to be defined in the text), the detectability of Gaussian random signals depends on the eigenvalues of the matrix R_c^-1R_s. The nonsingular matrix R_c and the matrix R_s are the correlation matrices of clutter-plus-noise and signal vectors respectively. It is shown that the “effective” fluctuation statistics of the signal to be detected is determined completely by the eigenvalues of the matrix R_c^-1R_s. For example the signal to be detected has an effective Swerling II fluctuation statistics when all eigenvalues of the above matrix are equal. Swerling I fluctuation statistics results effectively when all eigenvalues except one are equal to zero. Eigenvalue distributions between these two limiting cases correspond to fluctuation statistics that lie between Swerling I and II models     

Efficient L-D factorization algorithms for PDA, IMM, and IMMPDAfilters

Efficient algorithms exist for the square-root probabilistic data association filter (PDAF). The same approach is extended to develop square-root versions of the interacting multiple model (IMM) Kalman filter and the IMMPDAF algorithms. The computational efficiency of the method stems from the fact that the terms needed in the overall covariance updates of PDAF, IMM, and IMMPDAF can be obtained as part of the square-root covariance update of an ordinary Kalman filter. In addition, a new square-root covariance prediction algorithm that is substantially faster than the usual modified weighted Gram-Schmidt (MWG-S) algorithm, whenever the process noise covariance matrix is time invariant, is proposed     

Analysis of CA-CFAR processors for linear-law detection

A method for evaluating the performance of cell-averaging constant false alarm rate (CA-CFAR) processors which use the amplitude of echo signals rather than their squared amplitude is presented. Results for the case of Rayleigh clutter/noise statistics are given. Detection probabilities are evaluated for the case of a Rayleigh fluctuating target embedded in Rayleigh clutter/noise for linear-law CA-CFAR processors. These results are observed to be practically identical to those of square-law CA-CFAR processors for which analytical expressions are readily available. These observations are verified using Monte Carlo simulations. The same conclusion is reached in the case of a nonfluctuating target embedded in Rayleigh clutter/noise for which only simulation results are presented     

A comparison of radiation shielding effectiveness of materials for highly elliptical orbits

The Canadian Space Agency (CSA) has proposed a Polar Communications and Weather (PCW) satellite mission, in conjunction with other partners. The PCW will provide essential communications and meteorological services to the Canadian Arctic, as well as space weather observations of in situ ionizing radiation along the orbit. The CSA has identified three potential Highly Elliptical Orbits (HEOs) for a PCW satellite constellation, Molniya, Modified Tundra and Triple Apogee (TAP), each having specific merits, which would directly benefit the performance/longevity of a PCW spacecraft. Radiation shielding effectiveness of various materials was studied for the three PCW orbit options to determine the feasibility of employing materials other than conventional aluminium to achieve a specified spacecraft shielding level with weight savings over aluminium. It was found that, depending on the orbit-specific radiation environment characteristics, the benefits of using polyethylene based materials is significant enough (e.g., 22% in Molniya for PE at 50 krad TID) to merit further investigation.     

Multi-signal flow graphs: a novel approach for system testabilityanalysis and fault diagnosis

In this paper, we present a comprehensive methodology for a formal, but intuitive, cause-effect dependency modeling using multi-signal directed graphs that correspond closely to hierarchical system schematics and develop diagnostic strategies to isolate faults in the shortest possible time without making the unrealistic single fault assumption. A key feature of our methodology is that our models lend naturally to real-world necessities, such as system integration and hierarchical troubleshooting