Analysis of calibration, robustness, detection of space-time adaptive rada using experimental data

Signal cancellation effects in adaptive array radar are studied under non ideal conditions when there is a mismatch between the true desired signal and the presumed theoretical desired signal. This mismatch results in decreased performance when the estimated correlation matrix has a large desired signal component. The performance of the sample matrix inversion (SMI) method is compared to the eigenanalysis-based eigencanceler method. Both analytical results and the processing on the experimental data from the Mountaintop Program, show that eigenanalysis-based adaptive beamformers have greater robustness to signal cancellation effects than the SMI method. Also, the calibration of the recorded data, and the pulse compression method utilized to achieve high resolution are discussed

Adaptive radar in heterogeneous environment

Radar performance in heterogeneous clutter has been a much studied topic. In all the studies so far, various forms of the sample matrix inversion (SMI) technique where used to calculate the weight vector of the processor. In this thesis an eigenanalysis-based technique known as the eigencanceler, is used. Performance of this technique is compared to the performance of the generalized likelihood ration (GLR) processor. This comparison is done using the clutter edge model, in which there is an abrupt change in the clutter power in the reference window. It is shown that the false alarm rate fluctuations, of the cell averaging constant false alarm rate (CA-CFAR) eigencanceler, depend on the number of secondary data vectors used to estimate the covariance matrix. It is also shown that when the estimate of the covariance matrix is poor, the eigencanceler is able to perform where the GLR fails. These two methods are also compared using the range-dependent clutter power model, in which the range clutter power is a Weibull random variable. It is shown that the performance of the eigencanceler depends heavily on the variance of the clutter power random variable. It is again shown that the eigencanceler is able to perform with a low number of range cell samples, where the GLR fails

Narrow-band interference rejection in spread spectrum using an eigen analysis based approach

A new adaptive technique is suggested for rejecting narrow-band interferences in spread spectrum communications. When data is coded using a pseudo-noise code, the received signal consists of a wide-band signal with almost white spectral properties, thermal noise, and correlated narrow-band interferences. A new approach is proposed which exploits the statistical properties of the received signal via eigenanalysis of the received data. While the energy of the wide-band signal is distributed over all the eigenvalues of the signal autocorrelation matrix, the energy of the interference is concentrated in a few large eigenvalues. Hence, the eigenvectors corresponding to the large eigenvalues are termed the interference subspace. The proposed method derives a. weight vector residing in the subspace spanned by the rest of the eigenvectors termed the noise subspace. Consequently, it is orthogonal to the interference subspace. The eigenanalysis based interference cancellation is sub-optimal in a known signal environment, but is superior to the Wiener-Hopf filter when the signal statistics are estimated from a limited amount of data. A fast and effective adaptive algorithm is derived using the power method

Adaptive space-time processing for wireless communications

Adaptive space-time processing techniques have been found to increase the capacity of two major, multiple-access wireless communication systems: Time Division Multiple Access (TDMA) and Code Division Multiple Access (CDMA). In an IS-54 TDMA system, the frequency re-use factor has to be set to 7 so that cells with the same spectrum are separated far enough to meet a required carrier-to-interference ratio (CIR). Space processing uses multiple antennas which, in turn, provide alternative signal paths in order to cancel interferences and combat multipath fading. We have proposed the eigencanceler method and have reviewed the theoretical optimum combining and the feasible direct matrix inverse (DMI) technique. An analysis of the system performance reveals that when data sets are small, the eigencanceler is superior to DMI. Furthermore, we have proposed a. simple projection-based algorithm and have analyzed its performance. The capacity of CDMA communication systems is restricted by multiple-access interferences (MAI). We have shown that spatial and temporal processing can be combined to increase the capacity of CDMA-based wireless communications systems. The degrees of freedom provided by space-time processing can be exploited to combat both fading and MAI. Specifically, we have discussed the following methods: (1) space-time diversity, (2) cascade optimum spatial-diversity temporal, (3) cascade optimum spatial-optimum temporal, and (4) joint-domain optimum processing. We have proved that, due to its interference cancellation capability, optimum combining provides significantly better performance than diversity techniques

Comparison of adaptive radar algorithms : transformed SMI, eigencanceler, and SMI

Advanced airborne radars must perform target detection in the presence of interference and heavy clutter. In many applications, the practical usefulness of adaptive arrays is limited by their convergence rate. In this paper, we first analyze the performance of the SMI method. Then, two other methods, the transformed SMI and the eigencanceler, both based on the principle component inversion (PCI) technique, are described and analyzed by simulation. It is shown by simulation based comparison that the transformed SMI and the Eigencanceler outperform the SMI method. It is also shown that the transformed SMI and the eigencanceler has higher convergence rate in terms of output signal-to-noise ratio than the SMI, specially for short data record sizes. It is concluded that the transformed SMI and the eigencanceler are good alternatives to the SMI method when data set available is small