作者：
ORTIGUEIRA, MDLAGUNAS, MA[a]INSEC
R. Alves Redol 9 2° 1000 Lisbon Portugal
[b]Dep. TSC ETSIB-UPC Apdo. 30002 08080 Barcelona Spain
[c]Dep. TSC ETSIB-UPC Apdo. 30002 08080 Barcelona Spain
Two important questions in arraysignalprocessing are addressed in this paper: the data matrix versus autocorrelation matrix alternative and the recursive implementation of subspace DOA methods. The discussion of the...
详细信息
Two important questions in arraysignalprocessing are addressed in this paper: the data matrix versus autocorrelation matrix alternative and the recursive implementation of subspace DOA methods. The discussion of the first question is done in face of the proposed class of recursive algorithms. These new algorithms are easily implementable and have a high degree of parallelism that is suitable for on-line implementations. Algorithms for recursive implementation of the eigendecomposition (ED) of the autocorrelation matrix and SVD of the data matrix are described. The ED/SVD trade-off is discussed.
Least Mean Square (LMS) has been the most popular scheme in the realization of adaptive beamforming algorithms. In this paper a Robust Least Mean Square (R-LMS) algorithm is proposed which uses ratio parameters to con...
详细信息
Least Mean Square (LMS) has been the most popular scheme in the realization of adaptive beamforming algorithms. In this paper a Robust Least Mean Square (R-LMS) algorithm is proposed which uses ratio parameters to control the contribution of product vectors in the weight upgrading process. The idea behind the proposed scheme is inclusion of previous information in place of relying solely on current sample. The performance enhancement by R-LMS algorithm is achieved with insignificant increase in computational complexity of LMS algorithm, so the crux of the conventional technique is not lost. Simulation results are also presented which illustrate that R-LMS provides relatively fast convergence, less Brownian motion and improved stability.
The multistage constant modulus (CM) array was previously proposed for capturing multiple received signals in a cochannel signal environment. It consists of a cascade of individual CM array stages combined with adapti...
详细信息
The multistage constant modulus (CM) array was previously proposed for capturing multiple received signals in a cochannel signal environment. It consists of a cascade of individual CM array stages combined with adaptivesignal cancelers that remove the various signals captured across the stages. However, when the received signals are mutually correlated, the signals captured by the CM array stages are not completely canceled, and previous parallel extensions of the system do not guarantee that different signals will be captured across the stages. In this paper, we present a hybrid implementation of the multistage CM array for separating correlated signals where the canceler weights in the cascade structure provide estimates of the direction vectors of the captured signals. These estimates are then used in a parallel implementation of the linearly constrained CM (LCCM) array leading to the hybrid structure. Since the direction vectors are obtained directly from the canceler weights, the hybrid implementation does not require prior knowledge of the array response matrix and is independent of the type of antennas used in the receiver. The effect of a bias in the direction vector estimates for closely-spaced signals is analyzed, and the steady-state performance of the hybrid structure is compared to that of a conventional constrained implementation for correlated sources. Computer simulations for example cochannel scenarios are provided to illustrate various properties of the system. Mean-square-error (MSE) learning curves indicate that the proposed hybrid LCCM algorithm converges faster and has lower MSE than previous implementations.
Structured covariance matrix estimation in the presence of missing-(complex) data is addressed in this paper with emphasis on radar signalprocessing applications. After a motivation of the study, the array model is s...
详细信息
Structured covariance matrix estimation in the presence of missing-(complex) data is addressed in this paper with emphasis on radar signalprocessing applications. After a motivation of the study, the array model is specified and the problem of computing the maximum likelihood estimate of a structured covariance matrix is formulated. A general procedure to optimize the observed-data likelihood function is developed resorting to the expectation-maximization algorithm. The corresponding convergence properties are thoroughly established and the rate of convergence is analyzed. The estimation technique is contextualized for two practically relevant radar problems: beamforming and detection of the number of sources. In the former case an adaptive beamformer leveraging the EM-based estimator is presented;in the latter, detection techniques generalizing the classic Akaike information criterion, minimum description length, and Hannan-Quinn information criterion, are introduced. Numerical results are finally presented to corroborate the theoretical study.
Placing a radar in space for the detection and tracking of airborne moving targets has a number of advantages, however it creates a complex clutter environment and is susceptible to jamming. Both these problems can be...
详细信息
Placing a radar in space for the detection and tracking of airborne moving targets has a number of advantages, however it creates a complex clutter environment and is susceptible to jamming. Both these problems can be solved by employing a space-time adaptive processor. This paper considers how space-time adaptiveprocessing (STAP) can be applied and presents some simulation results illustrating its operation and showing the performance improvements potentially available.
暂无评论