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In this paper, we carry out a detailed performance analysis of the recently addressed wavelet-based generalized sidelobe canceller (GSC), which employs a set of P-regular M-band wavelet filters in the design of the blocking matrix involved. We begin our analysis by developing a closed-form expression of the output signal-to-(interference plus noise) ratio (SINR) of partially adaptive beamformers with the GSC as the underlying structure under a one jammer or multiple jammer environment. This new expression is shown to encompass several previous works as special cases. We then examine the relationship between the choices of the parameters P and M in the wavelet-based GSC and the corresponding output SINR based on this expression. Some guidelines for the choices of these parameters can thus be induced to achieve superior performance. Simulation results are also furnished to justify these suggestions. Index Terms--Adaptive beamforming, generalized sidelobe canceller, partially adaptive arrays, performance analysis, wavelet filters.

A multiple signal classification based approach for jointly estimating the directions of arrival of a wireless multiray channel, is proposed.

This paper presents a novel narrow-band adaptive beamformer with the generalized sidelobe canceller (GSC) as the underlying structure. The new beamformer employs a wavelet-based approach for the design of the blocking matrix of the GSC, which is now constituted by a set of regular M-band wavelet filters. Such a construction of the blocking matrix can not only block the desired signals from the lower path as required provided the wavelet filters have sufficiently high regularity, but it also encompasses the widely used one with ones and minus ones along the diagonals as a special case. In addition, it possesses two advantageous features. First, the eigenvalue spreads of the covariance matrices of the blocking matrix outputs, as demonstrated in various scenarios, are decreased as compared with those of previous approaches. Since the popular leastmean squares (LMS) algorithm has been notorious for its slow convergence rate, the reduction of the eigenvalue spreads can, in general, accelerate the convergence speed of the succeeding LMS algorithm. Second, the new beamformer belongs to a specific type of partially adaptive beamformers, wherein only a portion of the available degree of freedom is utilized in the adaptive processing. As such, the overall computational complexity is substantially reduced when compared to previous works. The issues of choosing the parameters involved for superior performance are also addressed. Simulation results are furnished as well to justify this new approach. Index Terms: Adaptive beamforming, generalized sidelobe canceller, wavelet filters.

A novel procedure estimates the power spectral density of a random field from data inside a finite radius disk and provides non-negative results. The Radon transform domain covariance is always psd. The Gaussian basis functions enable the calculation of Radon transform from a few distinct points. A one-dimensional linear prediction yields spectral density estimates.

The recently developed fast algorithms to compute the Toeplitz-plus-Hankel covariance optimal linear least-squares prediction filters are provided as a systolic architecture. The architecture permits expression of the intrinsic concurrency of the parallelizable algorithm. The centrosymmetric covariance array structure simplification is also accomplished.

This work presents an efficient adaptive algorithm based on center of mass (CEM) for fast block motion estimation. Binary transform, subsampling, and horizontal/vertical projection techniques are also proposed. As the conventional CEM calculation is computationally intensive, binary transform and subsampling approaches are proposed to simplify CEM calculation; the binary transform center of mass (BITCEM) is then derived. The BITCEM motion types are classified by percentage of (0,0) BITCEM motion vectors. Adaptive search patterns are allocated according to the BITCEM moving direction and the BITCEM motion type. Moreover, the BITCEM motion vector is utilized as the initial search point for near-still or slow BITCEM motion types. To support the variable block sizes, the horizontal/vertical projections of a binary transformed macroblock are utilized to determine whether the block requires segmentation. Experimental results indicate that the proposed algorithm is better than the five conventional algorithms, that is, three-step search (TSS), new three-step search (N3SS), four three-step search (4SS), block-based gradient decent search (BBGDS), and diamond search (DS), in terms of speed or picture quality for eight benchmark sequences.