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Abstract - UA2

UA2.1	Efficient Super Resolution Time Delay Estimation Techniques J. Li, R. Wu, Z. Liu (University of Florida, USA) In this paper, an efficient Weighted Fourier transform and Relaxation based algorithm (referred to as WRELAX) is first proposed for the well-known time delay estimation problem. WRELAX involves only a sequence of weighted Fourier transforms. Its resolution is much higher than that of the conventional matched filter approach. One disadvantage associated with WRELAX is that it converges slowly when the signals are spaced very closely. To overcome this problem, the well-known high resolution MODE (Method of Direction Estimation) algorithm, which was originally proposed for angle estimation in array processing, is modified and used with WRELAX for super resolution time delay estimation. The latter method is referred to as MODE-WRELAX. MODE-WRELAX provides better accuracy than MODE and higher resolution than WRELAX. Moreover, it applies to both complex- and real-valued signals (including those with highly oscillatory correlation functions). Numerical results show that the MODE-WRELAX estimates can approach the corresponding the Cramer-Rao bounds.
UA2.2	Iterative Gram-Schmidt Orthonormalization for Efficient Parameter Estimation H. Ge (New Jersey Institute of Technology, USA) We present an efficient method for estimating non-linearly entered parameters of a linear signal model corrupted by additive noise. The method uses the Gram-Schmidt orthonormalization procedure in combination with a number of iterations to de-bias and re-balance the coupling between non-orthogonal signal components efficiently. Projection interpretation is provided as rationale of the proposed iterative algorithm. Computer simulations are conducted to show the effectiveness of the algorithm.
UA2.3	The Performance of Maximum Likelihood Over-the-Horizon Radar Coordinate Registration R. Anderson, J. Krolik (Duke University, USA) A well-known source of target localization errors in over-the-horizon radar is the uncertainty about downrange ionospheric conditions. Maximum likelihood (ML) coordinate registration, using statistical modeling of ionospheric parameters, has recently been proposed as a method which is robust to ionospheric variablity. This paper reports ML performance results for real data from a known target using estimates of ionospheric statistics derived from ionosonde measurements. Bootstrap samples derived from these statistics are then used in a hidden Markov model approximation to the ground range likelihood function. Comparison of the ML and conventional methods for over 250 radar dwells indicates the new technique achieves better than a factor of two improvement in ground range accuracy.
UA2.4	A Matrix-Pencil Approach to Blind Separation of Non-White Signals in White Noise C. Chang (University of Hong Kong, P R China); Z. Ding, S. Yau (Hong Kong University of Sci & Tech, P R China); F. Chan (University of Hong Kong, P R China) The problem of blind source separation in additive white noise is an important problem in speech, array and acoustic signal processing. In general this problem requires the use of higher order statistics of the received signals. Nonetheless, many signal sourcess such as speech with distinct, non-white power spectral densities, second order statistics of the received signal mixture can be exploited for signal separation. While previous approaches often assume that additive noise is absent or that the noise correlation matrix is known, we propose a simple and yet effective signal extraction method for signal source separation under unknown white noise. This new and unbiased signal extractor is derived from the matrix pencil formed between output auto-correlation matrices at different delays. Simulation examples are presented.
UA2.5	Exploitation of Signal Structure in Array-Based Blind Copy and Copy-Aided DF Systems B. Agee, S. Bruzzone, M. Bromberg (Radix Technologies, USA) A general approach to array-based copy and DF of structured communication signals is presented that can substantially outperform conventional techniques, by exploiting additional information about the structure of the signals of interest to the reception system. The techniques are derived from optimal parameter estimation concepts that directly incorporate this additional information into the estimation strategy. The resultant algorithms demonstrate strong theoretical, experimental, and implementation advantages over conventional techniques. Results are demonstrated for separation and DF of co-channel FM, CPFSK, DSB-AM, and burst waveforms.
UA2.6	Using Signal Cancellation for Optimum Beamforming in a Cellular CDMA System T. Luo, S. Blostein (Queen's University, Canada) We propose a new algorithm for estimating the interference-plus-noise covariance matrix for beamforming in a cellular CDMA system in a fading channel. The method uses direct PN sequence signal cancellation. We show in theory that our method outperforms that of[1,2] for finite input data. The results, confirmed by simulation, show that we get improved DOA estimates and SINR with lower computational requirements.
UA2.7	A Novel Wavelet-Based Generalized Sidelobe Canceller Y. Chu, W. Fang (National Taiwan Univ of Sci & Tech, Taiwan, ROC); S. Chang (National Taiwan Ocean University, Taiwan, ROC) This paper presents a novel narrowband adaptive beamformer with the generalized sidelobe canceller (GSC) as the underlying structure. The new beamformer employs the regular M-band wavelet filters in the design of the blocking matrix of the GSC, which, as justified analytically, can indeed block the desired signals as required, provided the wavelet filters have sufficiently high regularity. Additionally, the eigenvalue spreads of the covariance matrices of the blocking matrix outputs, as demonstrated in various scenarios, decrease, thus accelerating the convergence speed of the succeeding least mean squares (LMS) algorithm. Also, the new beamformer belongs to a specific type of partially adaptive beamformers, wherein only a portion of weights is utilized in the adaptive processing. Consequently, the computational complexity is substantially reduced as compared with previous approaches. The issues of choosing the parameters involved for superior performance are addressed as well. Simulation results are furnished to justify this new approach.
UA2.8	Generalized Forward/Backward Subaperture Smoothing Techniques for Sample Starved STAP U. Pillai (Polytechnic University, USA); J. Guerci (Science Application International Corp., USA); Y. Kim (Polytechnic University, USA) A major issue in space-time adaptive processing (STAP) for airborne moving target indicator (MTI) radar is the so-called sample support problem. Often, the available sample support for estimating the interference covariance matrix leads to severe rank deficiency, thereby precluding STAP beamforming based on the direct sample matrix inversion (SMI) method. The intrinsic interference subspace removal (ISR) technique, which is a computationally useful form of diagonally loaded SMI method, can handle this case, although the performance is poor in low sample situations. In this context, new subarray-subpulse schemes using forward and backward data vectors are introduced to overcome the data deficiency problem. It is shown here that multiplicative improvement in data samples can be obtained at the expense of negligible loss in space-time aperture of the steering vector.
UA2.9	Optimal Loading Factor for Minimal Sample Support Space-Time Adaptive Radar Y. Kim, U. Pillai (Polytechnic University, USA); J. Guerci (Science Application International Corp., USA) A major issue in space-time adaptive processing (STAP) for airborne moving target indicator (MTI) radar is the so-called sample support problem. Often, the available sample support for estimating the interference covariance matrix leads to severe rank deficiency, thereby precluding STAP beamforming based on the direct sample matrix inversion (SMI) method. The intrinsic interference subspace removal (ISR) technique, which is a computationally and analytically useful form of diagonally loaded SMI method, is derived here. It covers from Hung-Turner Projection (HTP) algorithm to matched filter according to the loading factor. Also the optimum loading factor which gives the maximum signal-to-interference-plus-noise ratio (SINR) is derived here from the viewpoint of singular value decomposition of the covariance matrix. The simulation results with synthetic data show that the maximum SINR indeed coincides with the proposed optimum loading factor in various data sample situations.
UA2.10	Spatio-Temporal Coding for Radar Array Processing P. Calvary, D. Janer (Thomson-csf Applications Radar, France) The aim of this paper is to present a new method allowing radar digital beamforming (DBF) with only one receiver channel. The key point is the use of a particular spatio-temporal waveform transmitted by an active phased array antenna. By properly choosing the temporal modulation applied on each of the transmission elements, one can transmit different signals in different directions, allowing (under certain hypotheses) angular localisation with a single receiver channel. The major advantage is the design of a low cost system providing DBF capabilities.

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