121 research outputs found

    Asymptotic optimal SINR performance bound for space-time beamforming

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    International audienceIn many detection applications, the main performance criterion is the signal to interference plus noise ratio (SINR). After linear filtering, the optimal SINR corresponds to the maximum value of a Rayleigh quotient, which can be interpreted as the largest generalized eigenvalue of two covariance matrices. Using an extension of Szegö's theorem for the generalized eigenvalues of Hermitian block Toeplitz matrices, an expression of the theoretical asymptotic optimal SINR w.r.t. the number of taps is derived for arbitrary arrays with a limited but arbitrary number of sensors and arbitrary spectra. This bound is interpreted as an optimal zero-bandwidth spatial SINR in some sense. Finally, the speed of convergence of the optimal wideband SINR for a limited number of taps is analyzed for several interference scenario

    Unit Circle Roots Based Sensor Array Signal Processing

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    As technology continues to rapidly evolve, the presence of sensor arrays and the algorithms processing the data they generate take an ever-increasing role in modern human life. From remote sensing to wireless communications, the importance of sensor signal processing cannot be understated. Capon\u27s pioneering work on minimum variance distortionless response (MVDR) beamforming forms the basis of many modern sensor array signal processing (SASP) algorithms. In 2004, Steinhardt and Guerci proved that the roots of the polynomial corresponding to the optimal MVDR beamformer must lie on the unit circle, but this result was limited to only the MVDR. This dissertation contains a new proof of the unit circle roots property which generalizes to other SASP algorithms. Motivated by this result, a unit circle roots constrained (UCRC) framework for SASP is established and includes MVDR as well as single-input single-output (SISO) and distributed multiple-input multiple-output (MIMO) radar moving target detection. Through extensive simulation examples, it will be shown that the UCRC-based SASP algorithms achieve higher output gains and detection probabilities than their non-UCRC counterparts. Additional robustness to signal contamination and limited secondary data will be shown for the UCRC-based beamforming and target detection applications, respectively

    Spatial filtering for pilot-aided WCDMA systems: a semi-blind subspace approach

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    This paper proposes a spatial filtering technique for the reception of pilot-aided multirate multicode direct-sequence code division multiple access (DS/CDMA) systems such as wideband CDMA (WCDMA). These systems introduce a code-multiplexed pilot sequence that can be used for the estimation of the filter weights, but the presence of the traffic signal (transmitted at the same time as the pilot sequence) corrupts that estimation and degrades the performance of the filter significantly. This is caused by the fact that although the traffic and pilot signals are usually designed to be orthogonal, the frequency selectivity of the channel degrades this orthogonality at hte receiving end. Here, we propose a semi-blind technique that eliminates the self-noise caused by the code-multiplexing of the pilot. We derive analytically the asymptotic performance of both the training-only and the semi-blind techniques and compare them with the actual simulated performance. It is shown, both analytically and via simulation, that high gains can be achieved with respect to training-onlybased techniques.Peer Reviewe

    3-D Beamspace ML Based Bearing Estimator Incorporating Frequency Diversity and Interference Cancellation

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    The problem of low-angle radar tracking utilizing an array of antennas is considered. In the low-angle environment, echoes return from a low flying target via a specular path as well as a direct path. The problem is compounded by the fact that the two signals arrive within a beamwidth of each other and are usually fully correlated, or coherent. In addition, the SNR at each antenna element is typically low and only a small number of data samples, or snapshots, is available for processing due to the rapid movement of the target. Theoretical studies indicates that the Maximum Likelihood (ML) method is the only reliable estimation procedure in this type of scenario. However, the classical ML estimator involves a multi-dimensional search over a multi-modal surface and is consequently computationally burdensome. In order to facilitate real time processing, we here propose the idea of beamspace domain processing in which the element space snapshot vectors are first operated on by a reduced Butler matrix composed of three orthogonal beamforming weight vectors facilitating a simple, closed-form Beamspace Domain ML (BDML) estimator for the direct and specular path angles. The computational simplicity of the method arises from the fact that the respective beams associated with the three columns of the reduced Butler matrix have all but three nulls in common. The performance of the BDML estimator is enhanced by incorporating the estimation of the complex reflection coefficient and the bisector angle, respectively, for the symmetric and nonsymmetric multipath cases. To minimize the probability of track breaking, the use of frequency diversity is incorporated. The concept of coherent signal subspace processing is invoked as a means for retaining the computational simplicity of single frequency operation. With proper selection of the auxiliary frequencies, it is shown that perfect focusing may be achieved without iterating. In order to combat the effects of strong interfering sources, a novel scheme is presented for adaptively forming the three beams which retains the feature of common nulls

    Signal Processing in Large Systems: a New Paradigm

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    For a long time, detection and parameter estimation methods for signal processing have relied on asymptotic statistics as the number nn of observations of a population grows large comparatively to the population size NN, i.e. n/N→∞n/N\to \infty. Modern technological and societal advances now demand the study of sometimes extremely large populations and simultaneously require fast signal processing due to accelerated system dynamics. This results in not-so-large practical ratios n/Nn/N, sometimes even smaller than one. A disruptive change in classical signal processing methods has therefore been initiated in the past ten years, mostly spurred by the field of large dimensional random matrix theory. The early works in random matrix theory for signal processing applications are however scarce and highly technical. This tutorial provides an accessible methodological introduction to the modern tools of random matrix theory and to the signal processing methods derived from them, with an emphasis on simple illustrative examples

    Minimum redundancy array structure for interference cancellation

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    Adaptive antenna arrays are widely used in many advanced radar, sonar, and communication systems because of their effectiveness in cancelling intentional or unintentional interferers. A uniformly spaced linear array, referred to as a Uniform Regular Array (URA), is the usual structure used for interference cancellation. The Minimum Redundancy Array (MRA) structure proposed in this work is a special kind of thinned array whose application was limited in the past to direction finding. MRAs with the same number of array elements can resolve directions of much more closely spaced signals than URAs. The URA structure is customarily utilized for interference cancellation, and the Minimum Noise Variance (MNV) criterion is a common performance measure for deriving optimum weights, provided that the desired signal is absent during adaptation. The MNV criterion is to minimize the combined sum of the interference and background noise power. Another approach to interference cancellation using the URA structure is the eigencanceling method. This method, which is based on the eigenstructure of the spatial autocorrelation matrix, when compared to the conventional beamforming method, has the following advantages: 1) deeper interference cancellation 2) independence of the interfers\u27 power, and 3) faster optimum weight convergence. In this work, both the conventional beamforming and eigencanceling methods were applied to the MRA structure and investigated analytically. Performance of the MRAs were studied and compared to that of the URAs. For uncorrelated interferers, the cancellation depth of the MRA in the main beam region was almost the same as that of the URA with the same aperture and many more elements. When the eigencanceling technique was applied, it was found that the convergence rate of the MRA was about four times faster than that of the URA. This work also contains other topics, such as the relation between the eigenspaces of the MRA structure and its corresponding URA. Preliminary results on planar MRA structures are also included. For an array application with a large aperture requirement in terms of the number of array elements, the MRA proved to be a much better choice than the URA in achieving interference cancellation

    Joint transceiver design for MIMO channel shortening.

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    Channel shortening equalizers can be employed to shorten the effective impulse response of a long intersymbol interference (ISI) channel in order, for example, to decrease the computational complexity of a maximum-likelihood sequence estimator (MLSE) or to increase the throughput efficiency of an orthogonal frequency-division multiplexing (OFDM) transmission scheme. In this paper, the issue of joint transmitter–receiver filter design is addressed for shortening multiple-input multiple-output (MIMO) ISI channels. A frequency-domain approach is adopted for the transceiver design which is effectively equivalent to an infinite-length time-domain design. A practical space–frequency waterfilling algorithm is also provided. It is demonstrated that the channel shortening equalizer designed according to the time-domain approach suffers from an error-floor effect. However, the proposed techniques are shown to overcome this problem and outperform the time-domain channel shortening filter design. We also demonstrate that the proposed transceiver design can be considered as a MIMO broadband beamformer with constraints on the time-domain multipath length. Hence, a significant diversity gain could also be achieved by choosing strong eigenmodes of the MIMO channel. It is also found that the proposed frequency-domain methods have considerably low computational complexity as compared with their time-domain counterparts
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