SAM-4.1

A Subspace Method for Separating Cochannel TDMA Signals
Rajiv Chandrasekaran, Kuei-Chiang Lai, John J Shynk (University of California, Santa Barbara)

In this paper, we describe an adaptive algorithm that uses a subspace method for separating cochannel time-division multiple-access (TDMA) signals impinging on an antenna array. A frame synchronization algorithm is initially employed to identify the cochannel scenario. The adaptive algorithm uses a subspace decomposition of the array signals to constrain the beamformer weights for the signal of interest to be in a specific subspace. This subspace corresponds to the orthogonal complement of the subspace spanned by the direction vectors of the interferers. A follow-on linear equalizer removes the intersymbol interference (ISI) introduced by the transmit filter.

SAM-4.2

Performance Analysis for Direction Finding in Non-Gaussian Noise
Brian M Sadler (Army Research Laboratory), Richard J Kozick (Bucknell University), Terrence Moore (American University)

We consider narrowband angle of arrival estimation in non-Gaussian (NG) noise channels, such as arises in some indoor and outdoor mobile communications channels. We develop a general expression for the Cramer-Rao bound (CRB) for direction finding using arrays for deterministic signals plus iid non-Gaussian noise, generalizing the Gaussian CRB. The CRBs for the noise and direction parameters decouple. The CRB for direction finding is expressed as a product of two terms that depend on the noise distribution, and the signal, respectively. We illustrate the results for a Gaussian mixture pdf, and present simulation results comparing five direction finding algorithms. An approach based on the expectation-maximization (EM) algorithm, that simultaneously estimates the noise parameters, the signal directions, and the signal waveforms, is shown to achieve the CRB over a wide SNR range.

SAM-4.3

Spatial Signature Estimation in Cyclic Cumulant Domain and Multiuser Signal Separation in Uplink SDMA
Qinye Yin, Minli Yao (Xi'an Jiaotong University)

In smart antenna system (SAS), the most compelling research work is the space-division multiple-access (SDMA) which includes uplink source separation and downlink selective transmission. In this paper, we propose a subspace-based spatial signature estimation algorithm of cochannel multiuser signals by jointly exploiting cumulant and cyclostationarity, and apply it to multiuser signal copy in uplink SDMA. Our method constructs a new matrix called spatial signature matrix (SS Matrix), and estimates multiuser spatial signature through the eigen-decomposition of the SS Matrix. Based on estimated spatial signature, a spatial filter bank is designed for cochannel multiuser waveform estimation. Computer simulations show that the spatial response of the filter bank can be used to find direction-of-arrival (DOA) of the correlated multiuser signals.

SAM-4.4

Finite Dimensional Algorithms for the Hidden Markov Model Multi-armed Bandit Problem
Vikram Krishnamurthy, Josipa Mickova (University of Melbourne)

The multi-arm bandit problem is widely used in scheduling of traffic in broadband networks, manufacturing systems and robotics. This paper presents a finite dimensional optimal solution to the multi-arm bandit problem for Hidden Markov Models. The key to solving any multi-arm bandit problem is to compute the Gittins index. In this paper a finite dimensional algorithm is presented which exactly computes the Gittins index. Suboptimal algorithms for computing the Gittins index are also presented and experimentally shown to perform almost as well as the optimal method. Finally an application of the algorithms to tracking multiple targets with a single intelligent sensor is presented.

SAM-4.5

A Denoising Approach to Multichannel Signal Estimation
Anil M Rao, Douglas L Jones (Coordinated Science Laboratory, University of Illinois)

Multichannel sensor array processing has received considerable attention in many important areas of signal processing. Almost all data recorded by multisensor instruments contain various amounts of noise, and much work has been done in developing optimal processing structures for estimating the signal source from the noisy multichannel observations. The techniques developed so far assume the signal and noise processes are at least wide-sense-stationary so that optimal linear estimation can be achieved with a set of linear, time-invariant filters. Unfortunately, nonstationary signals arise in many important applications and there is no efficient structure with which to optimally deal with them. While wavelets have proven to be useful tools in dealing with certain nonstationary signals, the way in which wavelets are to be used in the multichannel setting is still an open question. Based on the structure for optimal linear estimation of nonstationary multichannel data and statistical models of spatial signal coherence, we propose a method to obtain an efficient multichannel estimator based on the wavelet transform.

SAM-4.6

Low Complexity Blind Space-Time Identification of Propagation Parameters
Marc Chenu-Tournier (THOMSON-CSF LESiR), Anne Ferreol (THOMSON-CSF), Pascal Larzabal (LESiR)

The radio electrical transmissions often have multiple paths due to reflections on physical objects or due to the inhomogeneity of the propagation medium for example the ionospheric layers in a HF communication. This work presents a new algorithm for the blind estimation of the physical parameters in a multipath channel (direction of arrival, time delay and fading). The results exposed are useful for tactical applications such as HF radio-localization, or for radio communication systems, to combat the degradations due to the channel. The present study is based on the recent work done on blind deconvolution which estimates the channels impulse responses. Based on a physical path parametric model, a spatio-temporal parametric blind identification of the front wave is performed. These parameters are direction of arrival : DOA theta, relative time delay tau and complex gain alpha (fading).

SAM-4.7

Direction Estimation Using Conjugate Cyclic Cross-Correlation : More Signals Than Sensors
Vel B Manimohan, William J Fitzgerald (Cambridge University Engineering Department)

We consider the problem of estimating the directions of arrival of multiple communication signals arriving at a uniform linear array. By considering the conjugate cyclic cross-correlations of the sensor outputs and using a Bayesian framework, we propose a direction finding algorithm that allows us to estimate the directions of arrival for a more signals than sensors scenario. The algorithm does not need any training sequence and only requires a priori knowledge of the cyclic frequencies. It is also possible to estimate the directions of arrival in a multi-path fading environment under certain conditions.

SAM-4.8

d-MUSIC, a Real-Time Algorithm for Estimating the DOA of Coherent Sources Using a Single Array Snapshot
Randy K Howell (Raytheon Canada Limited), R. Lynn Kirlin (Dept. Elec. & Comp. Eng., University of Victoria)

d-MUSIC estimates the DOA of two closely spaced sources using a single array snapshot. To overcome the coherent signal problem d-MUSIC utilizes additional information, specifically the derivative of an array snapshot. The combined vector set produces a full rank signal space projector. The algorithm nearly attains the Cram�r-Rao bound for typical air traffic control problems. As it does not require a subspace decomposition (e.g., eigenstructure) and all operations are highly vectorized it can be readily implemented in real-time. The algorithm is tested using vertical linear array data with a low flying helicopter. With a spacing of 16% to 35% of a beamwidth between the direct and surface reflected rays, the d-MUSIC rms error is 9.6% of a beamwidth for the 4 data collections while MUSIC resolved the two rays for 2 of the 4 cases with a rms error of 18.1%.

SAM-4.9

Selective Direction Finding for Cyclostationary Signals by Exploitation of New Array Configuration
Minli Yao, Liang Jin, Qin-ye Yin (Xi'an Jiaotong University)

This paper presents a new cyclic direction-of-arrival (DOA) estimation algorithm. By exploiting a new minimum-redundancy linear-array (MRLA) configuration and cyclostationarity, the proposed algorithm constructs a matrix pencil from the pseudo-data matrix of 2N-1 virtual sensors��which are the results of temporal and spatial processing with designed M-sensor Sum-MRLA. Theoretical analysis and computer simulations show that, for each cyclic frequency, the algorithm can estimate 2N-2 DOAs with M-sensor Sum-MRLA (N>M).

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Last Update: February 4, 1999 Ingo Höntsch