Steven D. Blostein

Error Analysis in Stereo Determination of 3-D Point Positions

The relationship between the geometry of a stereo camera setup and the accuracy in obtaining three-dimensional position information is of great practical importance in many imaging applications. Assuming a point in a scene has been correctly identified in each image, its three-dimensional position can be recovered via a simple geometrical method known as triangulation. The probability that position estimates from triangulation are within some specified error tolerance is derived. An ideal pinhole camera model is used and the error is modeled as known spatial image plane quantization. A points measured position maps to a small volume in 3-D determined by the finite resolution of the stereo setup. With the assumption that the points actual position is uniformly distributed inside this volume, closed form expressions for the probability distribution of error in position along each coordinate direction (horizontal, vertical, and range) are derived. Following this, the probability that range error dominates over errors in the points horizontal or vertical position is determined. It is hoped that the results presented will have an impact upon both sensor design and error modeling of position measuring systems for computer vision and related applications.

Detecting small, moving objects in image sequences using sequential hypothesis testing

An algorithm is proposed for the solution of the class of multidimensional detection problems concerning the detection of small, barely discernible, moving objects of unknown position and velocity in a sequence of digital images. A large number of candidate trajectories, organized into a tree structure, are hypothesized at each pixel in the sequence and tested sequentially for a shift in mean intensity. The practicality of the algorithm is facilitated by the use of multistage hypothesis testing (MHT) for simultaneous inference, as well as the existence of exact, closed-form expressions for MHT test performance in Gaussian white noise (GWN). These expressions predict the algorithms computation and memory requirements, where it is shown theoretically that several orders of magnitude of processing are saved over a brute-force approach based on fixed sample-size tests. The algorithm is applied to real data by using a robust preprocessing procedure to eliminate background structure and transform the image sequence into a residual representation, modeled as GWN. Results are verified experimentally on a variety of video image sequences. >

MIMO Minimum Total MSE Transceiver Design With Imperfect CSI at Both Ends

This paper presents new results on joint linear transceiver design under the minimum total mean-square error (MSE) criterion, with channel mean as well as both transmit and receive correlation information at both ends of a multiple-input multiple-output (MIMO) link. The joint design is formulated into an optimization problem. The optimum closed-form precoder and decoder are derived. Compared to the case with perfect channel state information (CSI), linear filters are added at both ends to balance the suppression of channel noise and the noise from imperfect channel estimation. The impact of channel estimation error as well as channel correlation on system performance is assessed, based on analytical and simulation results.

Multiple antenna systems: their role and impact in future wireless access

Multiple antennas play an important role in improving radio communications. In view of this role, the area of multiple antenna communication systems is in the forefront of wireless research. This article reviews two key related aspects of multiple antenna communication systems: multiple access interference mitigation at the receiver via multi-user beamforming; and space-time modulation and coding for MIMO systems. It is shown that both multi-user and MIMO receivers share similar signal processing and complexity tradeoffs.. Following that, a general unified framework for assessing different types of space-time modulation for MIMO systems is introduced. These space-time modulation methods are then compared in terms of Shannon capacity over multipath channels. Key MIMO system performance and implementation issues are also highlighted.

Approximate Minimum BER Power Allocation for MIMO Spatial Multiplexing Systems

Power allocation for multiple-input multiple-output (MIMO) spatial multiplexing systems is investigated. Minimization of bit error rate (MBER) is employed as the optimization criterion. MBER power allocation schemes for a variety of receiver structures, including zero-forcing (ZF), successive interference cancellation (SIC), and ordered SIC (OSIC), are proposed. It is shown that the newly proposed MBER power allocation schemes improve error rate performance. Simulation results indicate that SIC and OSIC with MBER power allocation outperform MBER precoding for ZF equalization as well as minimum mean squared-error (MMSE) preceding/decoding. Performance under noisy channels and power feedback is analyzed. A modified algorithm that mitigates error propagation in interference cancellation is developed. Compared to existing precoding methods, the proposed schemes significantly reduce both processing complexity and feedback overhead, and improve error rate performance.

Motion-based object segmentation and estimation using the MDL principle

There are increasing demands for ultralow bit-rate video image transmission. We present a new formulation of moving object segmentation and motion estimation to quantify the potential gain in using object-oriented motion compensation in image sequence coding. Motivated by real-valued parameter estimation required in object-oriented motion-compensated video source coding, a framework motivated by Rissanens (1983) minimum description length (MDL) principle is proposed to more tightly couple motion estimation and object segmentation algorithms to the overall objective of minimizing source bit rate. A new objective function is constructed, and a suboptimal procedure to segment and estimate moving objects in a scene is proposed. Each object is represented by chain-coded block boundaries, affine motion parameters, and motion-compensated prediction error. A number of experimental comparisons between block- and object-oriented coding schemes suggests a significant potential coding gain using object-oriented motion-compensated coding.

Modified decorrelating decision-feedback detection of BLAST space-time system

We propose a stable and reduced-complexity detection method for the Bell Labs layered space-time (BLAST) coding system. The existing iterative nulling and cancellation algorithm for BLAST has high computational complexity and requires repeated matrix pseudo-inverse calculation which may lead to numerical instability. A square-root algorithm was proposed by other researchers to reduce complexity and improve numerical stability. In this paper, to further reduce complexity, we modify the decorrelating decision-feedback CDMA multiuser detection method and apply it to BLAST. Similar to the square-root algorithm, numerical stable unitary transformations are performed on the Cholesky-decomposed matrices to reorder the detection and cancellation steps. However, our method exploits a symmetry property in the triangularization process, which may further improve the numerical stability and reduce computational complexity over that of the square-root algorithm. In the simulation results, the impact of the reordering on performance is demonstrated.

Timing and Carrier Synchronization With Channel Estimation in Multi-Relay Cooperative Networks

Multiple distributed nodes in cooperative networks generally are subject to multiple carrier frequency offsets (MCFOs) and multiple timing offsets (MTOs), which result in time varying channels and erroneous decoding. This paper seeks to develop estimation and detection algorithms that enable cooperative communications for both decode-and-forward (DF) and amplify-and-forward (AF) relaying networks in the presence of MCFOs, MTOs, and unknown channel gains. A novel transceiver structure at the relays for achieving synchronization in AF-relaying networks is proposed. New exact closed-form expressions for the Cramer-Rao lower bounds (CRLBs) for the multi-parameter estimation problem are derived. Next, two iterative algorithms based on the expectation conditional maximization (ECM) and space-alternating generalized expectation-maximization (SAGE) algorithms are proposed for jointly estimating MCFOs, MTOs, and channel gains at the destination. Though the global convergence of the proposed ECM and SAGE estimators cannot be shown analytically, numerical simulations indicate that through appropriate initialization the proposed algorithms can estimate channel and synchronization impairments in a few iterations. Finally, a maximum likelihood (ML) decoder is devised for decoding the received signal at the destination in the presence of MCFOs and MTOs. Simulation results show that through the application of the proposed estimation and decoding methods, cooperative systems result in significant performance gains even in presence of impairments.

Identification of frequency non-selective fading channels using decision feedback and adaptive linear prediction

In this paper we propose an algorithm for tracking the phase and amplitude of frequency nonselective fading channels. In contrast to Irvine-McLanes (see IEEE Journal on Selected Areas in Communications, vol.10, no.8, p.1289-1299, 1992) symbol-aided plus decision-directed (SADD) tracker which mainly uses training symbols for channel estimation, the new algorithm exploits data-bearing-bearing symbol correlation of fading channels. This algorithm combines decision feedback and adaptive linear prediction (DFALP) by using tentative coherent detection and the least mean square (LMS) algorithm. Both simulation results and theoretical analysis demonstrate that the performance of this algorithm compares favorably with the SADD algorithm in terms of decision delay, mean-square error and bit-error-rate. The algorithm is also applied to a system similar to EIA/TIA recommendation IS-54 and has achieved 3 dB gain using the same channel coding and similar interleaving. >

Bounds and Algorithms for Multiple Frequency Offset Estimation in Cooperative Networks

The distributed nature of cooperative networks may result in multiple carrier frequency offsets (CFOs), which make the channels time varying and overshadow the diversity gains promised by collaborative communications. This paper seeks to address multiple CFO estimation using training sequences in space-division multiple access (SDMA) cooperative networks. The system model and CFO estimation problem for cases of both decode-and-forward (DF) and amplify-and-forward (AF) relaying are formulated and new closed-form expressions for the Cramer-Rao lower bound (CRLB) for both protocols are derived. The CRLBs are then applied in a novel way to formulate training sequence design guidelines and determine the effect of network protocol and topology on CFO estimation. Next, two computationally efficient iterative estimators are proposed that determine the CFOs from multiple simultaneously relaying nodes. The proposed algorithms reduce multiple CFO estimation complexity without sacrificing bandwidth and training performance. Unlike existing multiple CFO estimators, the proposed estimators are also accurate for both large and small CFO values. Numerical results show that the new methods outperform existing algorithms and reach or approach the CRLB at mid-to-high signal-to-noise ratio (SNR). When applied to system compensation, simulation results show that the proposed estimators significantly reduce average-bit-error-rate (ABER).