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We consider the design of a sensor network for detecting an emitter which if present is known to be located in an interval but whose exact position is unknown. We seek to minimize the total system power consumption subject to detection performance constrains by carefully choosing the thresholds and positions of the sensors. Toward this goal, we propose an iterative algorithm for the optimization problem. Numerical results are given to provide insights into the design of such networks. We show that random sensor placement can perform poorly, in contrast to what many currently believe.

We consider a receive antenna selection multiple-input-multiple-output (MIMO) system, where only one out of [N.sub.r] receive antennas is selected. Spatial channel correlation will be considered at the receiver side only. Our goal is to investigate the capacity performance of this system. In particular, we will derive a closed-form expression for the outage probability and an upper bound for the ergodic (average) capacity. These quantities will be expressed using an infinite series representation. To do so, we will first derive the joint cumulative distribution function and the joint probability density function of the squared row norms of the channel matrix. This is enabled by taking advantage of the statistical properties of multivariate chi-squared random variables. Using the outage expression derived, we demonstrate that the considered antenna selection system achieves a full diversity order that is similar to a MIMO system without antenna selection. Next, we derive the probability density function of the maximum of the squared row norms of the channel matrix and its moments, which is straightly related to the system ergodic capacity. We also analyze the error rate performance of the aforementioned receive antenna selection MIMO system while using orthogonal space-time block codes (OSTBCs) at the transmitter. Our simulation results are shown to validate our analytical findings. Index Terms--Antenna selection, channel capacity, correlated channels, multiple-input-multiple-output (MIMO), multivariate chi-squared distribution.

A new class of jointly optimized communication and binary signal detection schemes are presented for decreasing the number of sensor transmissions needed while sensors operate in an autonomous manner. Results suggest significant savings even for smaller values of the distance measure.

A novel cooperative relay signaling is proposed that applies channel aware decentralized detection theory to exploit the FA (finite alphabet) property of the source message. The numerical examples have shown that the proposed scheme has performed better than the two existing cooperative relay strategies, namely amplify-forward and decode-forward.

This paper addresses the problem of radar waveform design for target identification and classification. Both the ordinary radar with a single transmitter and receiver and the recently proposed multiple-input multiple-output (MIMO) radar are considered. A random target impulse response is used to model the scattering characteristics of the extended (nonpoint) target, and two radar waveform design problems with constraints on waveform power have been investigated. The first one is to design waveforms that maximize the conditional mutual information (MI) between the random target impulse response and the reflected waveforms given the knowledge of transmitted waveforms. The second one is to find transmitted waveforms that minimize the mean-square error (MSE) in estimating the target impulse response. Our analysis indicates that under the same total power constraint, these two criteria lead to the same solution for a matrix which specifies the essential part of the optimum waveform design. The solution employs water-filling to allocate the limited power appropriately. We also present an asymptotic formulation which requires less knowledge of the statistical model of the target.

A new approach to design space-time convolutional code (STCC) for multiple-input multiple-output (MIMO) systems, with arbitrary constellation size and any number of transmit antennas is presented. The approach is based on modeling an n x m MIMO system as an equivalent of n distinct 1 x m single-input multiple-output (SIMO) systems.

A study introducing the statistical multiple-input multiple-output (MIMO) radar concept inspired by the latest advances in MIMO communications is illustrated. The fundamental difference between statistical MIMO and other radar array system is that the latter seek to maximize the coherent processing gain, while statistical MIMO radar capitalizes on the diversity of target scattering to improve radar performance.

The properties of the diversity gain and the coding gain of space-time codes (STCs) used in frequency-selective channels are derived. The results revealed that the diversity gain of an STC that provides maximum diversity gain for channels with the maximum expected memory is robust to a decrease in memory.

The system mutual information of a MIMO system with multiple users which mutually intefere is examined and reported.

Distributed signal detection is extended to multiuser scenarios. Optimum decision rules for cooperating receivers are found for the jointly optimum criterion and the individually optimum criterion. Numerical results show that distributed systems achieve nearly centralized performance using only one or two-bit descriptions of the preliminary decisions. Moreover, distributed signal detection can be helpful in lessening the severity of the near-far problem when full multiuser detection is impractical. To further reduce the complexity, a suboptimum approach which employs linear multiuser detectors at its front end is investigated. The investigated suboptimum approach is shown to achieve considerable improvement over an alternate suboptimum approach that attempts to minimize distortion.

Optimum distributed detection schemes base don the Neyman-Pearson criterion is examined.

Distributed signal detection schemes have received significant attention recently, but usually under the assumption of stationary observations which are independent from sensor to sensor. Here, order statistics based constant false alarm rate (OS-CFAR) detection techniques are applied to a distributed detection system with nonstationary observations where the signal observations are assumed to be dependent from sensor to sensor. Cases are considered where weak narrowband random signals are observed in additive Gaussian noise-plus-clutter of unknown power. Necessary conditions are given which specify the best sensor thresholds for some n-sensor cases. The best schemes using nonrandomized fusion rules are found for some specific two-sensor cases. The best schemes may use either AND or OR fusion rules depending on the specific false alarm probability and the number of reference observations used in the OS-CFAR scheme. Distributed OS-CFAR and cell-averaging CFAR (CA-CFAR) schemes are compared in terms of their capability to maintain false alarm probability in nonhomogeneous backgrounds. At least for the specific cases we have studied, there are OS-CFAR schemes which generally outperform the CA-CFAR schemes in this regard.

An adaptive receiver based on multiple antennas for diversity against fading has been developed for used in an impulsive noise environment.

The papers in this special issue are devoted to multiple-input multiple-output (MIMO) wireless communications technology.

Bayes-optimal binary quantization to detect shift in mean in dependent Gaussian random variables, was examined.

Space-time adaptive processing algorithms are discussed.

Physical mechanisms generating noise in communication receivers with antenna arrays were mathematically modeled to developed a statistical noise model.

Research describing the development of analytical formulas for adaptive radar detection algorithms is presented. These algorithms may be used where a non-homogeneous environment leads to a mismatch between the estimated and the true covariance matrix of the data to be tested.

The development and performance study of a simple adaptive diversity receiver for communication environments with signal fading and non-Gaussian noise and interference is presented. The probability density function of the additive noise and interference was modeled by a finite mixture of Gaussian distributions. The model contains a number of parameters that characterize the Gaussian mixture noise probability density function and the fading characteristics at each antenna.