<p>this thesis the problem of maximum likelihood (ML) detection for the linear multiple-input multiple-output (MIMO) channel is considered. The thesis investigates two algorithms previously proposed in the literature for implementing the ML detector, namely semide nite relaxation and sphere decoding.</p><p>The first algorithm, semide nite relaxation, is a suboptimal implementation of the ML detector meaning that it is not guaranteed to solve the maximum likelihood detection problem. Still, numerical evidence suggests that the performance of the semide nite relaxation detector is close to that of the true ML detector. A contribution made in this thesis is to derive conditions under which the semide nite relaxation estimate can be guaranteed to coincide with the ML estimate.</p><p>The second algorithm, the sphere decoder, can be used to solve the ML detection problem exactly. Numerical evidence has previously shown that the complexity of the sphere decoder is remarkably low for problems of moderate size. This has led to the widespread belief that the sphere decoder is of polynomial expected complexity. This is however unfortunately not true. Instead, in most scenarios encountered in digital communications, the expected complexity of the algorithm is exponential in the number of symbols jointly detected. However, for high signal to noise ratio the rate of exponential increase is small. In this thesis it is proved that for a large class of detection problems the expected complexity is lower bounded by an exponential function. Also, for the special case of an i.i.d. Rayleigh fading channel, an asymptotic analysis is presented which enables the computation of the expected complexity up to the linear term in the exponent.</p>

<p>Two estimation problems are treated in this thesis. Estimators are suggested and the asymptotical properties of the estimates are investigated analytically. Numerical simulations are used to assess small-sample performance. In addition, performance bounds are calculated. The first problem treated is parameter estimation for the reduced rank linear regression. A new method based on instrumental variable principles is proposed and its asymptotical performance analyzed. In addition, the Cram\'{e}r-Rao bound for the problem is derived for a general Gaussian noise model. The new method is asymptotically efficient (it has the smallest possible covariance) if the noise is temporally white, and outperforms previously suggested algorithms when the noise is temporally correlated. The approximation of a matrix with one of lower rank under a weighted norm is needed as part of the estimation algorithm. Two new, computationally efficient, methods are suggested. While the general matrix approximation problem has no known closed form solution, the proposed methods are asymptotically optimal as part of the estimation procedure in question. A new algorithm is also suggested for the related rank detection problem. The second part of this thesis treats direction of arrival estimation for narrowband signals using an array of sensors. Most algorithms require the noise covariance matrix to be known (up to a scaling factor) or to possess a known structure. In many cases the noise covariance is in fact estimated from a separate batch of signal-free samples. This work addresses the combined effects of finite sample sizes both in the estimated noise covariance matrix and in the data with signals present. No assumption is made on the structure of the noise covariance. The asymptotical covariance of weighted subspace fitting (WSF) is derived for the case in which the data are whitened using the noise covariance estimate. The obtained expression suggests an optimal weighting that improves performance compared to the standard choice. In addition, a new method based on covariance matching is proposed. Both methods are asymptotically statistically efficient. The Cramér-Rao bound for the problem is derived, and the expression becomes surprisingly simple.</p>

<p>Spectrum regulation is tricky and until recently the methods used for almost a century has sufficed. But as wireless communication has increased the demands on spectrum has increased. The regulators have responded by relaxing the current regulatory framework as well as opening up more bands for license exempt or unlicensed operation.</p><p>In unlicensed spectrum users can be expected to act greedily and possibly also break etiquette rules. Using game theory we find that in most cases a user benefits form acting greedily and this decrease total system capacity. It is possible to deter a user from cheating by applying punishment to the user. This function should preferably be incorporated in the access network.</p><p>We also study the case of networks competing in unlicensed spectrum and find that the most successful network is the one with lowest quality guarantees and with the most dense access network. In the case studied here the greedy behavior of the networks increases the spectrum utilization. We also evaluate a number of cases where two networks that cooperate in unlicensed spectrum. Isolation between the networks is the key factor to achieve better performance than splitting the spectrum.</p><p>The evaluations are carried out using numerical experiments and game theory. Game theory ia a powerful tool for modelling coexistence problems in unlicensed spectrum, but the systems are too complex to allow a fully analytical treatment.</p>

<p>In this thesis a terrain positioning method for underwater vehicles called the correlation method is presented. Using the method the vehicle can determine its absolute position with the help of a sonar and a map of the bottom topography. The thesis is focused towards underwater positioning but most of the material is directly applicable to flying vehicles as well. The positioning of surface vehicles has been revolutionized by the global positioning system (GPS). However, since the GPS signal does not penetrate into the sea water volume, underwater vehicles still have to use the inertial navigation system (INS) for navigation. Terrain positioning is therefore a serious alternative to GPS for underwater vehicles for zeroing out the INS error in military applications. The thesis begins with a review of different estimation methods as Bayesian and extended Kalman filter methods that have been used for terrain navigation. Some other methods that may be used as the unscented Kalman filter or solving the Fokker-Planck equation using finite element methods are also discussed. The correlation method is then described and the well known problem with multiple terrain positions is discussed. It is shown that the risk of false positions decreases exponentially with the number of measurement beams. A simple hypothesis test of false peaks is presented. It is also shown that the likelihood function for the position under weak assumptions converges to a Gaussian probability density function when the number of measuring beams tends to infinity. The Cramér-Rao lower bound on the position error covariance is determined and it is shown that the proposed method achieves this bound asymptotically. The problem with measurement bias causing position bias is discussed and a simple method for removing the measurement bias is presented. By adjusting the footprint of the measuring sonar beams to the bottom topography a large increase in accuracy and robustness can be achieved in many bottom areas. This matter is discussed and a systematic theory about how to choose way-points is developed. Three sea-trials have been conducted to verify the characteristics of the method and some results from the last one in October 2002 are presented. The sea-trials verify to a very high degree the theory presented. Finally the method is briefly discussed under the assumption that the bottom topography can be described by an autoregressive stochastic process.</p>

<p>Multi-robot systems have important applications, such as space explorations, underwater missions, and surveillance operations. In most of these cases robots need to exchange data through communication. Limitations in the communication system however impose constraints on the design of coordination strategies. In this thesis we present three papers on cooperative control problems in which different communication constraints are considered. The first paper describes a rendezvous problem for a team of robots that exchanges position information through communication. A local control law for each robot should steer the team to a common meeting point when communicated data are quantized. The robots are not equipped with any sensors so the positions of other teammates are not measured. Two different types of quantized communication are considered: uniform and logarithmic. Logarithmic quantization is often preferable since it requires that fewer bits are communicated compared to when uniform quantization is used. For a class of feasible communication topologies, control laws that solve the rendezvous problem are derived. A hierarchical control structure is proposed in the second paper, for modelling autonomous underwater vehicles employed in finding a minimum of a scalar field. The controller is composed of two layers. The upper layer is the team controller, which is modeled as discrete-event system. It generates waypoints based on the simplex search optimization algorithm. The waypoints are used as target points by the lower control layer, which continuously steers each vehicle from the current to the next waypoint. It is shown that the communication of measurements is needed at each step for the team controller to generate unique waypoints. A protocol is proposed to reduce the amount of data to be exchanged, motivated by that underwater communication is costly in terms of energy. In the third paper, a probabilistic pursuit{evasion game is considered as an example to study constrained communication in multi-robot systems. This system can be used to model search-and-rescue operations and multi-robot exploration. Communication protocols based on time-triggered and event-triggered synchronization schemes are considered. It is shown that by limiting the communication to events when the probabilistic map updated by the individual pursuer contains new information, as measured by a map entropy, the utilization of the communication link can be considerably improved compared to conventional time-triggered communication.</p>

Thesis (Ph. D.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 164-199).

Thesis (Ph. D.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 160 pages. Includes vita. Includes bibliographical references.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 2009. / Thesis Advisor(s): Fouts, Douglas J. ; Pace, Phillip E. "December 2009." Description based on title screen as viewed on January 27, 2010. Author(s) subject terms: Choi-Williams Distribution, Signal Processing, Algorithm Optimization, C programming, Low Probability of Intercept (LPI), Radar detection, Radar classification. Includes bibliographical references (p. 75-76). Also available in print.

Thesis (M.S. in Engineering Acoustics)--Naval Postgraduate School, December 2009. / Thesis Advisor(s): Kapolka, Daphne ; Smith, Kevin B. "December 2009." Description based on title screen as viewed on January 29, 2010. Author(s) subject terms: Particle velocity, beamforming, beam pattern, acoustic array, acoustic vector sensor, Microflown, hybrid array, frequency spectrum, linear array, directional. Includes bibliographical references (p. 151-153). Also available in print.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 2009. / Second Reader: Pace, Phillip E. "December 2009." Description based on title screen as viewed on January 27, 2010. Author(s) subject terms: Spectrum estimation, Frequency estimation, Frequency down-conversion, MUSIC, ESPRIT, MRSS. Includes bibliographical references (p. 89). Also available in print.