Flower constellation optimization and implementation

Satellite constellations provide the infrastructure to implement some of the most important global services of our times both in civilian and military applications, ranging from telecommunications to global positioning, and to observation systems. Flower Constellations constitute a set of satellite constellations characterized by periodic dynamics. They have been introduced while trying to augment the existing design methodologies for satellite constellations. The dynamics of a Flower Constellation identify a set of implicit rotating reference frames on which the satellites follow the same closed-loop relative trajectory. In particular, when one of these rotating reference frames is “Planet Centered, Planet Fixed”, then all the orbits become compatible (or resonant) with the planet; consequently, the projection of the relative path on the planet results in a repeating ground track. The satellite constellations design methodology currently most utilized is the Walker Delta Pattern or, more generally, Walker Constellations. The set of orbital planes and initial spacecraft positions are represented by a set of only three integers and two real parameters rather than by all the orbital elements; Flower Constellations provide a more general framework in which most of the former restrictions are removed, by allowing the use of resonant elliptical orbits. Flower Constellations can represent hundreds of spacecraft with a set of 6 integers and 5 real parameters only and existing constellations can be easily reproduced. How to design a Flower Constellation to satisfy specific mission requirements is an important problem for promoting the acceptance of this novel concept by the space community. Therefore one of the main goals of this work is that of proposing design techniques that can be applied to satisfy practical mission requirements. The results obtained by applying Global optimization techniques, such as Genetic Algorithms, to some relevant navigation and Earth observation space-based systems show that the Flower Constellations not only are as effective asWalker Constellations, but can also be applied to non-traditional constellation problem domains, such as regional coverage and reconnaissance

Flower Constellations: Optimization and Applications

Un satélite artificial es un objeto diseñado por el ser humano y lanzado al espacio mediante un vehículo espacial con el objetivo de sobrellevar una misión específica. El primer satélite artificial, orbitando en torno a la Tierra, fue lanzado in 1957 por la Unión Soviética, y su nombre es Sputnik I. Después de dicho evento, miles de satélites artificiales han sido lanzados en diferentes orbitas en torno a la Tierra. En muchas ocasiones, un satélite no es suficiente para tener éxito en una misión espacial, por lo que un grupo de satélites es necesario. Definiremos una constelación de satélites como un conjunto de satélites persiguiendo un objetivo común y operando de manera conjunta. En las últimas décadas el ser humano ha diseñado constelaciones de satélites con diferentes objetivos [1,2]; Global Positioning System (GPS), Galileo o GLONASS son ejemplos de constelaciones de satélites cuya finalidad es la navegación y la geodesia. La constelación estadounidense Orbcomm formada actualmente por 29 satélites operativos situados en órbitas bajas es un sistema de telecomunicación. Iridium y Globalstar son las competidoras directas de Orbcomm. Las constelaciones rusas Molniya y Tundra son sistemas de telecomunicación famosas por su gran excentricidad. Otros objetivos de las constelaciones pueden ser la observación de la Tierra, aplicaciones militares, la protección del ser humano (Disaster Monitoring Constellation), etc. Estos, entre muchos otros, son ejemplos concretos de constelaciones de satélites. Las constelaciones existentes utilizan, en general, orbitas circulares. Sin embargo, como Draim indica en su trabajo [3], las orbitas excéntricas podrían ser mejores que las circulares. Así, otra forma de diseñar constelaciones de satélites, sin la necesidad de tener órbitas circulares, era necesaria. Por ello, el Dr D. Daniele Mortari desarrollo en torno al año 2004 las "Flower Constellations" [4,5,6] que solucionan este problema dejando la excentricidad como otra variable libre. Estas constelaciones fueron extendidas en los años posteriores a las "Harmonic Flower Constellations" (HFC) [7], las "2D Lattice Flower Constellations" (2D-LFC) [8,9], que serán la principal herramienta en este trabajo, y finalmente las "3D Lattice Flower Constellations (3D-LFC)" [10]. Los problemas de cobertura regional y global constituyen el principal tema de investigación en torno a las constelaciones de satélites. En particular, el problema de posicionamiento global consiste en la determinación de la posición de un usuario con unos pocos centímetros de error en la precisión. Este problema requiere de al menos cuatro satélites visibles desde cualquier punto de la esfera terrestre en cualquier instante de tiempo, para lo que se requiere una geometría de la constelación bastante compleja [11,12]. El primer objetivo de este trabajo consiste en la búsqueda de 2D-LFCs cuya geometría sea óptima para la resolución del problema de posicionamiento global. El GDOP, del inglés Geometric Dilution of Precision [13], es la métrica que determina cómo de buena es la geometría de una constelación para encontrar la posición exacta de un usuario y el desfase horario entre el reloj del satélite y el del usuario. Por lo tanto, la métrica que define la optimalidad de las 2D-LFCs en nuestro problema es el máximo valor del GDOP experimentado desde cualquier punto de la superficie terrestre durante el tiempo de propagación. Por motivos prácticos, discretizamos el tiempo de propagación en pasos de 60 segundos y consideramos 30000 estaciones terrestres aleatoriamente distribuidas sobre la superficie terrestre con probabilidad uniforme. Los algoritmos evolutivos [14] son la principal herramienta para tratar este problema de optimización. En particular, en este trabajo utilizamos Algoritmos Genéticos y los "Particle Swarm Optimization Algorithms". Mediante este análisis, encontramos 2D-LFCs cuyos satélites presentan configuraciones que mejoran ligeramente el máximo valor del GDOP experimentado con respecto a las constelaciones existentes de Galileo y GLONASS. El gran costo computacional requerido para propagar las constelaciones y el enorme tamaño de nuestro espacio de búsqueda nos ha llevado a desarrollar diferentes técnicas que reducen el tiempo necesario para encontrar las soluciones óptimas. Dichas técnicas consisten en la reducción del espacio de búsqueda, así como la reducción del tiempo de propagación de manera que todo siga siendo matemáticamente correcto. Además, hemos utilizado técnicas de paralelización en la implementación de los algoritmos evolutivos. El análisis de este problema ha permitido comparar las diferentes técnicas de optimización empleadas, concluyendo que el "Particle Swarm Optimization Algorithm" es el método que mejores resultados proporciona en nuestra búsqueda. En este trabajo hemos realizado una búsqueda entre todas las 2D-LFCs posibles variando el número de satélites entre 18 y 40. Hemos obtenido resultados sorprendentes como sería el hecho de que con 27 satélites encontramos mejores configuraciones que con 28 satélites para resolver el problema de posicionamiento global. Puesto que nuestra 2D-LFC de 27 satélites sólo puede mejorarse añadiendo al menos dos satélites, concluimos que es una de las mejores constelaciones. Además, gracias a las 2D-LFCs hemos podido incluir órbitas excéntricas en nuestra búsqueda, encontrando algunas configuraciones óptimas cuyas órbitas presentan una excentricidad en torno a 0.3, muy distinta de la excentricidad nula que presentan las órbitas más usuales. En este trabajo hemos comparado la evolución del GDOP de nuestras 2D-LFCs óptimas con respecto a las existentes GLONASS Y Galileo, observando que nuestras constelaciones son ligeramente mejores debido a que el máximo valor del GDOP que obtenemos en cada instante es siempre menor. El estudio de las colisiones entre satélites en la constelación, es un problema intrínseco en nuestro problema de optimización puesto que si hay próxima una alineación de satélites, el GDOP en ese instante es elevado y automáticamente dicha constelación queda excluida en nuestra búsqueda. El estudio previo ha sido realizado en un modelo puramente Kepleriano. El siguiente paso para acercar nuestras constelaciones a una visión más realista consiste en introducir el problema de los dos cuerpos perturbado [15]. La Tierra es considerada como una esfera perfecta en el modelo Kepleriano. Sin embargo, como una primera aproximación a un modelo más realista, consideramos la Tierra como un sólido de revolución achatado por el centro (elipsoide). Esto nos lleva a incluir el zonal armónico J2 en la función potencial. La introducción de zonales armónicos de órdenes superiores no se considera en este trabajo ya que estos armónicos son al menos tres órdenes de magnitud menores que el J2 [16]. La introducción del zonal armónico J2 nos conduce a plantearnos el segundo problema tratado en esta tesis. Este problema consiste en la búsqueda de parámetros de una 2D-LFC para conseguir que sea estable, esto es, que los satélites de la constelación se vean afectados por las perturbaciones pero todos de la misma manera. De esta forma la posición relativa entre los satélites de la constelación (en el espacio de los elementos osculadores) quedará inalterada, obteniendo así las constelaciones que tienen por nombre "Rigid Constellations". La mayoría de autores que trabajan el problema principal del satélite promedian las perturbaciones no seculares en un periodo orbital, considerando únicamente las perturbaciones de largo periodo y las perturbaciones seculares [17]. En este trabajo, consideramos las perturbaciones seculares y no seculares (de largo y corto periodo) que afectan a la aceleración del satélite. Por ello, en lugar de promediar la expresión del potencial en un periodo orbital, consideramos la expresión completa de la función potencial [16]. Con la expresión completa del potencial y haciendo uso de las Ecuaciones de Lagrange [15] podemos estudiar la evolución de los elementos orbitales en el tiempo. Los objetivos principales son controlar la perturbación secular para que sea idéntica en todos los satélites de la constelación y minimizar las perturbaciones no seculares que afectan a nuestros satélites. Si logramos estos objetivos los satélites de la constelación se verán perturbados por el efecto del J2 de la misma manera. De esta forma las posiciones relativas de los satélites serán prácticamente constantes (en el espacio de los elementos osculadores) y la estructura de "Flower Constellation" se mantendrá con el paso del tiempo, lo que denominamos como "Rigid Constellation". Para controlar la parte secular de los satélites de la constelación consideramos un satélite de referencia. Primero estudiamos la dependencia de la parte secular de los elementos osculadores con respecto a los valores iniciales del ángulo del nodo (RAAN) y de la anomalía media (M). Observamos que ninguna de las componentes seculares depende del valor del valor del ángulo del nodo, pero observamos una fuerte dependencia con respecto al valor de la anomalía media. En el caso particular de una 2D-LFC, todos los satélites tienen los mismos valores del semieje (a), excentricidad (e), inclinación (i) y argumento del perigeo, pero tienen distintos los valores del ángulo del nodo y de la anomalía media. Por lo que, a priori, la componente secular de los elementos osculadores de cada satélite será distinta. Para conseguir que sea idéntica, aplicamos un método de corrección. Dicho método consiste en modificar el semieje mayor de todos los satélites unos pocos kilómetros. De esta forma el periodo orbital (Tp) se verá modificado y en particular la componente secular de la variación de la anomalía media en el tiempo. A través de esta corrección, conseguimos que la componente secular de los elementos osculadores de cada uno de los satélites de la constelación coincida hasta un orden de 10^{-11}. Con esta técnica, conseguimos controlar la perturbación secular de nuestros satélites. Tratar de controlar la parte no secular resulta algo más complicado. En primer lugar, aplicamos para cada elemento osculador interpolación lineal sobre los datos que han sido obtenidos previamente para calcular la posición exacta de los satélites. A través de estas funciones lineales de los elementos osculadores somos capaces de calcular en cada instante de tiempo una posición aproximada o lineal. De tal manera que la distancia entre ambas posiciones (real y lineal) será debida a las perturbaciones no seculares que afectan a nuestro satélite de referencia. El objetivo final consiste en analizar entre los posibles valores de la excentricidad y la inclinación aquellos que minimicen esta distancia (desviación). De esta forma, minimizamos la perturbación no secular que afecta a nuestro satélite de referencia. Estos valores serán extrapolables al resto de satélites de nuestra constelación. Consecuentemente, la perturbación no secular que afecta a los satélites de la constelación queda minimizada. Mediante este trabajo somos capaces de diseñar 2D-LFCs cuya configuración se mantiene bajo los efectos del J2, obteniendo las denominadas "Rigid Constellations". La teoría que hemos desarrollado tiene dos aplicaciones directas. La primera consiste en validar la teoría de las 3D-LFCs, en el caso en que la función potencial no sea promediada en un periodo orbital, asumiendo que los semiejes son corregidos y el valor de la desviación es lo más pequeño posible. La segunda aplicación sirve para resolver problemas de cobertura global en los que se incluye el efecto del zonal J2 en el potencial. Será suficiente con encontrar una "Rigid Constellation" que minimice una función "fitness" ligeramente modificada y podremos propagar los satélites en un modelo Kepleriano. Nuestro último objetivo consiste en reducir el elevado número de satélites que por lo general componen una constelación simétrica. Proporcionamos un método para determinar todos los subconjuntos de satélites de las 2D-LFCs de tal forma que sigan manteniendo las simetrías que las caracterizan [18]. Para conseguir este objetivo hemos identificado la primera órbita de nuestra constelación, que posee Nso posiciones admisibles con un collar G (en inglés, "necklace") de Nso perlas [19]. Tomamos un número Nrso (Nrso < Nso) representando los satélites reales por órbita. De esta forma consideramos la primera órbita de la constelación como un "necklace" de Nso perlas, de las cuales Nrso son negras y el resto blancas. Esto es, una órbita con Nso posiciones admisibles, de las cuales Nrso están ocupadas por un satélite y el resto no. La distribución de los satélites en las restantes órbitas es idéntica a la primera, pero desplazados k perlas. De este modo una "Necklace Flower Constellation" (NFC) se caracteriza mediante un par (G, k). Notar que, no todos los pares producen NFC validas, ni dos pares distintos producen distintas NFC. Estos dos problemas se denominan problema de consistencia y de minimalidad, respectivamente. Utilizando teoría de números [20] somos capaces de resolverlos completamente. Finalmente, desarrollamos diversos teoremas de conteo para determinar la cantidad de pares posibles (G, k) que existen a partir de los parámetros de distribución de una 2D-LFC. Las constelaciones de satélites son un tema de candente actualidad por las posibilidades que pueden proporcionar para los servicios comerciales e institucionales en aplicaciones como las telecomunicaciones, el posicionamiento dinámico o la observación de la Tierra, con costos razonables. Los resultados obtenidos en este trabajo estimulan el estudio de las mismas, que pueden resultar, en un futuro próximo, en constelaciones más eficientes que las actuales para diversas misiones espaciales

Resource Optimization in Wireless Sensor Networks for an Improved Field Coverage and Cooperative Target Tracking

There are various challenges that face a wireless sensor network (WSN) that mainly originate from the limited resources a sensor node usually has. A sensor node often relies on a battery as a power supply which, due to its limited capacity, tends to shorten the life-time of the node and the network as a whole. Other challenges arise from the limited capabilities of the sensors/actuators a node is equipped with, leading to complication like a poor coverage of the event, or limited mobility in the environment. This dissertation deals with the coverage problem as well as the limited power and capabilities of a sensor node. In some environments, a controlled deployment of the WSN may not be attainable. In such case, the only viable option would be a random deployment over the region of interest (ROI), leading to a great deal of uncovered areas as well as many cutoff nodes. Three different scenarios are presented, each addressing the coverage problem for a distinct purpose. First, a multi-objective optimization is considered with the purpose of relocating the sensor nodes after the initial random deployment, through maximizing the field coverage while minimizing the cost of mobility. Simulations reveal the improvements in coverage, while maintaining the mobility cost to a minimum. In the second scenario, tracking a mobile target with a high level of accuracy is of interest. The relocation process was based on learning the spatial mobility trends of the targets. Results show the improvement in tracking accuracy in terms of mean square position error. The last scenario involves the use of inverse reinforcement learning (IRL) to predict the destination of a given target. This lay the ground for future exploration of the relocation problem to achieve improved prediction accuracy. Experiments investigated the interaction between prediction accuracy and terrain severity. The other WSN limitation is dealt with by introducing the concept of sparse sensing to schedule the measurements of sensor nodes. A hybrid WSN setup of low and high precision nodes is examined. Simulations showed that the greedy algorithm used for scheduling the nodes, realized a network that is more resilient to individual node failure. Moreover, the use of more affordable nodes stroke a better trade-off between deployment feasibility and precision

Optimization of anchor nodes placement in wireless localization networks

This work focuses on optimizing node placement for time-of-flight-based wireless localization networks. Main motivation are critical safety applications. The first part of my thesis is an experimental study on in-tunnel vehicle localization. In- tunnel localization of vehicles is crucial for emergency management, especially for large trucks transporting dangerous goods such as inflammable chemicals. Compared to open roads, evacuation in tunnels is much more difficult, so that fire or other accidents can cause much more damage. We provide distance measurement error characterization inside road tunnels focusing on time of flight measurements. We design a complete system for in-tunnel radio frequency time-of- flight-based localization and show that such a system is feasible and accurate, and that few nodes are sufficient to cover the entire tunnel. The second part of my work focuses on anchor nodes placement optimization for time-of-flight-based localization networks where multilateration is used to obtain the target position based on its distances from fixed and known anchors. Our main motivation are safety at work applications, in particular, environments such as factory halls. Our goal is to minimize the number of anchors needed to localize the target while keeping the localization uncertainty lower than a given threshold in an area of arbitrary shape with obstacles. Our propagation model accounts for the presence of line of sight between nodes, while geometric dilution of precision is used to express the localization error introduced by multilateration. We propose several integer linear programming formulations for this problem that can be used to obtain optimal solutions to instances of reasonable sizes and compare them in terms of execution times by simulation experiments. We extend our approach to address fault tolerance, ensuring that the target can still be localized after any one of the nodes fails. Two dimensional localization is sufficient for most indoor applications. However, for those industrial environments where the ceiling is very high and the worker might be climbing or be lifted from the ground, or if very high localization precision is needed, three-dimensional localization may be required. Therefore, we extend our approach to three-dimensional localization. We derive the expression for geometric dilution of precision for 3D multilateration and give its geometric interpretation. To tackle problem instances of large size, we propose two novel heuristics: greedy placement with pruning, and its improved version, greedy placement with iterative pruning. We create a simulator to test and compare all our proposed approaches by generating multiple test instances. For anchor placement for multilateration-based localization, we obtain solutions with below 2% anchors overhead with respect to the optimum on average, with around 5s average execution time for 130 candidate positions. For the fault-tolerant version of the same problem, we obtain solutions of around 1% number of anchors overhead with respect to the optimum on average, with 0.4s execution time for 65 candidate positions, by using greedy heuristic with pruning. For 3D placement, the greedy heuristic with iterative pruning produced results of 0.05% of optimum on average, with average execution time of around 6s for 250 candidate positions, for the problem instances we tested

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Constellation Reconfiguration: Tools and Analysis

Constellation reconfi guration consists of transforming an initial constellation of satellites into some final constellation of satellites to maintain system optimality. Constellations with phased deployment, changing mission requirements, or satellite failures would all benefi t from reconfi guration capability. The constellation reconfiguration problem can be broken into two broad sub-problems: constellation design and constellation transfer. Both are complicated and combinatorial in nature and require new, more efficient methods. Having reviewed existing constellation design frameworks, a new framework, the Elliptical Flower Constellations (EFCs), has been developed that offers improved performance over traditional methods. To assist in rapidly analyzing constellation designs, a new method for orbit propagation based on a sequential solution of Kepler's equation is presented. The constellation transfer problem requires an optimal assignment of satellites in the initial orbit to slots in the final orbit based on optimal orbit transfers between them. A new method for approximately solving the optimal two-impulse orbit transfer with fixed end-points, the so-called minimum Delta v Lambert's problem, is developed that requires the solution of a 4th order polynomial, as opposed to the 6th or higher order polynomials or iterative techniques of existing methods. The recently developed Learning Approach to sampling optimization is applied to the particular problem of general orbit transfer between two generic orbits, with several enhancements specifi c to this problem that improve its performance. The constellation transfer problem is then posed as a Linear Assignment Problem and solved using the auction algorithm once the orbit transfers have been computed. Constellations designed for global navigation satellite systems and for global communications demonstrate signifi cant improvements through the use of the EFC framework over existing methods. An end-to-end example of constellation recon figuration for a constellation with changing regional coverage requirements shows the effectiveness of the constellation transfer methods

The Future of the Operating Room: Surgical Preplanning and Navigation using High Accuracy Ultra-Wideband Positioning and Advanced Bone Measurement

This dissertation embodies the diversity and creativity of my research, of which much has been peer-reviewed, published in archival quality journals, and presented nationally and internationally. Portions of the work described herein have been published in the fields of image processing, forensic anthropology, physical anthropology, biomedical engineering, clinical orthopedics, and microwave engineering. The problem studied is primarily that of developing the tools and technologies for a next-generation surgical navigation system. The discussion focuses on the underlying technologies of a novel microwave positioning subsystem and a bone analysis subsystem. The methodologies behind each of these technologies are presented in the context of the overall system with the salient results helping to elucidate the difficult facets of the problem. The microwave positioning system is currently the highest accuracy wireless ultra-wideband positioning system that can be found in the literature. The challenges in producing a system with these capabilities are many, and the research and development in solving these problems should further the art of high accuracy pulse-based positioning

Joint Communication and Positioning based on Channel Estimation

Mobile wireless communication systems have rapidly and globally become an integral part of everyday life and have brought forth the internet of things. With the evolution of mobile wireless communication systems, joint communication and positioning becomes increasingly important and enables a growing range of new applications. Humanity has already grown used to having access to multimedia data everywhere at every time and thereby employing all sorts of location-based services. Global navigation satellite systems can provide highly accurate positioning results whenever a line-of-sight path is available. Unfortunately, harsh physical environments are known to degrade the performance of existing systems. Therefore, ground-based systems can assist the existing position estimation gained by satellite systems. Determining positioning-relevant information from a unified signal structure designed for a ground-based joint communication and positioning system can either complement existing systems or substitute them. Such a system framework promises to enhance the existing systems by enabling a highly accurate and reliable positioning performance and increased coverage. Furthermore, the unified signal structure yields synergetic effects. In this thesis, I propose a channel estimation-based joint communication and positioning system that employs a virtual training matrix. This matrix consists of a relatively small training percentage, plus the detected communication data itself. Via a core semi- blind estimation approach, this iteratively includes the already detected data to accurately determine the positioning-relevant parameter, by mutually exchanging information between the communication part and the positioning part of the receiver. Synergy is created. I propose a generalized system framework, suitable to be used in conjunction with various communication system techniques. The most critical positioning-relevant parameter, the time-of-arrival, is part of a physical multipath parameter vector. Estimating the time-of-arrival, therefore, means solving a global, non-linear, multi-dimensional optimization problem. More precisely, it means solving the so-called inverse problem. I thoroughly assess various problem formulations and variations thereof, including several different measurements and estimation algorithms. A significant challenge, when it comes to solving the inverse problem to determine the positioning-relevant path parameters, is imposed by realistic multipath channels. Most parameter estimation algorithms have proven to perform well in moderate multipath environments. It is mathematically straightforward to optimize this performance in the sense that the number of observations has to exceed the number of parameters to be estimated. The typical parameter estimation problem, on the other hand, is based on channel estimates, and it assumes that so-called snapshot measurements are available. In the case of realistic channel models, however, the number of observations does not necessarily exceed the number of unknowns. In this thesis, I overcome this problem, proposing a method to reduce the problem dimensionality via joint model order selection and parameter estimation. Employing the approximated and estimated parameter covariance matrix inherently constrains the estimation problem’s model order selection to result in optimal parameter estimation performance and hence optimal positioning performance. To compare these results with the optimally achievable solution, I introduce a focused order-related lower bound in this thesis. Additionally, I use soft information as a weighting matrix to enhance the positioning algorithm positioning performance. For demonstrating the feasibility and the interplay of the proposed system components, I utilize a prototype system, based on multi-layer interleave division multiple access. This proposed system framework and the investigated techniques can be employed for multiple existing systems or build the basis for future joint communication and positioning systems. The assessed estimation algorithms are transferrable to all kinds of joint communication and positioning system designs. This thesis demonstrates their capability to, in principle, successfully cope with challenging estimation problems stemming from harsh physical environments

Multistatic radar optimization for radar sensor network applications

The design of radar sensor networks (RSN) has undergone great advancements in recent years. In fact, this kind of system is characterized by a high degree of design flexibility due to the multiplicity of radar nodes and data fusion approaches. This thesis focuses on the development and analysis of RSN architectures to optimize target detection and positioning performances. A special focus is placed upon distributed (statistical) multiple-input multipleoutput (MIMO) RSN systems, where spatial diversity could be leveraged to enhance radar target detection capabilities. In the first part of this thesis, the spatial diversity is leveraged in conjunction with cognitive waveform selection and design techniques to quickly adapt to target scene variations in real time. In the second part, we investigate the impact of RSN geometry, particularly the placement of multistatic radar receivers, on target positioning accuracy. We develop a framework based on cognitive waveform selection in conjunction with adaptive receiver placement strategy to cope with time-varying target scattering characteristics and clutter distribution parameters in the dynamic radar scene. The proposed approach yields better target detection performance and positioning accuracy as compared with conventional methods based on static transmission or stationary multistatic radar topology. The third part of this thesis examines joint radar and communication systems coexistence and operation via two possible architectures. In the first one, several communication nodes in a network operate separately in frequency. Each node leverages the multi-look diversity of the distributed system by activating radar processing on multiple received bistatic streams at each node level in addition to the pre-existing monostatic processing. This architecture is based on the fact that the communication signal, such as the Orthogonal Frequency Division Multiplexing (OFDM) waveform, could be well-suited for radar tasks if the proper waveform parameters are chosen so as to simultaneously perform communication and radar tasks. The advantage of using a joint waveform for both applications is a permanent availability of radar and communication functions via a better use of the occupied spectrum inside the same joint hardware platform. We then examine the second main architecture, which is more complex and deals with separate radar and communication entities with a partial or total spectrum sharing constraint. We investigate the optimum placement of radar receivers for better target positioning accuracy while reducing the radar measurement errors by minimizing the interference caused by simultaneous operation of the communication system. Better performance in terms of communication interference handling and suppression at the radar level, were obtained with the proposed placement approach of radar receivers compared to the geometric dilution of precision (GDOP)-only minimization metric