Joint waveform and guidance control optimisation for target rendezvous

The algorithm developed in this paper jointly selects the optimal transmitted waveform and the control input so that a radar sensor on a moving platform with linear dynamics can reach a target by minimising a predefined cost. The cost proposed in this paper accounts for the energy of the transmitted radar signal, the energy of the platform control input and the relative position error between the platform and the target, which is a function of the waveform design and control input. Similarly to the Linear Quadratic Gaussian (LQG) control problem, we demonstrate that the optimal solution satisfies the separation principle between filtering and optimisation and, therefore, the optimum can be found analytically. The performance of the proposed solution is assessed with a set of simulations for a pulsed Doppler radar transmitting linearly frequency modulated chirps. Results show the effectiveness of the proposed approach for optimal waveform design and optimal guidance control

Adaptive MIMO Radar for Target Detection, Estimation, and Tracking

We develop and analyze signal processing algorithms to detect, estimate, and track targets using multiple-input multiple-output: MIMO) radar systems. MIMO radar systems have attracted much attention in the recent past due to the additional degrees of freedom they offer. They are commonly used in two different antenna configurations: widely-separated: distributed) and colocated. Distributed MIMO radar exploits spatial diversity by utilizing multiple uncorrelated looks at the target. Colocated MIMO radar systems offer performance improvement by exploiting waveform diversity. Each antenna has the freedom to transmit a waveform that is different from the waveforms of the other transmitters. First, we propose a radar system that combines the advantages of distributed MIMO radar and fully polarimetric radar. We develop the signal model for this system and analyze the performance of the optimal Neyman-Pearson detector by obtaining approximate expressions for the probabilities of detection and false alarm. Using these expressions, we adaptively design the transmit waveform polarizations that optimize the target detection performance. Conventional radar design approaches do not consider the goal of the target itself, which always tries to reduce its detectability. We propose to incorporate this knowledge about the goal of the target while solving the polarimetric MIMO radar design problem by formulating it as a game between the target and the radar design engineer. Unlike conventional methods, this game-theoretic design does not require target parameter estimation from large amounts of training data. Our approach is generic and can be applied to other radar design problems also. Next, we propose a distributed MIMO radar system that employs monopulse processing, and develop an algorithm for tracking a moving target using this system. We electronically generate two beams at each receiver and use them for computing the local estimates. Later, we efficiently combine the information present in these local estimates, using the instantaneous signal energies at each receiver to keep track of the target. Finally, we develop multiple-target estimation algorithms for both distributed and colocated MIMO radar by exploiting the inherent sparsity on the delay-Doppler plane. We propose a new performance metric that naturally fits into this multiple target scenario and develop an adaptive optimal energy allocation mechanism. We employ compressive sensing to perform accurate estimation from far fewer samples than the Nyquist rate. For colocated MIMO radar, we transmit frequency-hopping codes to exploit the frequency diversity. We derive an analytical expression for the block coherence measure of the dictionary matrix and design an optimal code matrix using this expression. Additionally, we also transmit ultra wideband noise waveforms that improve the system resolution and provide a low probability of intercept: LPI)

An evolutionary algorithm approach to simultaneous multi-mission radar waveform design

It would be beneficial with today’s cluttered electromagnetic spectrum to be able to perform multiple radar missions simultaneously from a single platform. The design of a waveform for this application would greatly benefit the radar community. Radar systems are used to perform many missions, some of which include the detection and tracking of airborne and ground moving targets as well as Synthetic Aperture Radar (SAR) imaging. There are many systems that can operate in multiple modes to perform these missions, although there is no one radar that can simultaneously perform multiple missions using the same waveform [1]. Each mission can be mathematically reduced to an objective or set of objectives that can be used to evaluate their success. These objectives are functions of numerous radar and spatial parameters such as pulse repetition frequency (prf), center frequency, bandwidth, antenna beamwidth, and azimuth look angle, among others. In this thesis, an evolutionary multi-objective optimization technique known as the Strength Pareto Evolutionary Algorithm 2 (SPEA2), developed by Zitzler and Thiele [2], was applied to the simultaneous multi-mission radar waveform design problem. Several of the radar parameters mentioned above were varied to produce diverse waveforms that were manipulated using SPEA2. Due to computational constraints, the problem was approached by using two different scaled down real world scenarios to evaluate the performance of the evolutionary waveform design on a multi-objective moving target indication (MTI) mission and a multi-objective SAR mission, respectively. Multiple experiments showed that SPEA2 can select a set of Pareto optimal waveforms that accomplish these multi-objective missions effectively according to the objective functions that were developed for these missions. Finally, a procedure is outlined to combine these multi-objective MTI and SAR missions into one scaled experiment in which a distributed computing environment could be used to provide more computational resources

Multi-mission radar waveform design via a distributed SPEA2 genetic algorithm

This thesis furthers the development of Genetic Algorithms (GAs) and their application to the design of multi-mission radar waveforms. An application was developed with the goal of developing a waveform suite that finds the Pareto optimal solutions to a multi-objective optimization radar problem. Utilizing the Strength Pareto Evolutionary Algorithm 2 (SPEA2) a series of radar parameters are optimized along the fitness metrics of interest. This implementation builds upon previous work to develop an application that is capable of analyzing longer more realistic scenarios by using a distributed grid computer to spread the computational load across multiple CPUs. It also advances the previous research by solving for the Pareto optimal front of a simultaneous Synthetic Aperture Radar (SAR) and Moving Target Indication (MTI) mission. These results are presented to validate the performance of the new distributed application against previous work and introduce results of larger more realistic scenarios for a multi-mission radar suite