The motivation of having a joint radar and communication system on a single hardware is driven by space, military, and commercial applications. However, designing sequences that can simultaneously support radar and communication functionalities is one of the major hurdles in the practical implementation of these systems. In order to facilitate a simultaneous use of sequences for both radar and communication systems, a flexible sequence design is needed.

The objective of this dissertation is to address the sequence design problem for integrated radar and communication systems. The sequence design for these systems requires a trade-off between different performance measures, such as correlation characteristics, integrated sidelobe ratio, peak-to-sidelobe ratio and ambiguity function. The problem of finding a trade-off between various performance measures is solved by employing meta-heuristic algorithms.

This dissertation is divided into an introduction and three research parts based on peer-reviewed publications. The introduction provides background on binary and polyphase sequences, their use in radar and communication systems, sequence design requirements for integrated radar and communication systems, and application of meta-heuristic optimization algorithms to find optimal sets of sequences for these systems.

In Part I-A, the performance of conventional polyphase pulse compression sequences is compared with Oppermann sequences. In Part I-B, weighted pulse trains with the elements of Oppermann sequences serving as complex-valued weights are utilized for the design of integrated radar and communication systems. In Part I-C, an analytical expression for the cross-ambiguity function of weighted pulse trains with Oppermann sequences is derived. Several properties of the related auto-ambiguity and cross-ambiguity functions are derived in Part I-D. In Part II, the potential of meta-heuristic algorithms for finding optimal parameter values of Oppermann sequences for radar, communications, and integrated radar and communication systems is studied. In Part III-A, a meta-heuristic algorithm mimicking the breeding behavior of Cuckoos is used to locate more than one solution for multimodal problems. Further, the performance of this algorithm is evaluated in additive white Gaussian noise (AWGN). It is shown that the Cuckoo search algorithm can successfully locate multiple solutions in both non-noise and AWGN with relatively high degree of accuracy. In Part III-B, the cross-ambiguity function synthesization problem is addressed. A meta-heuristic algorithm based on echolocation of bats is used to design a pair of sequences to minimize the integrated square error between the desired cross-ambiguity function and a synthesized cross-ambiguity function.