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In this paper, we study the joint design of Doppler robust transmit sequence and receive filter to improve the performance of an active sensing system dealing with signal-dependent interference. The signal-to-noise-plus-interference (SINR) of the filter output is considered as the performance measure of the system. The design problem is cast as a max-min optimization problem to robustify the system SINR with respect to the unknown Doppler shifts of the targets. To tackle the design problem, which belongs to a class of NP-hard problems, we devise a novel method (which we call DESIDE) to obtain optimized pairs of transmit sequence and receive filter sharing the desired robustness property. The proposed method is based on a cyclic maximization of SINR expressions with relaxed rank-one constraints, and is followed by a novel synthesis stage. We devise synthesis algorithms to obtain high quality pairs of transmit sequence and receive filter that well approximate the behavior of the optimal SINR (of the relaxed problem) with respect to target Doppler shift. Several numerical examples are provided to analyze the performance obtained by DESIDE.

In this paper, we propose a recursive method for finding Costas arrays that relies on a particular formation of Costas arrays from similar patterns of smaller size. By using such an idea, the proposed algorithm is able to dramatically reduce the computational burden (when compared to the exhaustive search), and at the same time, still can find all possible Costas arrays of given size. Similar to exhaustive search, the proposed method can be conveniently implemented in parallel computing. The efficiency of the method is discussed based on theoretical and numerical results.

In this paper, we study the problem of approaching peak periodic or aperiodic correlation bounds for complex-valued sets of sequences. In particular, novel algorithms based on alternating projections are devised to approach a given peak periodic or aperiodic correlation bound. Several numerical examples are presented to assess the tightness of the known correlation bounds as well as to illustrate the effectiveness of the proposed methods for meeting these bounds.

The NP-hard problem of optimizing a quadratic form over the unimodular vector set arises in radar code design scenarios as well as other active sensing and communication applications. To tackle this problem (which we call unimodular quadratic program (UQP)), several computational approaches are devised and studied. Power method-like iterations are introduced for local optimization of UQP. Furthermore, a monotonically error-bound improving technique (MERIT) is proposed to obtain the global optimum or a local optimum of UQP with good sub-optimality guarantees. The provided sub-optimality guarantees are case-dependent and may outperform the pi/4 approximation guarantee of semi-definite relaxation. Several numerical examples are presented to illustrate the performance of the proposed method. The examples show that for several cases, including rank-deficient matrices, the proposed methods can solve UQPs efficiently in the sense of sub-optimality guarantee and computational time.

The NP-hard problem of optimizing a quadratic form over the unimodular vector set arises in radar code design scenarios as well as other active sensing and communication applications. To tackle this problem, a monotonically error-bound improving technique (MERIT) is proposed to obtain the global optimum or a local optimum of UQP with good sub-optimality guarantees. The provided sub-optimality guarantees are case-dependent and may outperform the pi/4 approximation guarantee of semi-definite relaxation.

In this paper, we study the problem of meeting peak periodic or aperiodic correlation bounds for complex-valued sets of sequences. To this end, the Welch, Levenstein, and Exponential bounds on the peak inner-product of sequence sets are considered and used to provide compound peak correlation bounds in both periodic and aperiodic cases. The peak aperiodic correlation bound is further improved by using the intrinsic dimension deficiencies associated with its formulation. In comparison to the compound bound, the new aperiodic bound contributes an improvement of more than 35% for some specific values of the sequence length n and set cardinality m. We study the tightness of the provided bounds by using both analytical and computational tools. In particular, novel algorithms based on alternating projections are devised to approach a given peak periodic or aperiodic correlation bound. Several numerical examples are presented to assess the tightness of the provided correlation bounds as well as to illustrate the effectiveness of the proposed methods for meeting these bounds.

In this paper, Perfect Root-of-Unity Sequences (PRUS) with entries in $\alpha_p = \{ x \in \complexC ~ |~ x^p =1\}$ (where $p$ is a prime) are studied. A lower bound on the number of distinct phases that are used in PRUS over $\alpha_p$ is derived. We show that PRUS of length $L \geq p(p-1)$ must use all phases in $\alpha_p$. Certain conditions on the lengths of PRUS are derived. Showing that the phase values of PRUS must follow a given difference multiset property, we derive a set of equations (which we call the principal equations) that give possible lengths of a PRUS over $\alpha_p$ together with their phase distributions. The usefulness of the principal equations is discussed, and guidelines for efficient construction of PRUS are provided. Through numerical results, also contributions are made to the current state-of-knowledge regarding the existence of PRUS. In particular, a combination of the developed ideas allowed us to numerically settle the problem of existence of PRUS with $(L,p)=(28,7)$ within about two weeks--- a problem whose solution (without using the ideas in this paper) would likely take more than three million years on a standard PC.