Target parameter estimation in active sensing, and particularly radar signal processing, is a long-standing problem that has been studied extensively. In this paper, we propose a novel approach for target parameter estimation in cases where one-bit analog-to-digital-converters (ADCs), also known as signal comparators with time-varying thresholds, are employed to sample the received radar signal instead of high-resolution ADCs. The considered problem has potential applications in the design of inexpensive radar and sensing devices in civilian applications, and can likely pave the way for future radar systems employing low-resolution ADCs for faster sampling and high-resolution target determination. We formulate the target estimation as a multivariate weighted-least-squares optimization problem that can be solved in a cyclic manner. Numerical results are provided to exhibit the effectiveness of the proposed algorithms.

Binary sequence sets with asymptotically optimal auto/cross-correlation peak sidelobe level (PSL) growth have been known in the literature for a long time, and their construction has been studied both analytically and numerically. In contrast, it has been a long-standing problem whether we can construct a family of binary sequences whose auto-correlation PSL grows in an optimal manner. In this paper, we devise a construction method of binary sequences with asymptotically optimal PSL growth from the sequence sets with good correlation properties. A key component of the design follows from the observation that if the PSL of the sequence set grows optimally or nearly optimally, then the PSL of the constructed binary sequence will experience a similar growth as a consequence. The proposed construction is simple-to-implement, and is shown to be accomplished in polynomial-time. With such a construction, we not only bridge the gap between analytical construction and computational search, but also pave the way to settle the long-standing design problem of binary sequences with an optimal growth of the auto-correlation PSL.

The number of commercial and civilian vehicles equipped with automotive radars is expected to rise rapidly in the forthcoming years and with that comes the problem of increased mutual interference between the radar sensors, which can result in severely reduced radar sensitivity and increased false alarm rates. The difficulty and complexity of the problem increase with MIMO radar systems and multiply even further with a growing number of vehicles present on the scene. A system of connected vehicles can efficiently address this problem by sharing information amongst themselves. In this paper, we propose an efficient waveform design algorithm that seeks to minimize a collective cross-ambiguity function. Vehicles that can talk to each other, can perform the design online in a collaborative manner, or offline, in which case the radar codes can be designed and stored in a codebook for future use. The proposed coding scheme is computationally efficient for practical use and the incorporation of such schemes requires only a slight modification of the existing systems. Our numerical examples indicate that the proposed scheme can significantly reduce the interference power level in a desired area of the radar cross-ambiguity functions.

In this paper, we investigate the problem of jointly optimizing the waveform covariance matrix and the antenna position vector for multiple-input-multiple-output (MIMO) radar systems to approximate a desired transmit beampattern as well as to minimize the cross-correlation of the received signals reflected back from the targets. We formulate the problem as a non-convex program and then propose a cyclic optimization approach to efficiently tackle the problem. We further propose a novel local optimization framework in order to efficiently design the corresponding antenna positions. Our numerical investigations demonstrate a good performance both in terms of accuracy and computational complexity, making the proposed framework a good candidate for real-time radar signal processing applications.

Unimodular sequences with good correlation properties have attracted significant research interest due to their applications in several areas such as radar sensing and communications. In this paper, we address the problem of designing unimodular sequence sets with good correlation and complementary correlation properties using generalized cyclic algorithms for the minimization of weighted integrated sidelobe level (WISL) based metrics. The set of sequences are obtained by considering their complete second-order characterization, which has been proven to be beneficial for identification and sensing systems employing widely linear signal processing. Several numerical examples have been presented to illustrate the performance of the proposed algorithms.