Environmental sensing can be achieved using Wireless Sensor Networks (WSNs) with low-complexity sensors. Integrated sensing and communication (ISAC) allows simultaneous sensing and data transmission through optimized waveform, architecture, and protocol design. Given the multiple sensors in WSNs, minimizing interference and ensuring Doppler-tolerant pulses for detecting dynamic targets is crucial. This paper proposes a Doppler-tolerant waveform design that embeds multiple communication symbols within each pulse of a coherent processing interval (CPI). We formulate a Sum-of-Squares (SoS) optimization problem to minimize the cross-correlation integrated sidelobe level (CISL) for a MIMO sensor arrangement, constrained by unwrapped quadratic phase behavior. Simulation results confirm the effectiveness of the proposed approach.

Automotive radar sensors are vital for enhancing vehicle safety and autonomy, enabling functionalities such as adaptive cruise control and collision avoidance. The performance of these radar systems is highly dependent on the selected waveform. There are several waveform options for automotive radars, including Frequency Modulated Continuous Wave (FMCW), Phase Modulated Continuous Wave (PMCW), Orthogonal Frequency Division Multiplexing (OFDM), and Orthogonal Time Frequency Space (OTFS). Each offers unique benefits and tradeoffs concerning range resolution, Doppler resolution, resistance to multipath fading, and complexity. Notably, OTFS, as a promising technique in Integrated Sensing And Communication (ISAC), outperforms OFDM in high-mobility scenarios for communication systems. In this paper, we design a phase-perturbation matrix using the Coordinate Descent (CD) framework, to optimize the range Integrated Side-lobe Level (ISL) of OTFS waveform in automotive radar applications. We also examine the Ambiguity Function (AF) characteristics of OTFS compared to OFDM, PMCW, and FMCW. The simulation results demonstrate an improvement in the ISL values of the designed OTFS waveforms.

In this paper, we present a generic signal model applicable to various distributed radar setups, encompassing both phased array (PA) and MIMO radar configurations. We consider a range of waveform modulation methods, including TDM, BPM, DDM, and fast time CDM. We devise a GLRT based detector for scenarios where the interference consists of colored noise plus a signal in a low-rank subspace and prove that the designed detector is CFAR. We demonstrate that when the CPI time is similar for the systems, the PA radar system exhibits better detection performance than MIMO, irrespective of the waveform modulation approach adopted. However, if the CPI time of the PA system is divided to the number of transmit waveforms utilized in the MIMO radar case (to account for the time needed for a PA radar to scan all angles), then in the presence of non-uniform interference, MIMO techniques, except TDM, surpass the performance of PA. Conversely, in cases of uniform interference, the performance of both MIMO techniques and PA are equivalent.

Driven by emerging applications, mmWave radars are increasingly being integrated into indoor scene monitoring systems due to their ability to provide high accuracy range, velocity, and angle information of the objects. This paper addresses the problem of moving target detection in a connected, distributed Multiple-Input Multiple-Output (MIMO) radar sensor network designed as an indoor scene monitoring system. We propose a general signal model for distributed connected MIMO radar sensors that collect unwanted and interference signals in a low-rank subspace based on their angle and Doppler frequency spread with different subspace coefficients. We use Generalized Likelihood Ratio Test (GLRT) for moving target detection and find the best detector while demonstrating that it has a constant false alarm rate. The performance of the proposed detector is validated by Monte-Carlo simulation.

In this paper, we look at a waveform design problem for colocated Multiple-Input Multiple-Output (MIMO) radar systems. Under continuous phase constraint, we aim to minimize the range-Integrated Sidelobe Level (ISL) with a compatible spectral response. In this regard, we define the range-ISL function in the time domain first, and then express it in the frequency domain using the Parseval relation. Following that, we incorporate weights on the range-ISL in the frequency domain to apply spectral compatibility. As a result, we have a multi-variable, non-convex, NP-hard optimization problem. We proposed an iterative algorithm based on the Coordinate Descent (CD) method to obtain a local optimum solution. We show the performance of the proposed method and compare it to the counterparts in the numerical results.

Pulse compression can enhance both the performance in range resolution and sensitivity for weather radar. However, it will introduce the issue of high sidelobes if not delicately implemented. Motivated by this fact, we focus on the pulse compression design for weather radar in this paper. Specifically, we jointly design both the subpulse codes and extended mismatch filter based on the alternating direction method of multipliers (ADMM). This joint design will yield a pulse compression with low sidelobes, which equivalently implies a high signal-to-interference-plus-noise ratio (SINR) and a low estimation error on meteorological reflectivity. The experiment results demonstrate the efficacy of the proposed pulse compression strategy since its achieved meteorological reflectivity estimations are highly similar to the ground truth.

In conventional polar format algorithm (PFA), the effective imaging scene is bounded to a limited region near the reference point unless postprocessing is utilized. In a previous paper, refocusing and zoom-in PFA (RZPFA) for curvilinear spotlight SAR imaging were proposed to produce a refocused image for an arbitrary region of interest (ROI) with constant elevation. However, for certain applications, the residual distortion and defocus caused by rugged terrain could not be ignored. In this article, RZPFA is adapted to incorporate the known digital elevation model (DEM) into the imaging process, which is named as orthorectified PFA (OPFA). With just little additional computations than RZPFA, OPFA can realize georeferenced orthorectified imaging via a nonuniform fast Fourier transform of type 3 (NuFFT-3) without the need of postprocessing. The quantitative metrics for the residual DEM distortion and residual DEM defocus were also derived to determine the effective imaging extent of OPFA. Within the effective extent, the proposed OPFA can obtain an orthorectified image efficiently, and the image has a very high quality comparable to backprojection (BP). The imaging results of measured echo and DEM data demonstrated the effectiveness of the proposed algorithm.

In this work, an intelligent and reconfigurable ultra-wideband angular sensing (UWAS) framework is proposed which is independent of the maximum number of active transmissions in a wideband spectrum unlike the existing UWAS methods. To perform the above task, we propose a sub-Nyquist sampling and sparse ruler based multi-antenna array receiver architecture. By characterizing and selecting a set of frequency bands via a learning algorithm, the proposed receiver allows sensing of more active transmissions than the number of antenna over an unlimited bandwidth. The simulation results show that due to the learning based approach, the proposed UWAS outperforms when compared to non-learning based UWAS method.

We investigate waveform design for coexistence between a multiple-input multiple-output (MIMO) radar and MIMO communications (MRMC), with a radar-centric criterion that leads to a minimal interference in the communications system. The communications use the traditional mode of operation in Long Term Evolution (LTE)/Advanced (FDD), where we formulate the design problem based on information-theoretic criterion with the discrete phase constraint at the design stage. The optimization problem, is non-convex, multi-objective and multi-variable, where we propose an efficient algorithm based on the coordinate descent (CD) framework to simultaneously improve radar target detection performance and the communications rate. The numerical results indicate the effectiveness of the proposed algorithm in designing discrete phase set of sequences, potentially binary.

This paper is focused on designing a set of constant modulus waveform for cognitive Multiple-Input Multiple-Output (MIMO) radar systems. The aim is to shape the beam-pattern in transmitter to minimize the Integrated Side-lobe Level (ISL) in spatial domain in a cognitive paradigm. This minimization leads to a NP-hard and non-convex optimization problem. To address this, the problem is formulated based on Coordinate Descent (CD) framework with constant modulus constraint. Subsequently, an low-complexity and fast method based on Discrete Fourier Transform (DFT) is proposed which monotonically decreases the spatial ISL. Finally, we show some numerical results and assess the performance of the proposed technique.

Single waveform design for automotivejoint radar-communications (JRC) is being increasingly considered of late. This paper formulates the JRC design as an optimization problem exploiting the co-location of the two systems and investigates the trade-off between them. We propose an algorithm to maximize the performance of communication and radar receivers (e.g., BER and probability of detection, respectively). This intractable optimization problem is decomposed into two subproblems, which are subsequently solved in succession through a combination of gradient projection method and convex relaxations. The benefits of the proposed waveform are illustrated through numerical simulations.

In this paper, a new framework for designing the radar transmit waveform is established through shaping the radar Ambiguity Function (AF). Specifically, the AF of the phase coded waveforms are analyzed and it is shown that a continuous/discrete phase sequence with the desired AF can be obtained by solving an optimization problem promoting equality between the AF of the transmit sequence and the desired AF. An iterative algorithm based on Coordinate Descent (CD) method is introduced to deal with the resulting non-convex optimization problem. Numerical results illustrate the proposed algorithm make it possible to design sequences with remarkably high tolerance towards Doppler frequency shifts, which is of interest to the future generations of automotive radar sensors.

The paper investigates the applicability of a conventional communications signal for sensing and detection in vehicular scenario. This Joint Radar-Communications (JRC) system caters to an automotive setting characterized by a multi-target environment including clutter. In this contribution, an automotive radar employing phase modulated communication signal is considered wherein the detection of other vehicles is enabled in presence of clutter. Particularly, it is shown when the clutter is dominant in the environment, bit error rate (BER) of communications will suffer. Further, the receiver processing includes demodulation of the communication symbols at communicating vehicle in addition to the extraction of the radar parameters − range and Doppler shifts of the targets − at the JRC-equipped vehicle. The study investigates the impact of various parameters in determining the performance of the JRC set-up.