Unmanned aerial vehicles (UAVs) in wireless communication systems offer rapid deployment, flexible reconfiguration, and superior communication channels, thanks to their short-range line-of-sight links, making them more efficient and cost-effective than terrestrial or high-altitude platform networks. As a promising technique, integrated sensing and communication (ISAC) enhances UAV networks by integrating sensing and communication functionalities. This concurrent design improves spectrum efficiency and reduces hardware costs. To ensure reliable communication for their high mobility, a novel modulation technique, the orthogonal time-frequency space (OTFS) waveform, has been proposed, which leverages the delay-Doppler domain for efficient information transmission. In this paper, we investigate ISAC-based multi-target sensing with distributed OTFS radars to deliver reliable performance for UAV networks. To achieve that, we propose to leverage the delay and Doppler information featured by OTFS signals to determine the range and radial velocity, accomplishing successful sensing tasks. Moreover, to address the challenge of unassociated measurements and targets, we propose a novel optimization framework to concurrently perform data association and target sensing tasks. This framework is developed by formulating a mixed-integer optimization problem, which is then solved with polynomial complexity through convex approximation. Additionally, we propose an iterative maximum likelihood estimator (MLE) to further enhance sensing performance by accounting for target-measurement errors. Extensive simulation results verify the superiority of our proposed work to state-of-the-art methods.

This paper investigates the system design and experimental validation of integrated sensing and communication (ISAC) for environmental sensing, which is expected to be a critical enabler for next-generation wireless networks. We advocate exploiting orthogonal time frequency space (OTFS) modulation for its inherent sparsity and stability in delay- Doppler (DD) domain channels, facilitating a low-overhead environment sensing design. Moreover, a comprehensive environmental sensing framework is developed, encompassing DD domain channel estimation, target localization, and experimental validation. In particular, we first explore the OTFS channel estimation in the presence of fractional delay and Doppler shifts. Given the estimated parameters, we propose a three-ellipse positioning algorithm to localize the target's position, followed by determining the mobile transmitter's velocity. Additionally, to evaluate the performance of our proposed design, we conduct extensive simulations and experiments using a software-defined radio (SDR)-based platform with universal software radio peripheral (USRP). The experimental validations demonstrate that our proposed approach outperforms the benchmarks in terms of localization accuracy and velocity estimation, confirming its effectiveness in practical environmental sensing applications.

Existing communication signal-based sensing systems mainly rely on the orthogonal frequency division multiplexing (OFDM) technique due to its remarkable communication performance. However, extracting Doppler shifts from the received signal is not straightforward for OFDM and usually requires additional operations. The recently emerging orthogonal time frequency space (OTFS) modulation, which employs the Delay-Doppler (DD) domain for data transmission, can reveal the physical wireless propagation environments and provide the DD information directly. This paper investigates the moving multi-target sensing problem based on OTFS signaling. In particular, we attempt to tackle sensing and data association tasks concurrently by using the time delay (TD) and Doppler information from OTFS channel estimation. To this end, we formulate a mixed-integer optimization problem and approximate it as a convex problem. Simulation results has demonstrated the effectiveness of the proposed method.

The Orthogonal Time Frequency Space (OTFS) waveform, utilizing the Delay-Doppler (DD) domain for information transmission, has the capability to reveal intricate wireless propagation environments. Specifically, the Doppler component contains valuable geometric information that can be harnessed to enhance target localization accuracy. Surprisingly, existing communication radar-based target localization studies often ne-glect Doppler information. This paper delves into the problem of elliptic localization for moving objects in distributed OTFS radars. We explore the significance of Doppler by deriving the Cramer-Rao Lower Bound (CRLB) and subsequently formulate a non-convex Weighted Least Squares (WLS) problem. This problem is approximated as a convex semidefinite program through the application of Semidefinite Relaxation (SDR). The efficacy of the proposed algorithm is demonstrated through theoretical analysis and simulations.

In this article, we study the uplink transmission scheme and channel estimation design for reconfigurable intelligent surfaces (RIS)-aided orthogonal time–frequency space (OTFS) systems in high-mobility scenarios. To this end, we first propose an efficient and reliable transmission scheme that utilizes the delay-Doppler (DD) information in OTFS to facilitate the configuration of RIS. Specifically, the proposed scheme exploits the estimated delay and Doppler shifts of the cascaded channel to sense the channel parameters, and the sensing parameters are then used for RIS passive beamforming. It is noteworthy that we estimate the channel state information (CSI) by employing only one OTFS frame and configure the RIS based on the predicted channel parameters, leading to substantially reduced channel training overhead and more real-time RIS configuration. To obtain the essential information for channel information sensing, we then propose a low-complexity algorithm which determines the Doppler and delay shifts of the channel between the user and RIS based on linear systems and the mapping relationship of the DD pairs, respectively. With the DD information in hand, a user localization algorithm constructed by the least square (LS) and a channel tracking method relying on extended Kalman filter (EKF) are then presented to obtain the spatial angle information. By making use of the channel parameters acquired at the base station (BS), the RIS reflection vector is designed to maximize the achievable rate. The results obtained from the simulation experiments affirm the efficacy of the proposed scheme, thereby confirming its capability to attain efficient communications under high Doppler channels.