The article titled “Performance Analysis of Spectrum Sharing Radar in Multipath Environment” has been accepted for publication in the IEEE Open Journal of the Communications Society (2023).

Gunnery Srinath, Bethi Pardhasaradhi, Ashoka Chakravarthi Mahipathi,
Prashantha Kumar H, Pathipati Srihari, and Linga Reddy Cenkeramaddi, “Performance Analysis of Spectrum Sharing Radar in Multipath Environment,” has been accepted for publication in the IEEE Open Journal of the Communications Society (2023).

Keywords: Radar, Communication systems, Measurement, Wireless sensor networks, Wireless communication, Receivers, Interference cancellation

Abstract: Radar based sensing and communication systems sharing a common spectrum have become a potential research problem in recent years due to spectrum scarcity. The spectrum sharing radar (SSR) is a new technology that uses the total available bandwidth (BW) for both radar based sensing and communication. Unlike traditional radar, the SSR divides the total available BW into radar-only and mixed-use bands. In a radar-only band, only radar sensor signals can be transmitted and received. In contrast, radar and communication signals can both be transmitted and received in the mixed-use band. Taking such BW sharing into account, this paper investigates the performance of SSR in an information-theoretic sense. To evaluate performance, mutual information (MI), spectral efficiency (SE) and capacity (C) metrics are used. Initially, this paper considered a clean environment (no multipath) in order to evaluate performance metrics in the mixed-use band with and without successive interference cancellation. Following that, this paper addresses the performance of BW allocation by allocating low to high BW in mixed-band. Furthermore, the performance metrics are extended to account for the multipath environment, and the same analogy as in a clean environment is used. In addition, the MI and SE of traditional radar system is taken into account when comparing the performance of SSR with and without the use of the SIC. Finally, MI and capacity results show that using the SIC scheme in a mixed-use band yields performance comparable to traditional radar and communication system. In terms of SE, the SSR with SIC scheme outperforms traditional radar and communication system.

More details: DOI: 10.1109/OJCOMS.2023.3240116

The article titled, “Machine Learning based Screening and Measurement to Measurement Association for Navigation in GNSS Spoofing Environment,” has been accepted for publication in the IEEE Sensors Journal (2022). 

B. Pardhasaradhi, R. R. Yakkati, and L. R. Cenkeramaddi, “Machine Learning based Screening and Measurement to Measurement Association for Navigation in GNSS Spoofing Environment,” has been accepted for publication in the IEEE Sensors Journal (2022).

Keywords: Global navigation satellite system, Interference, Receivers, Distortion measurement, Jamming, Distortion, Sensors

Abstract: Global navigation satellite system (GNSS) provides reliable positioning across the globe. However, GNSS is vulnerable to deliberate interference problems like spoofing, which can cause fake navigation. This article proposes navigation in a GNSS spoofing environment by taking the received power, correlation distortion function, and pseudorange measurement observation space into account. In the proposed approach, both actual and interference measurements are considered a set. Machine learning screens the authentic measurements from the accessible set using parameters such as received power and correlation function distortion. To maintain the track and navigate the GNSS’s time-varying kinematics, we used a combination of the gating technique within the Kalman filter framework and logic-based track management. The machine learning classifiers like support vector machines (SVMs), neural networks (NNs), ensemble, nearest neighbor, and decision trees are explored, and we observe that linear SVM and NN provide a test accuracy of 98.20%. A time-varying position-pull off strategy is considered, and the metrics like position RMSE and track failure are compared with the conventional M-best algorithm. The results show that for four authentic measurements and spoof injections, there are only a few track failures. In contrast, even with an increase in spoof injections, track failures are zero in the case of six authentic measurements.

More details:DOI: 10.1109/JSEN.2022.3214349