Browsing by Author "Kumuda, D.K."

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A Mutual Interference Mitigation Algorithm for Dense On-Road Automotive Radars Scenario
(Institute of Electrical and Electronics Engineers Inc., 2023) Kumuda, D.K.; Srihari, P.; Seshagiri, D.; Raju, M.K.; Pardhasaradhi, B.
The mmWave frequency modulated continuous wave (FMCW) radars are widely adopted in the automotive industry because they work in all weather conditions. Due to the increased on-road density of mmWave radars, the primary radar mounted on the ego vehicle faces mutual interference. The traditional detection scheme employs a one-dimension fast Fourier transform (FFT) followed by a constant false alarm rate (CFAR) on the intermediate frequency (IF) signal to get the target detections. In the case of mutual interference, the IF signals behavior is abnormal and leads to miss-detection and false detections within the traditional framework. We propose a weighted beat signal normalization algorithm on the intermediate frequency (IF) signal followed by a traditional detection scheme as a mutual interference mitigation mechanism. This methodology implementation is easy since it does not disturb any processing modules like the mixer, LPF, FFT, and CFAR blocks in the architecture. The results demonstrate that, the SINR increases by the proposed method thereby minimizing the probability of missing the target detection. Â© 2023 IEEE.
Clipping and Hampel Filtering Algorithm to Mitigate Mutual Interference for Automotive Radars
(Institute of Electrical and Electronics Engineers Inc., 2023) Kumuda, D.K.; Srihari, P.; Seshagiri, D.; Rajesh Kumar, P.R.; Pardhasaradhi, B.
The frequency modulated continuous wave (FMCW) radars are widely adopted in the automotive industry to serve the emergency brake assistant (EBA) and automatic cruise control (ACC) functions. Nowadays, autonomous vehicles on-road density increasing and creating a problem of mutual interference. Due to this mutual interference, the structural properties like periodicity and amplitude of intermediate frequency (IF) signal varies and creates undeserved target detections. It is observed that, applying a one-dimension fast Fourier transform (FFT) on the intermediate frequency (IF) results in increased false alarms and missed detections. This paper process a clipping followed by a Hampel filtering on the IF signal to mitigate this mutual interference. Initially, the clipping framework chopoff the unwanted and abrupt amplitude information from the IF signal. The threshold of the clipper circuit is taken as the standard deviation of the acquired IF signal. Second, the Hampel filter was employed to identify the outliers in time-series data and replace them with more representative values. The Hampel is configured in a sliding window to calculate the median by providing the standard deviation of the acquired IF signal. This methodology implementation is easy and can be placed as an intermediate block between IF and FFT. The results demonstrate that the proposed methodology facilitating the good detection rate by decreasing the false alarm rate and missed detections. Â© 2023 IEEE.
Multiple Target Detection using Î£Î”-STAP in the Presence of Airborne Clutter
(Institute of Electrical and Electronics Engineers Inc., 2021) Kumuda, D.K.; K Shetty, A.; Srihari, P.; Seshagiri, D.; Mahajan, V.; Joseph, P.
Spatio-Temporal Adaptive Processing (STAP) technique has been widely applied to mitigate the effects of strong interference (joint effects of clutter and other electronic counter measures(ECM)). Limited channel and reduced rank STAP techniques are alternatively proposed in the literature to address the computational issues present in fully adaptive STAP. This paper investigates performance of two channel Î£Î” (SigmaDelta)-STAP for detecting multiple targets in Ground Moving Target Indication (GMTI) scenario in the presence of clutter. Clutter is simulated from an airborne platform operating at 10 GHz and a pulse repetition frequency of 5 kHz. First, a single ground moving target is detected effectively using the Î£ Î”-STAP. Further, this algorithm is applied to detect two targets moving with 7.5 m/s and 15 m/s velocities. In addition, five target scenario is considered and the Î£ Î”-STAP algorithm successfully detected these five targets, Â© 2021 IEEE.
Multitarget Detection and Tracking by Mitigating Spot Jammer Attack in 77-GHz mm-Wave Radars: An Experimental Evaluation
(Institute of Electrical and Electronics Engineers Inc., 2023) Kumuda, D.K.; Vandana, G.S.; Pardhasaradhi, B.; Raghavendra, B.S.; Srihari, P.; Cenkarmaddi, L.R.
Small form factor radar sensors at millimeter wavelengths find numerous applications in the industrial and automotive sectors. These radar sensors provide improved range resolution, good angular resolution, and enhanced Doppler resolution for short range and ultrashort ranges. However, it is challenging to detect and track the targets accurately when a radar is interfered by another radar. This article proposes an experimental evaluation of a 77-GHz IWR1642 radar sensor in the presence of a second 77-GHz AWR1642 radar sensor acting as a spot jammer. A real-time experiment is carried out by considering five different targets of various cross sections, such as a car, a larger size motorcycle, a smaller size motorcycle, a cyclist, and a pedestrian. The collected real-time data are processed by four different constant false alarm rate detectors, cell averaging (CA)-CFAR, ordered statistics (OS)-CFAR, greatest of CA (GOCA)-CFAR, and smallest of CA (SOCA)-CFAR. Following that, data from these detectors are fed into two different clustering algorithms (density-based spatial clustering of applications with noise (DBSCAN) and K-means), followed by the extended Kalman filter (EKF)-based tracker with global nearest neighbor (GNN) data association, which provide tracks of various targets with and without the presence of a jammer. Furthermore, four different metrics [tracks reported (TR), track segments (TSs), false tracks (FTs), and track loss (TL)] are used to evaluate the performance of various tracks generated for two clustering algorithms with four detection schemes. The experimental results show that the DBSCAN clustering algorithm outperforms the K-means clustering algorithm for many cases. © 2001-2012 IEEE.