Browsing by Author "Sreenivasula Reddy, T.S."

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    FPGA Implementation of Moving Target Indicator Filter for FMCW Radar Data
    (Institute of Electrical and Electronics Engineers Inc., 2023) Sreelekha, N.; Vandana, G.S.; Srihari, P.; Leelarani, V.; Raju, M.K.; Sreenivasula Reddy, T.S.
    This study examines several digital finite impulse response (FIR) filter approaches for moving target indication (MTI) employing short-range FMCW radar sensors. The FIR filters can filter out low doppler shift responses from undesirable stationary targets. A 77 GHz AWR1642 FMCW radar sensor and a DCA1000 data capture card are used to build a hardware configuration. A single data frame (samples × chirps) containing a target approaching the radar is been considered. The recorded radar is preserved in a 256x64 matrix of in-phase and quadrature-phase components, which is then processed using various digital filters. The radar provides insights into doppler characteristics for the observations. This study proposes designing and implementing a two-tap and a three-tap FIR filter-based MTI processing module to reduce static targets. The VLSI DSP pipelining approach is deployed to improve filter performance regarding critical path delay and throughput. © 2023 IEEE.
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    Improved Target Tracking and Fusion Using Optimally Quantized Measurement Channels
    (Institute of Electrical and Electronics Engineers Inc., 2023) Balarami Reddy, B.N.; Reddy, G.H.; Sreenivasula Reddy, T.S.; Srihari, P.; Pardhasaradhi, B.
    Nowadays, autonomous underwater vehicle (AUV) technologies provide localization of AUV s, high-precision 3D measurement mapping, and underwater target tracking. Usually, the AUV consists of various sensors to acquire the dense measurements of the underwater scene to perform target tracking and functionalities. In in-water mobility, the bandwidth is a significant bottleneck, allowing communication with other AUV s and performing centralized target tracking and fusion. Since the communication modules within the AUV are compact, low power, and have low bandwidth, the quantized measurements are transmitted to the fusion center (FC). The sensing devices provide different measurements like range, range rate, azimuth, elevation, and directional cosines corresponding to the scene. Whereas the range measurements are in meters, azimuth measurements range from 0 to 360°• Hence, using a single quantizer with a predefined step size leads to tremendous errors. This paper proposes to deploy an optimal quantizer for every measurement channel and then transmit it to the FC. To explicitly study the quantization effect, we have used linear and optimal quantization techniques which can adaptively choose the levels of the measurements. The extended Kalman filter (EKF) in combination with correlation-free covariance intersection (CI) fusion algorithm is used to attain the global track information. The performance of the proposed method is quantified using the position root mean square error (PRMSE) and compared with the no-quantization state-of-art. © 2023 IEEE.