Browsing by Author "Reddy, G.H."

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A Modified Strassen Algorithm based DSP Accelerated 3D Kalman Filter
(Institute of Electrical and Electronics Engineers Inc., 2023) Mohalia, V.; Srihari, P.; Reddy, S.; Reddy, G.H.; Pardhasaradhi, B.
The high-speed Kalman filter (KF) algorithms are essential for robotics, autonomous vehicle, target tracking, and other applications. The dimensions of the state vector and traditional matrix multiplication (complexity of order O(n3)) are the two main reasons for the computational time of the K F algorithm. Hence, a matrix multiplication accelerator module is needed to accelerate the KF algorithm for higher dimensions of the state vector. In this paper, modified Strassen matrix multiplication (complexity of order O(n2.80)) is utilized to increase the computational efficiency of the KF algorithm. The number of cycles is evaluated against the dimensions of the KF algorithm to illustrate the proposed methodology. After that, 2D-KF and 3D-KF algorithms targeted on DSP processor TMS320C6678 using C language to ensure real-time processing. The 3 D-K F with a state of nine consumes 19.962 ~ms, 30.47 ~ms, and 40.04 ~ms of time by employing the hybrid Strassen, Strassen, and conventional matrix multiplication. It is observed that the usage of hybrid Strassen takes only half of the time provided by conventional multiplications. Â© 2023 IEEE.
FPGA Accelerated Track to Track Association and Fusion for ADAS Distributed Sensors
(Institute of Electrical and Electronics Engineers Inc., 2023) Gopala Swamy, B.; Reddy, G.H.; Srihari, P.; Shripathi Acharya, U.; Pardhasaradhi, B.
The integration and amalgamation of sensor data in the automotive domain play a pivotal role in informing real-time decision-making for advanced driver assistance and safety (ADAS) systems. In a distributed architecture, the track-to-track association (T2TA) modules are responsible for associating the correct track pairs and subsequently fusion modules fuses the information. The T2TA and fusion modules operate within the CPU framework, often leading to elevated latency across the system. This paper introduces digital signal processing (DSP) architectures for the T2TA and fusion modules, designed to meet stringent constraints in terms of both area and latency. These modules encompass critical operations such as matrix inversion, vector-to-matrix multiplications, and matrix-to-matrix multiplications. The challenge of vector-to-matrix multiplications is effectively addressed through the utilization of the constant co-efficient multiplication technique. Additionally, matrix-to-matrix multiplication is performed by employing a vector-to-vector multiplication architecture with Block RAMs (BRAMs). Further-more, matrix inversion is realized through the LU decomposition method. Moreover, this paper presents an innovative approach to expedite the T2TA and fusion modules by harnessing folded DSP architecture within a system-on-chip (SOC) framework. The results of simulations substantiate that the proposed architectures exhibit a remarkable suitability for applications necessitating low area, low power consumption, and high throughput capabilities. Â© 2023 IEEE.
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 360Â°â€¢ 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. Â© 2023 IEEE.