Browsing by Author "Tharmarasa, R."

Now showing 1 - 4 of 4

Coherent Radar Target Detection With In-Band Cyclostationary Wireless Interference
(Institute of Electrical and Electronics Engineers Inc., 2022) Gunnery, G.; Kumar, H.P.; Srihari, P.; Tharmarasa, R.; Kirubarajan, T.
Spectral congestion necessitates the in-band operation or the spectrum-sharing of legacy radar and communication systems. Since these systems operate in the same band in spectrum-sharing mode, they interfere with one another. To address this problem from the radar's perspective, this paper considers the coherent detection of target-reflected radar signals in the presence of interference from an in-band cyclostationary digital modulated wireless communication signal. Three different cases of target-reflected radar signals, namely, deterministic signals, signals with random phase, and completely random signals, are considered in this paper. The optimum detection rules are derived for these three cases and the corresponding receiver structures for the equalization of the interfering signal are presented. Sub-optimum detection structures are also derived with the assumption that the in-band interference is a white stationary time-invariant Gaussian process. Further, considering the equalization, modified CFAR receiver structures are also presented. By considering the mathematical models for cyclostationary or periodic in-band interference, the performances of the optimum, sub-optimum detectors, and modified CFAR detectors are quantified analytically in terms of detection probability and false alarm probability, and the resulting receiver operating characteristic (ROC) curves are analyzed as a function of the signal-to-interference ratio. It is demonstrated that improper equalization of the interfering signal significantly affects the performance of the optimum detector and this impact is analyzed in detail. As spectrum-sharing becomes more prevalent due to spectrum congestion, the proposed optimal, sub-optimal, and modified CFAR detection rules and receiver structures can be incorporated into existing systems with substantial savings. © 2013 IEEE.
Comprehensive Track Segment Association for Improved Track Continuity
(2018) Raghu, J.; Srihari, P.; Tharmarasa, R.; Kirubarajan, T.
Track breakages are common in target tracking due to highly maneuvering targets, association with false alarms or incorrect target-originated measurements, low detection probability, close target formations, large measurement errors, and long sampling intervals, among other causes. Existing track segment association (TSA) algorithms solve this breakage problem by predicting old track segments and retrodicting young track segments to a common time followed by two-dimensional (2-D) assignment. This approach presents two disadvantages. First, these algorithms predict or retrodict from the actual point of termination or beginning of their respective tracks, that is, they neither check if the cause of a track termination was incorrect association nor redress such an erroneous association. Second, these algorithms do not utilize the measurement information during the breakage period. Often, track terminations are due to incorrect measurement association. To solve the first problem, this paper proposes a 2-D assignment-based TSA algorithm that releases incorrectly associated measurements by going backward and forward in time along old and young track segments, respectively, and then performing prediction and retrodiction. Furthermore, to address both shortcomings in existing TSA algorithms simultaneously, we propose a novel multiframe assignment-based TSA algorithm that estimates the track during the breakage period, utilizing both unassociated and released measurements simultaneously. Moreover, the proposed algorithms can handle target maneuvers subject to a single turn during the breakage period. In the proposed solution, model parameters, such as starting time of the turn, ending time of the turn, and turn rate are obtained by maximizing the likelihood that a given measurement-tuple originated from the track couple under consideration. Simulation results demonstrate that the proposed TSA algorithm is superior to existing ones in terms of association accuracy and computational cost. 1965-2011 IEEE.
Comprehensive Track Segment Association for Improved Track Continuity
(Institute of Electrical and Electronics Engineers Inc., 2018) Raghu, J.; Srihari, P.; Tharmarasa, R.; Kirubarajan, T.
Track breakages are common in target tracking due to highly maneuvering targets, association with false alarms or incorrect target-originated measurements, low detection probability, close target formations, large measurement errors, and long sampling intervals, among other causes. Existing track segment association (TSA) algorithms solve this breakage problem by predicting old track segments and retrodicting young track segments to a common time followed by two-dimensional (2-D) assignment. This approach presents two disadvantages. First, these algorithms predict or retrodict from the actual point of termination or beginning of their respective tracks, that is, they neither check if the cause of a track termination was incorrect association nor redress such an erroneous association. Second, these algorithms do not utilize the measurement information during the breakage period. Often, track terminations are due to incorrect measurement association. To solve the first problem, this paper proposes a 2-D assignment-based TSA algorithm that releases incorrectly associated measurements by going backward and forward in time along old and young track segments, respectively, and then performing prediction and retrodiction. Furthermore, to address both shortcomings in existing TSA algorithms simultaneously, we propose a novel multiframe assignment-based TSA algorithm that estimates the track during the breakage period, utilizing both unassociated and released measurements simultaneously. Moreover, the proposed algorithms can handle target maneuvers subject to a single turn during the breakage period. In the proposed solution, model parameters, such as starting time of the turn, ending time of the turn, and turn rate are obtained by maximizing the likelihood that a given measurement-tuple originated from the track couple under consideration. Simulation results demonstrate that the proposed TSA algorithm is superior to existing ones in terms of association accuracy and computational cost. © 1965-2011 IEEE.
Comprehensive Track Unswappinng for Improved Tracker Performance
(Institute of Electrical and Electronics Engineers Inc., 2025) Raghu, J.; Lingadevaru, T.L.; Pardhasaradhi, B.; Srihari, P.; Tharmarasa, R.; Kirubarajan, T.
In practical surveillance systems, multiple-target tracking can suffer from undesirable effects such as track breakages and track swaps. Track stitching or track segment association (TSA) algorithms have been proposed in the literature to stitch broken tracks deemed to have originated from the same target across time and to improve track continuity. Measurements from multiple neighboring targets may fall within the validation gates of one another, causing association errors that may eventually lead to not just track breaks but also track swaps. Therefore, TSA alone is insufficient to improve the overall tracker performance, as it considers only the broken tracks but not the continuous ones that might have swaps among themselves or with other broken tracks. To mitigate the effects of track swaps, this article proposes an algorithm that detects and resolves possible track swaps using kinematic and nonkinematic—classification and amplitude—information. Track swap detection involves identifying the most likely instant of track swap occurrence. Further, the proposed algorithm is extended to stitch broken track segments (as in the standard TSA) and those tracks that are algorithmically broken due to the detection of possible swaps. Simulation results demonstrate the effectiveness of the proposed algorithm in resolving track swaps and thereby improving track purity and the overall tracker performance. © 1965-2011 IEEE.