Beyond RSS A PRR and SNR Aided Localization System for Transceiver-free Target in Sparse Wireless Networks
ABSTARCT :
Nowadays transceiver-free (also referred to as device-free) localization using Received Signal Strength (RSS) is a hot topic for researchers due to its widespread applicability. However, RSS is easily affected by the indoor environment, resulting in a dense deployment of reference nodes. Some hybrid systems have already been proposed to help RSS localization, but most of them require additional hardware support. In order to solve this problem, in this paper, we propose two algorithms, which leverage the Packet Received Rate (PRR) to help RSS localization without additional hardware support. Moreover, we take the environment noise information into consideration by utilizing the Signal-to-Noise Ratio (SNR) which is based on the RSS and Noise Floor (NF) information instead of pure RSS. Thus, we can alleviate the noise effect in the environment and make our system more sensitive to the target. Specifically, when reference nodes are sparsely deployed and RSS is very weak, PRR and SNR can help in performing localization more accurately. Our BEYOND RSS system is based on sparse wireless sensor networks, wherein the experimental results show that the average localization error of our approach outperforms the pure RSS based approach by about 15.19%.
EXISTING SYSTEM :
? To the best of our knowledge, most of the existing research is focused on the second type, i.e., on the design of receivers for communication systems.
? Moreover, the addition of such subsystems will inevitably cause electromagnetic compatibility issues, which may impose serious mutual interference on the existing subsystems.
? Therefore, there is a growing demand to utilize existing communication systems, such as cellular BSs, to monitor unauthorized UAVs while offering wireless services to authorized UEs, which needs no substantial extra hardware and thus reduces the cost .
? We present existing, or potential application scenarios of CRSS from both civilian and military perspectives.
DISADVANTAGE :
? However, several problems need to be investigated in that context, such as specific mmWave channel models and constraints.
? The corresponding optimization problem is solved via Lagrangian dual decomposition and alternating minimization methods.
? The authors then formulate an optimization problem to maximize the rate subject to power and radar SINR constraints. It is worth noting that this problem can be solved in closed-form when the radar interference satisfies certain conditions.
? To address the coexistence problem of the MIMO radar and the multi-user MIMO (MU-MIMO) communication system, the authors of have proposed a robust beamforming design at the MIMO BS when the ICSI between the radar and the communication system is imperfectly known.
PROPOSED SYSTEM :
• The feasibility and the efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, our discussions are summarized by overviewing the open problems in the research field of CRSS.
• UAVs have been proposed as aerial base stations to a range of data-demanding scenarios such as concerts, football games, disasters and emergency scenarios.
• A novel channel estimation approach has been proposed in by exploiting the radar probing waveform as the pilot signal, where the radar is oblivious to the operation of the communication system.
• In , an integrated metric is proposed for the DFRC receiver, which is the weighted sum of the estimation and communication rates.
ADVANTAGE :
? We consider the performance of the bias-corrected MLE in the case when measurements are RSS.
? Comparing proximity and RSS bounds allows us to determine the performance loss associated with proximity as compared to the performance of RSS.
? However, if we consider particular location estimators, we can determine their performance via simulation, and then compare the results to the CRB.
? In order to deploy localization systems based on proximity or QRSS, future work must be done to explore the performance of particular location estimators which use quantized RSS or proximity measurements.
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