Adaptive Energy Efficiency Based Power Allocation for MIMO Radar Network
T. Harikala1, R.V.S. Satyanarayana2

1T Harikala*, Electronics and Communication, S.V. University, Tirupati Name, India.
2R.V.S. Satyanarayana, Electronics and Communication, S.V. University, Tirupati, India

Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 1720-1727 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5189119119/2019©BEIESP | DOI: 10.35940/ijitee.A5189.119119
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Abstract: Multiple-input multiple-output (MIMO) radar is used extensively due to its application of simultaneous transmission and reception of multiple signals through multiple antennas or channels. MIMO radar receives enormous attention in communication technologies due to its better target detection, higher resolution and improved accurate target parameter estimation. The MIMO radar has several antennas for transmitting the information and also the reflected signals from the target is received by the multiple antennas and it mainly used in military and civilian fields. But sometimes the performance of the MIMO radars is degraded due to its limited power. So the optimum power allocation is required in the communication systems of MIMO radar to improve its performance. In this paper, an Energy Efficiency based Power Allocation (EEPA) is used to allocate the power to a user of the clusters and also across the clusters. Here, the MIMO radars are clustered by using a naive bayes classifier. Subsequently, an efficient target detection is achieved by using Generalized Likelihood Ratio Test (GLRT) and then the clusters are divided into primary and distributive clusters based on the distance from the target. Here, the proposed methodology is named as EEPA-GLRT and the implementation of this MIMO radar system with an effective power allocation is done by Labview. The performance of the EEPA-GLRT methodology is analyzed in terms of the power consumption of various clusters. The performance of the EEPA-GLRT methodology is compared with Generalized Nash Game (GNG) method and it shows the power consumption of EEPA-GLRT is 0.0549 for cluster 1 of scenario 1, which is less when compared to the GNG method.
Keywords: Multiple-Input Multiple-Output Radar, Generalized Likelihood Ratio test, Naive Bayes Classifier, Power Allocation and Power Consumption.
Scope of the Article: Classification