Resource Allocation Schemes for Multiple Targets Tracking in Distributed MIMO Radar Systems

Song, Xiyu; Zheng, Nanning; Bai, Ting

doi:10.1155/2017/7241281

Cited by 13 publications

(11 citation statements)

References 19 publications

(27 reference statements)

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“…6 indicates that the maximum number of detectable targets on the particular PMIMO radar with M = 4, N = 8, is 8. This is in agreement with the result from (33) for M = 4, for which the number of P max,PMIMO is in the range of [5,16].…”

Section: C: Phased Mimo Radarsupporting

confidence: 93%

FPMIMO: A General MIMO Structure With Overlapping Subarrays for Various Radar Applications

Tahcfulloh

Hendrantoro

2020

IEEE Access

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Unlike the phased array (PA) radar that achieves array gain in proportion to the number of elements, the MIMO radar provides improvement to parameter estimation, target detection, and so on. Recently, the combination of the PA and MIMO concepts into the Phased MIMO (PMIMO) radar was proposed with overlapping non-coherent subarrays at the transmitter and an array with non-coherent elements at the receiver that proved to outperform the more conventional radars. This paper examines the parameter identifiability, i.e., the maximum number of detectable targets uniquely identified and the detection performance, i.e., the probabilities of detection and false alarm, of the Full PMIMO radar (FPMIMO), which is a further generalization of combination of the PA and MIMO concepts that uses overlapping subarrays on both transmit and receive arrays. The maximum number of resolvable targets on the FPMIMO radar is derived by the least squares (LS) method and its performance is subsequently evaluated numerically by considering the magnitude of target reflection coefficient as function of direction angle for comparison against PA, MIMO and PMIMO radars. Similarly, the derivation of expressions for probabilities of detection and false alarm is given. In particular, the numerical evaluation is made on the maximum number of resolvable targets with respect to the impact of the number of subarrays, the total number of elements and the element spacing, while the detection performance is evaluated based on the effect of threshold, SNR, and the number of subarrays. The results demonstrate that, being the more general form of multi-antenna radars, the FPMIMO radar is capable of flexibly trading off the maximum number and the lowest reflection coefficient of identifiable targets, with the PA, MIMO and PMIMO radars being special cases. The desired compromise can be obtained by adjusting the number of subarrays on the transmit and the receive arrays. An example of vehicular radar design that satisfies some mission requirements using the FPMIMO radar concept is also presented. INDEX TERMS MIMO radar, parameter estimation, phased array radar, probability of detection, target detection.

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Section: C: Phased Mimo Radarsupporting

confidence: 93%

FPMIMO: A General MIMO Structure With Overlapping Subarrays for Various Radar Applications

Tahcfulloh

Hendrantoro

2020

IEEE Access

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“…The normalized processing time of the measurement matrix design is higher than the energy allocation method. Song, X., Zheng, N. and Bai, T [17] introduced a distributed MIMO radar system with antenna selection schemes for improving the performance of resource allocation. These antenna selection schemes are introduced for tracking the multiple targets with various location estimation MSE and priorities of the targets.…”

Section: Literature Workmentioning

confidence: 99%

Adaptive Energy Efficiency Based Power Allocation for MIMO Radar Network

Harikala*,

Satyanarayana

2019

IJITEE

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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.

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“…Therefore, Bayesian Cramer Rao Lower Bound is extracted, normalized, and utilizes as the optimization criterion. To increase the system performance and resource utilization of widely separated MIMO radar, a joint resource allocation for velocity estimation problem in multi-target tracking is proposed in [23]. The authors chose one target as a key target and then examined their strategy by this target.…”

Section: -Introductionmentioning

confidence: 99%

“…For instance, in [12], range-only estimation for target tracking in MIMO radar with widely separated antennas is performed and it is not considered velocity. Whereas, in [23], velocity estimation is utilized to compute target tracking for power allocation problem in distributed MIMO radar. These are just some papers which we investigated and we concluded that joint estimation for target tracking in power allocation problem for MIMO radar with widely separated antennas is not used yet.…”

Section: -Introductionmentioning

confidence: 99%

SQP-based Power Allocation Strategy for Target Tracking in MIMO Radar Network with Widely Separated Antennas

Darzikolaei¹,

Karami²,

Najimi³

2021

jist

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MIMO radar with widely separated antennas enhances detection and estimation resolution by utilizing the diversity of the propagation path. Each antenna of this type of radar can steer its beam independently towards any direction as an independent transmitter. However, the joint processing of signals for transmission and reception differs this radar from the multistatic radar. There are many resource optimization problems which improve the performance of MIMO radar. But power allocation is one of the most interesting resource optimization problems. The power allocation finds an optimum strategy to assign power to transmit antennas with the aim of minimizing the target tracking errors under specified transmit power constraints. In this study, the performance of power allocation for target tracking in MIMO radar with widely separated antennas is investigated. Therefore, a MIMO radar with distributed antennas is configured and a target motion model using the constant velocity (CV) method is modeled. Then Joint Cramer Rao bound (CRB) for target parameters (joint target position and velocity) estimation error is calculated. This is utilized as a power allocation problem objective function. Since the proposed power allocation problem is nonconvex. Therefore, a SQP-based power allocation algorithm is proposed to solve it. In simulation results, the performance of the proposed algorithm in various conditions such as a different number of antennas and antenna geometry configurations is examined. Results affirm the accuracy of the proposed algorithm.

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Resource Allocation Schemes for Multiple Targets Tracking in Distributed MIMO Radar Systems

Cited by 13 publications

References 19 publications

FPMIMO: A General MIMO Structure With Overlapping Subarrays for Various Radar Applications

FPMIMO: A General MIMO Structure With Overlapping Subarrays for Various Radar Applications

Adaptive Energy Efficiency Based Power Allocation for MIMO Radar Network

SQP-based Power Allocation Strategy for Target Tracking in MIMO Radar Network with Widely Separated Antennas

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