IEEE - Aerospace and Electronic Systems - November 2022 - 20

Detection, Mode Selection, and Parameter Estimation in Distributed Radar Networks
interference. However, further investigation is required to
assess the tradeoff between performance and complexity
of such approach in distributed radar networks
DISTRIBUTED RESOURCE ALLOCATIONS
Optimizing allocations of resources, such as bandwidth,
power, antennas etc., is an important task in distributed
radar networks. Bandwidth allocation for radar nodes is
important for minimizing internode interference and
enhancing radar operations. Distributed learning algorithms,
such as musical chairs, multiarm bandits, and reinforcement
learning, can be employed for solving
bandwidth allocation problems [43], [44], [45]. However,
it is not well known how reward functions can be modeled
in practical scenarios, wherein each node has limited or no
information of other nodes, including other RF sources.
Distributed beamforming and radar waveform optimization
help the network to optimally utilize the available
power. However, these problems are often nonconvex,
especially due to nonconvex unit modulus constraints on
radar waveforms. Although manifold optimization problems
have been shown to provide acceptable performance
with reduced complexity [64], [65], it is not known how
the approach can be extended for distributed implementation.
All of these resource allocation problems are important
for enhancing resource utilization in radar networks,
but new technologies are needed for achieving performance
close to that of the centralized method, with a
reduced complexity.
CONCLUSION
In this article we demonstrated, by analysis and simulations,
that target detection and estimation of range, Doppler, and
angle parameters can be significantly improved with a distributed
network of radars compared to the single-node
radar system. More specifically, we showed that nodes that
observe low signal-to-noise ratio (SNR) values can considerably
improve their detection and estimation performance
by cooperating with other nodes in the network, which
observe high SNR values. It is worth noting that this performance
improvement is achieved by sharing limited information
among neighboring nodes only. We proposed a
mode selection algorithm that assigns poor performing
nodes to passive mode of operation so that their resources
can be allocated to nodes operating in active mode. A deep
learning approach with long-short term memory (LSTM)
networks was proposed for mapping neighboring nodes'
observations to those at each node. We proposed possible
future research directions in the field of distributed radar
systems. These directions include joint data association and
parameter estimation for achieving efficient target tracking,
distributed detection and estimation in cluttered radar
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environment, distributed designs that are robust against
channel impairments, distributed resource allocations, and
synchronization approaches.
REFERENCES
[1] M. A. Richards, J. A. Scheer, and W. A. Holm, Principles
of Modern Radar, Vol 1: Basic Principles.Raleigh,NC,
USA: Scitech Publishing, Inc., 2010.
[2] D. E. Hack, L. K. Patton, B. Himed, and M. A. Saville,
" Centralized passive MIMO radar detection without
direct-path references, " IEEE Trans. Signal Process.,
vol. 62, no. 11, pp. 3013-3023, Jun. 2014.
[3] C. J. Baker and A. L. Hume, " Netted radar sensing, " IEEE
Trans. Aerosp. Electron. Syst. Mag., vol. 18, no. 2,
pp. 3-6, Feb. 2003.
[4] B. K. Chalise and B. Himed, " GLRT detector in single frequency
multi-static passive radar systems, " Signal Process.,
vol. 142, pp. 504-512, Jan. 2018.
[5] S. Pak, B. K. Chalise, and B. Himed, " Target localization in
multistatic passive radar systems with artificial neural
networks, " in Proc. Int. Radar Conf., Toulon, France, 2019,
pp. 1-5.
[6] R. Viswanathan and P. K. Varshney, " Distributed detection
with multiple sensors: Part I-Fundamentals, " Proc.
IEEE, vol. 85, no. 1, pp. 54-63, Jan. 1997.
[7] A. R. Reibman and L. W. Nolte, " Design and performance
comparison of distributed detection networks, " IEEE
Trans. Aerosp. Electron. Syst., vol. 23, no. 6, pp. 789-797,
Nov. 1987.
[8] S. C. A. Thomopoulous, R. Vishwanathan, and D. C. Bougoulias,
" Optimal decision fusion in multiple sensor systems, "
IEEE Trans. Aerosp. Electron. Syst., vol. 23, no. 5,
pp. 644-653, Sep. 1987.
[9] D. Ciuonzo, A. Aubry, and V. Carotenuto, " Rician MIMO
channel-and jamming-aware decision fusion, " IEEE Trans.
Signal Process. vol. 65, no. 15, pp. 3866-3880, Aug. 2017.
[10] L. Xiao, S. Boyd, and S. Kim, " Distributed average consensus
with least-mean-square deviation, " J. Parallel Distrib.
Comput., vol. 67, no. 1, pp. 33-46, Jan. 2007.
[11] L. Pescosolido, S. Barbarossa, and G. Scutari, " Average
consensus algorithms robust against channel noise, " in
Proc. IEEE IEEE 9th Int. Workshop Signal Process. Adv.
Wireless Commun., Recife, Brazil, , 2008, pp. 261-265.
[12] R. Olfati-Saber and R. M. Murray, " Consensus problems
in networks of agents with switching topology and timedelays, "
IEEE Trans. Autom. Control, vol. 49, no. 9,
pp. 1520-1533, Sep. 2004.
[13] I. D. Schizas, A. Ribeiro, and G. B. Giannakis, " Consensus
in ad hoc WSNs with noisy links - Part I: Distributed estimation
of deterministic signals, " IEEE Trans. Signal Process.,
vol. 56, no. 1, pp. 350-364, Jan. 2008.
[14] L. Pescosolido, S. Barbarossa, and G. Scutari, " Radar sensor
networks with distributed detection capabilities, " in
Proc. IEEE Radar Conf., Rome, Italy, 2008, pp. 1-6.
IEEE A&E SYSTEMS MAGAZINE
NOVEMBER 2022
IEEE - Aerospace and Electronic Systems - November 2022

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