References of "Mishra, Kumar Vijay"
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See detailLocalization Performance of 1-Bit Passive Radars in NB-IoT Applications
Sedighi, Saeid UL; Mishra, Kumar Vijay; Shankar, Bhavani UL et al

in Sedighi, Saeid; Mishra, Kumar Vijay; Shankar, Bhavani (Eds.) et al Localization Performance of 1-Bit Passive Radars in NB-IoT Applications (2019, December 14)

Location-based services form an important use-case in emerging narrowband Internet-of-Things (NB-IoT) networks. Critical to this offering is an accurate estimation of the location without overlaying the ... [more ▼]

Location-based services form an important use-case in emerging narrowband Internet-of-Things (NB-IoT) networks. Critical to this offering is an accurate estimation of the location without overlaying the network with additional active sensors. The massive number of devices, low power requirement, and low bandwidths restrict the sampling rates of NB-IoT receivers. In this paper, we propose a novel low-complexity approach for NB-IoT target delay estimation in cases where one-bit analog-to-digital-converters (ADCs) are employed to sample the received radar signal instead of high-resolution ADCs. This problem has potential applications in the design of inexpensive NB-IoT radar and sensing devices. We formulate the target estimation as a multivariate fractional optimization problem and solve it via Lasserre's semi-definite program relaxation. Numerical experiments suggest feasibility of the proposed approach yielding high localization accuracy with a very low number of 1-bit samples. [less ▲]

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See detailOptimum Design for Sparse FDA-MIMO Automotive Radar
Sedighi, Saeid UL; Shankar, Bhavani UL; Mishra, Kumar Vijay et al

in Sedighi, Saeid; Shankar, Bhavani; Mishra, Kumar Vijay (Eds.) et al Optimum Design for Sparse FDA-MIMO Automotive Radar (2019, November 03)

Automotive radars usually employ multiple-input multiple-output (MIMO) antenna arrays to achieve high azimuthal resolution with fewer elements than a phased array. Despite this advantage, hardware costs ... [more ▼]

Automotive radars usually employ multiple-input multiple-output (MIMO) antenna arrays to achieve high azimuthal resolution with fewer elements than a phased array. Despite this advantage, hardware costs and desired radar size limits the usage of more antennas in the array. Similar trade-off is encountered while attempting to achieve high range resolution which is limited by the signal bandwidth. However, nowadays given the demand for spectrum from communications services, wide bandwidth is not readily available. To address these issues, we propose a sparse variant of Frequency Diverse Array MIMO (FDA-MIMO) radar which enjoys the benefits of both FDA and MIMO techniques, including fewer elements, decoupling, and efficient joint estimation of target parameters. We then employ the Cram\'{e}r-Rao bound for angle and range estimation as a performance metric to design the optimal antenna placement and carrier frequency offsets for the transmit waveforms. Numerical experiments suggest that the performance of sparse FDA-MIMO radar is very close to the conventional FDA-MIMO despite 50\% reduction in the bandwidth and antenna elements. [less ▲]

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See detailToward Millimeter-Wave Joint Radar Communications: A Signal Processing Perspective
Mishra, Kumar Vijay; Shankar, Bhavani UL; Koivunen, Visa et al

in IEEE Signal Processing Magazine (2019), 36(5), 100-114

Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency (RF) spectrum. Such a joint ... [more ▼]

Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency (RF) spectrum. Such a joint radar communications (JRC) model has advantages of low cost, compact size, less power consumption, spectrum sharing, improved performance, and safety due to enhanced information sharing. Today, millimeter-wave (mmwave) communications have emerged as the preferred technology for short distance wireless links because they provide transmission bandwidth that is several gigahertz wide. This band is also promising for short-range radar applications, which benefit from the high-range resolution arising from large transmit signal bandwidths. Signal processing techniques are critical to the implementation of mm-wave JRC systems. Major challenges are joint waveform design and performance criteria that would optimally trade off between communications and radar functionalities. Novel multiple-input, multiple-output (MIMO) signal processing techniques are required because mm-wave JRC systems employ large antenna arrays. There are opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads. This article provides a signal processing perspective of mm-wave JRC systems with an emphasis on waveform design. [less ▲]

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See detailMeeting the Lower Bound on Designing Set of Unimodular Sequences with Small Aperiodic/Periodic ISL
Alaee-Kerahroodi, Mohammad UL; Shankar, Bhavani; Mishra, Kumar Vijay et al

in 2019 20th International Radar Symposium (IRS) (2019)

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See detailDiscrete-Phase Sequence Design for Coexistence of MIMO Radar and MIMO Communications
Alaee-Kerahroodi, Mohammad UL; Mishra, Kumar Vijay; Shankar, M. R. Bhavani et al

in 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (2019)

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See detailLearning-based rainfall estimation via communication satellite links
Gharanjik, Ahmad UL; Mishra, Kumar Vijay; Shankar, Bhavani UL et al

in 2018 IEEE Statistical Signal Processing Workshop (SSP) (2018)

We present a method for estimating rainfall by opportunistic use of Ka-band satellite communication network. Our approach is based on the attenuation of the satellite link signal in the rain medium and ... [more ▼]

We present a method for estimating rainfall by opportunistic use of Ka-band satellite communication network. Our approach is based on the attenuation of the satellite link signal in the rain medium and exploits the nearly linear relation between the rain rate and the specific attenuation at Ka-band frequencies. Although our experimental setup is not intended to achieve high resolutions as millimeter wavelength weather radars, it is instructive because of easy availability of millions of satellite ground terminals throughout the world. The received signal is obtained over a passive link. Therefore, traditional weather radar signal processing to derive parameters for rainfall estimation algorithms is not feasible here. We overcome this disadvantage by employing neural network learning algorithms to extract relevant information. Initial results reveal that rainfall accumulations obtained through our method are 85% closer to the in situ rain gauge estimates than the nearest C-band German weather service Deutscher Wetterdienst (DWD) radar. [less ▲]

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