Spatial Slow-Time Waveform and Adaptive Receive Filter Design for MIMO Radar

Shi, Chunxuan; Li, Yongzhe; Tao, Ran

doi:10.1109/radarconf2248738.2022.9764280

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…As a frequency selection device, filters can effectively achieve the above goals through reasonable design. In addition, with the increasing level of intelligence in communication systems, the working state of the communication system needs to be flexibly adjusted according to practical applications [6][7][8][9][10][11]. As an important part of the communication system, filters also need to have adjustable functions.…”

Section: Introductionmentioning

confidence: 99%

An intelligent filter with adjustable bandwidth

Zhou,

Tu,

Wang

et al. 2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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A bandwidth intelligent tunable filter based on filter theory is designed in this article. By selecting a special interstage coupling structure and using a varactor circuit, dual frequency band tunable transmission could be achieved within the broadband range. The final design results show that the insertion loss of the filter is reduced to below 1.65dB and the return loss is greater than -15dB. In addition, the fixed frequency of the filter is 290MHz and the bandwidth is greater than 5MHz. The adjustable operating frequency ranges from 228-275.7MHz, the bandwidth is greater than 4.5MHz and the insertion loss is not greater than 1.2dB. The above parameters meet the filter requirements. This filter could achieve effective signal filtering in communication systems, which could ensuring system electromagnetic compatibility.

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Section: Introductionmentioning

confidence: 99%

An intelligent filter with adjustable bandwidth

Zhou,

Tu,

Wang

et al. 2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

View full text Add to dashboard Cite

show abstract

“…Given that the resulting design demands generally the solution of a non-convex and NP-hard optimization problem, several approaches have been successfully employed to yield high-quality waveforms synthesis. These methods include Majorization-Minimization (MM) [11], Cyclic Algorithms (CA) [12], [13], Alternating Direction Method of Multipliers (ADMM) [14], [15], successive Quadratically Constrained Quadratic Programming (QCQP) refinement [9], and Coordinate Descent (CD) paradigms [16], [17].…”

Section: Introductionmentioning

confidence: 99%

FDA-MIMO Transceiver Design Under the Uniform Frequency Increment Constraint

Lan,

Rosamilia,

Aubry

et al. 2024

Trans. Rad. Sys.

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This paper investigates the joint optimization of transmit parameters and receive filter in a Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar system with a uniform frequency increment from sensor to sensor. The problem is formulated as the maximization of the Signal-to-Interference-plus-Noise Ratio (SINR) at the output of the receive filter in a signal-dependent clutter environment, taking into account some practical constraints on the probing waveform and frequency increment. To tackle the resulting non-convex and NPhard optimization problem, a Minorization-Maximization (MM)-Maximum Block Improvement (MBI) algorithm is developed, which iteratively updates the variables block that yields the maximum increase of the objective function while keeping the others fixed. The convergence properties of the proposed algorithm are rigorously studied, and the computational complexity is analyzed. Numerical results demonstrate the effectiveness of the designed procedure under several clutter scenarios of practical relevance, including proper comparisons with counterparts.

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Adaptive Pulse Compression for Sidelobes Reduction in Stretch Processing Based MIMO Radars

2022

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Multiple-Input Multiple-Output (MIMO) radars provide various advantages as compared to conventional radars. Among these advantages, improved angular diversity feature is being explored for future fully autonomous vehicles. Improved angular diversity requires use of orthogonal waveforms at transmit as well as receive sides. This orthogonality between waveforms is critical as the cross-correlation between signals can inhibit the detection of weaker targets due to sidelobes of stronger targets. This paper investigates the Reiterative Minimum Mean Squared Error (RMMSE) mismatch filter design for range sidelobes reduction for a Slow-Time Phase-Coded (ST-PC) Frequency Modulated Continuous Wave (FMCW) MIMO radar. Initially, the performance degradation of RMMSE filter is analyzed for improperly decoded received pulses. It is then shown mathematically that proper decoding of received pulses requires phase compensation related to any phase distortions caused due to doppler and spatial locations of targets. To cater for these phase distortions, it is proposed to re-adjust the traditional order of operations in radar signal processing to doppler, angle and range. Additionally, it is also proposed to incorporate sidelobes decoherence for further suppression of sidelobes. This is achieved by modification of the structured covariance matrix of baseline single-input RMMSE mismatch filter. The modified structured covariance matrix is proposed to include the range estimates corresponding to each transmitter. These proposed modifications provide additional sidelobes suppression while it also provides additional fidelity for target peaks. The proposed approach is demonstrated through simulations as well as field experiments. Superior performance in terms of range sidelobes suppression is observed when compared with baseline RMMSE and traditional Hanning windowed range response.

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Spatial Slow-Time Waveform and Adaptive Receive Filter Design for MIMO Radar

Cited by 3 publications

References 19 publications

An intelligent filter with adjustable bandwidth

An intelligent filter with adjustable bandwidth

FDA-MIMO Transceiver Design Under the Uniform Frequency Increment Constraint

Adaptive Pulse Compression for Sidelobes Reduction in Stretch Processing Based MIMO Radars

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