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

Abstract

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.

A Scalable Arrangement Method for Aperiodic Array Antennas to Reduce Peak Sidelobe Level

“…where AF ps (θ, ϕ), AF s (θ, ϕ) are the subarray position factor and subarray factor. Then the PSLL can be calculated by PSLL (dB) = 20 log 10 AF sl,max (u, v) AF main (6) where AF sl,max (u,v) is the maximum sidelobe value of the radiation pattern. AF main is the peak value of the main lobe.…”

“…For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ communication, remote sensing [6], [7], [8] and medical imaging [9], [10]. Aperiodic array antennas with optimized elements can greatly reduce PSLL, but for large and medium-sized array, the array structure with high degree of freedom makes it difficult to realize wave control and power feeding.…”

A Space–Time–Range Joint Adaptive Focusing and Detection Method for Multiple Input Multiple Output Radar

“…For MIMO radar systems, two commonly employed pre-detection methods are Matched Filtering-Beam Forming-Discrete Fourier Transform (MF-BF-DFT) [23] and Adaptive Pulse Compression-Beam Forming-Discrete Fourier Transform (APC-BF-DFT) [24]. The MF-BF-DFT method is the simplest and most convenient method for engineering applications, while the APC-BF-DFT method improves the effect of suppression on range sidelobes compared to the former method but increases computation complexity.…”