Ambiguity function and imaging performance of coded FMCW waveforms with fast 4D receiver processing in MIMO radar

Santra, Avik; Ganis, Alexander; Mietzner, Jan; Ziegler, Volker

doi:10.1016/j.dsp.2019.102618

Cited by 19 publications

(7 citation statements)

References 26 publications

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“…This effect can be counteracted by reducing the chirp time, however the practical achievable linearity of the frequency ramp generation, the increased sampling speed requirements of the analog-to-digital converter (ADC), and the flight time of the radar signal from the sensor to the remote target and back limit the minimum practical value of T chirp . Different approaches to overcome this limitation have been proposed, like frequency division multiplex (FDM) [48], [49] or code division multiplex (CDM) [50], [51] of the transmit signals. Another approach is the application of digital modulation schemes, this will be addressed in Section IV.…”

Section: B Modulation and Signal Processingmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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Section: B Modulation and Signal Processingmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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“…where ϕ l; p represents the binary phase encoded by Walsh-Hadamard codes [24,25] (a string of values 0 [þ1] and π [−1]) of length P l . Figure 3, for instance, shows a sequence of four distinct codes (denoted by C 1 , C 2 , C 3 and C 4 ) used to encode signals in group 0 in a time block (assuming four transmitters for group 0).…”

Section: Proposed Algorithm For Waveform Diversitymentioning

confidence: 99%

An efficient waveform diversity based on variational mode decomposition of coded beat‐frequency shifted signals algorithm for multiple‐input multiple‐output millimetre‐wave imaging

Molaei

Yurduseven

Fusco

2021

IET Radar Sonar & Navi

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Waveforms used in multiple-input multiple-output (MIMO) systems must be considered in such a way that individual transceiver (T-R) pairs can be separated from the composite received signal. An algorithm called variational mode decomposition of coded beatfrequency shifted signals (VMD-CBFSS) is presented for waveform diversity. First, transmitters are divided into several groups. A small shift is applied to the carrier frequency of the transmitters of different groups. Also, signals within each group are encoded by assigning a binary phase in a time block. On the receiver side, by using a multiresolution analysis technique, the combined signal is decomposed into its main frequency components and then the composite signals of each group are decoded to retrieve each T-R pair. VMD-CBFSS provides an efficient solution in terms of overall transmitting time and sampling rate in addition to robustness against channel noise. Moreover, the algorithm is employed in a near-field MIMO millimetre-wave imaging system, and results are presented in the form of numerical simulations.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…for a suitable integration interval T , the individual contributions of M transmitting antenna elements can be separated at the receiver [8]- [11]. As a consequence, M N rather than M + N received signals are available for joint spatial processing (which would be the case for a conventional antenna array with M + N physical transceiver antenna elements).…”

Section: B Mimo Radar System Modelmentioning

confidence: 99%

“…As an example, Fig. 3 be realized in time domain, in frequency domain or in the signal domain, using orthogonal modulation schemes such as CDMA (code-division multiple access), OFDM (orthogonal frequency division multiplexing) or orthogonal codes [11]. Within the scope of this paper, we assume that ideal signal orthogonality is given.…”

Section: B Mimo Radar System Modelmentioning

confidence: 99%

“…MIMO radars with distributed antenna elements were shown to provide improved detection and parameter estimation performance for fluctuating targets by exploiting spatial diversity effects [9]. MIMO radars with co-located antennas, on the other hand, achieve enhanced angular resolutions based on a virtual enlargement of the physical antenna aperture [8], which is particularly attractive for compact radar sensors [10], [11].…”

Section: Introductionmentioning

confidence: 99%

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Compressive-Sensing-Aided MIMO Radar Enabling Multi-Functional and Compact Sensors in Air Scenarios Using Optimized Antenna Arrays

et al. 2021

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We address the problem of direction-of-arrival (DoA) estimation for air targets using a compact, multi-functional radar sensor. In order to enhance the angular resolution of such sensors while exploiting the sparseness of typical air scenarios, we consider the combination of a multipleinput multiple-output (MIMO) radar approach with suitable compressive-sensing (CS) techniques. In particular, we investigate the combination of MIMO processing for two-dimensional (2D) antenna arrays with CS-based angular processing for three-dimensional (3D) target localization and 2D DoA estimation in azimuth and elevation. We analyze the benefits of randomized antenna element positions in one and two dimensions and devise optimized array geometries for practicable aperture sizes. In particular, we take physical side constraints into account, such as the smallest realizable/ desired element spacing as well as area restrictions for antenna placement within the overall aperture. The aperture area reserved by this means could be used to accommodate additional hardware components enabling a multi-functional sensor approach. Extensive computer simulation results for different 3D target scenarios illustrate the advantages of our CS-based compact MIMO radar approach with randomized antenna elements compared to fully randomized arrays, given the same number of physical antenna elements.

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Ambiguity function and imaging performance of coded FMCW waveforms with fast 4D receiver processing in MIMO radar

Cited by 19 publications

References 26 publications

Automotive Radar — From First Efforts to Future Systems

Automotive Radar — From First Efforts to Future Systems

An efficient waveform diversity based on variational mode decomposition of coded beat‐frequency shifted signals algorithm for multiple‐input multiple‐output millimetre‐wave imaging

Compressive-Sensing-Aided MIMO Radar Enabling Multi-Functional and Compact Sensors in Air Scenarios Using Optimized Antenna Arrays

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