Pulse Compression Sidelobe Reduction by Minimization of L/sub p/-Norms

Cilliers, J.E.; Smit, Jw

doi:10.1109/taes.2007.4383616

Cited by 77 publications

(42 citation statements)

References 21 publications

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“…Alternatively, the requirement on the SNR can be relaxed, and a mismatched filter can be considered instead. In [27,29], significant reduction on the sidelobes of the overall impulse response can be obtained, if different L p -norm minimization criteria are considered. Observe that regardless of the optimization criteria employed, it is not possible in general to reconstruct the input perfectly, unless an IIR receiver is used, which brings up noise amplification issues in practice.…”

Section: Observation Windowmentioning

confidence: 99%

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Roles of equalization in radar imaging: modeling for superesolution in 3D reconstruction

Merched

2012

EURASIP J. Adv. Signal Process.

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In radar imaging, resolution is generally dictated by its corresponding system point spread function, the response to a point source as a result of an external excitation. This notion of resolution turns out to be rather questionable, as the interpretation of echoes received from a range of continuous targets according to a linear model allows one to cast the imaging problem as a communication system that maps the target reflectivity function onto measurements, which in turn suggests that by virtue of sampling and equalization, one can achieve unlimited spatial resolution. This article reviews the fundamental problem inherent to pulse compression in a multistatic multi-input-multi-output (MIMO) scenario, from a communications viewpoint, in both focused and un-focused scenarios. We generalize the notion of 1D range compression and replace it by a more general 4D pulse compression. The process of focusing and scanning over a 3D object can be interpreted as a MIMO 4D convolution between a reflectivity tensor and a space-varying system, which naturally induces a 4D MIMO channel convolution model. This implies that several well-established block and linear equalization methods can be easily extended to a 3D scenario with the purpose of achieving exact reconstruction of a given reflectivity volume. That is, assuming that no multiple scattering occurs, resolution is only limited in range by the sampling device in the unfocused case, while unlimited in case of focusing at multiple depths. Exact reconstruction under a zero-forcing or least-squares criterion depends solely on the amount of diversity induced by sampling in both space (via scanning rate) and time (via sampling rate), which further allows for a tradeoff between range and cross-range resolution. For instance, the fastest scanning rate is achieved by steering non overlapping beams, in which case portions of the object can be reconstructed independently from each other.

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Section: Observation Windowmentioning

confidence: 99%

“…where [·] :, N + B-2-n denotes the (N + B -2 -n)th column of the argument, and Y d 0 ,n is defined in (29). This implies that…”

Section: Mtsmentioning

confidence: 99%

Roles of equalization in radar imaging: modeling for superesolution in 3D reconstruction

Merched

2012

EURASIP J. Adv. Signal Process.

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“…This leads to the minimization of the energy in the sidelobes, whereas minimization of the L ∞ -norm will minimize the peak sidelobe level. The L ∞ -norm is not a well behaved function, so p L -norms with 2 p P = were used in [1] and the solutions were found by means of numerical techniques.…”

Section: Sidelobe Level Reduction By Application Of Norm Minimizationmentioning

confidence: 99%

“…This prompted the authors to investigate the effect of coefficient quantization on the extremely low sidelobe levels obtained in [1], an example of which is shown in Figure 2. The transmit and receive waveforms were quantized to 8 and 16 bits and the pulse compressor outputs were simulated with the assumption that all bit growth was retained in the FIR filter implementation.…”

Section: Effect Of Coefficient Quantizationmentioning

confidence: 99%

“…Post-processing of the pulse compressor output such as sidelobe cancellation [17] and sidelobe smoothing [18] have also been reported. Alternatively the pulse compression filter can be deliberately mismatched to reduce the sidelobe levels, but this implies a loss in signal to noise ratio and broadening of the compression peak as reported in [1]. An overview of the sidelobe reduction technique presented in [1] is given in the following section.…”

Section: Introductionmentioning

confidence: 99%

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On the effects of quantization on mismatched pulse compression filters designed using L-p norm minimization techniques

Cilliers

Smit

2007

IET International Conference on Radar Systems 2007

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In [1] the authors introduced a technique for generating mismatched pulse compression filters for linear frequency chirp signals. The technique minimizes the sum of the pulse compression sidelobes in an p L -norm sense. It was shown that extremely constant sidelobe levels (better than 60 dB) can be achieved for minimal mismatch loss and broadening of the compression peak. This paper reports on an investigation into the effect of quantization on pulse compression filters designed using the abovementioned technique. Simulation results for 8-bit and 16-bit implementations of a pulse compressor system are presented.

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Space Group Targets Detecting and Resolving Algorithm via Ultra-low Sidelobe Filtering

Chen¹,

Lu²,

Liu³

et al. 2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Pulse Compression Sidelobe Reduction by Minimization of L/sub p/-Norms

Cited by 77 publications

References 21 publications

Roles of equalization in radar imaging: modeling for superesolution in 3D reconstruction

Roles of equalization in radar imaging: modeling for superesolution in 3D reconstruction

On the effects of quantization on mismatched pulse compression filters designed using L-p norm minimization techniques

Space Group Targets Detecting and Resolving Algorithm via Ultra-low Sidelobe Filtering

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