Dimensionality Reduction Techniques for Efficient Adaptive Pulse Compression

Blunt, Shannon D.; Higgins, Thomas

doi:10.1109/taes.2010.5417167

Cited by 37 publications

(20 citation statements)

References 13 publications

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“…As a result, the total length of the processing window decrease by 2( −1) range cells at each stage [2] [3]. The number of iterations is therefore limited by both the available length of the received signal and length of the desired output.…”

Section: Signal Model and The Apcmentioning

confidence: 99%

“…However, in the presents of adjacent large scatterers, the range sidelobes produce interferences to the cell under observation. To mitigate this imperfection, Blunt et al propose a filtering technique based on linear minimum mean square error (LMMSE) [2] [3]. Denoted as reiterated LMMSE-based adaptive pulse compression (APC), the method shows effective side lobe interference suppression ability in both simulation and real data experiments [4] [5].…”

Section: Introductionmentioning

confidence: 99%

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Range limited adaptive pulse compression via linear Bayes estimation

Kong

et al. 2014

2014 IEEE Radar Conference

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The original adaptive pulse compression (APC) algorithm and its modified version, the Gain-Constraint APC (GCAPC), have the processing window decreasing effect, i.e. the length of the estimating output will decrease by a constant on each iteration stage. As a result, the iteration number is limited by the available input length and the extra computational load is inevitable for large initial processing window. Our analysis indicates that the estimating methods in APC and the GCAPC algorithms are special cases of the linear Bayes estimation. We derive the edge extended APC (EAPC) and GCAPC (EGCAPC) and an iterative linear Bayesian (ILB) algorithms that free from the processing window decreasing effect. We also proposed a modified iterative linear Bayesian (MILB) algorithm based on the Bayes theory and the original iterative algorithm that further improves the estimating performance. The MILB is compared with the EAPC, EGCAPC and ILB via simulations and is shown to be superior.

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Section: Signal Model and The Apcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Range limited adaptive pulse compression via linear Bayes estimation

Kong

et al. 2014

2014 IEEE Radar Conference

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“…The signal received at the radar can be represented as a convolution of the transmitted signal and the impulse response of the range profile illuminated by the radar [1,2]. Let the vector s denote N discrete samples of the transmitted waveform…”

Section: Introductionmentioning

confidence: 99%

Multistage Adaptive Pulse Compression

Vouras¹

2013

2013 IEEE Radar Conference (RadarCon13)

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Abstract-Many modern radar systems employ pulse compression to maximize the energy on target while maintaining high range resolution. For a solitary point target in white noise, employing a matched filter on receive will maximize the target signal-to-noise ratio (SNR) at the output of the receiver. The matched filter itself is a time-reversed version of the transmitted waveform which is convolved with the received time series to pulse compress the data. A drawback to the matched filter receiver is the range sidelobes which extend on either side of the point target and may mask another weaker target. To reduce range sidelobes after pulse compression, novel adaptive pulse compression techniques have been developed.One such technique is the Reiterative Minimum Mean Square Error Adaptive Pulse Compression (RMMSE-APC) algorithm. This algorithm employs an optimal compression filter at each range bin and significantly reduces the range sidelobes in the vicinity of large targets. In this paper, a pulse compression filter with output identical to the RMMSE filter is derived by employing a multi-stage decomposition of the Wiener filter. A reduced rank version of the Multi-Stage Wiener Filter (MSWF) with lower computational complexity can be created by pruning the number of stages in the decomposition. I. BACKGROUNDThe signal received at the radar can be represented as a convolution of the transmitted signal and the impulse response of the range profile illuminated by the radar [1,2]. Let the vector s denote N discrete samples of the transmitted waveformT , and letT represent a length N portion of the illuminated range profile for l = 0, 1, …, L-1, where l is a discrete delay index and L is the number of range cells of interest. Then the signal received at the radar iswhere v(l) is an N-by-1 vector of additive noise samples. The noise is assumed to be temporally and spatially white and uncorrelated with the transmitted signal samples s(k). The matrix A is written as ( )

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“…The computational complexity of APC depends on the number of operational stages and thus limits its use as a real-time system. The fast adaptive pulse compression (FAPC) algorithm [18] has been suggested with less computational complexity compared to the APC but with improved sensitivity. To alleviates the effect of the Doppler shift due to moving targets a Doppler Compensated APC (DC-APC) algorithm has been suggested [19].…”

Section: Introductionmentioning

confidence: 99%

An efficient hybrid adaptive pulse compression approach to radar detection

Baghel¹,

Panda

2013

2013 International Conference on Signal Processing and Communication (Icsc)

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The adaptive pulse compression (APC) technique has been proposed in the literature of pulse compression radar for superior performance for polyphase coded signals in comparison to conventional matched filter (MF) and mismatched filter techniques. However, its performance degrades in case of binary phase coded signals. The recently reported hybrid MF and radial function (MF-RF) model performs well only for the single target case. Thus there is a need to develop a model which would perform efficiently both for single and multi-targets conditions for binary phase coded signals. To achieve this objective a hybrid model (APC-RF) combining the APC and radial function is developed. The performance of the new scheme has been evaluated under different noise and Doppler shift conditions. The results of the simulation study demonstrate superior performance of the proposed APC-RF model over several pulse compression techniques such as MF, APC and MF-RF.Index Terms-pulse compression; matched filter; adaptive pulse compression; minimum mean square error.

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Dimensionality Reduction Techniques for Efficient Adaptive Pulse Compression

Cited by 37 publications

References 13 publications

Range limited adaptive pulse compression via linear Bayes estimation

Range limited adaptive pulse compression via linear Bayes estimation

Multistage Adaptive Pulse Compression

An efficient hybrid adaptive pulse compression approach to radar detection

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