Adaptive Clutter Filtering Using Autogressive Spectral Estimation

Bowyer, Duane E.; Rajasekaran, P.; Gebhart, W.W.

doi:10.1109/taes.1979.308738

Cited by 31 publications

(10 citation statements)

References 9 publications

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“…One such method is to model the clutter process as the output of a low model order multichannel moving average system driven by white noise. This kind of parametric approximation has been well documented in the radar literature [11]- [14]. In [15], Rangaswamy et al, showed that for a phased array monostatic radar system, the model order of 4 provides a decent approximation the ground clutter observed using real radar data.…”

Section: Introductionmentioning

confidence: 85%

Improving multistatic MIMO radar performance in data-limited scenarios

Qureshi

Rangaswamy

Bell

2014

2014 48th Asilomar Conference on Signals, Systems and Computers

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A MIMO Multistatic radar system consists of multiple bistatic MIMO pairs working in potentially different configurations. If a bistatic pair in a Multistatic MIMO radar system employs multiple transmit and receive elements, this increases the dimensionality of the data received over a Coherent Processing Interval (CPI), which in turn increases the training data needed to reliably estimate the covariance matrix. This, coupled with the non-stationarity in the received data resulting from the bistatic geometry further degrades the quality of the covariance matrix estimate used in the adaptive detector. In [1], Bell et al. presented a physics based MIMO clutter model, and showed that lack of training data support renders the MIMO radar unfeasible in that the individual bistatic pairs can outperform the overall MIMO system. For these systems, we need to investigate techniques that perform reasonably well in data limited scenarios. In this paper, we show that the physics based clutter model presented in [1] can be approximated as an AR process of model order 4. This has implications for the amount of data that is needed to reliably estimate the AR parameters. For the purpose of this discussion, we use the optimum AR coefficients for every model order generated using the clairvoyant clutter covariance matrix, and characterize the performance using two metrics: SINR loss, and the probability of detection as a function of SINR.

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Section: Introductionmentioning

confidence: 85%

Improving multistatic MIMO radar performance in data-limited scenarios

Qureshi

Rangaswamy

Bell

2014

2014 48th Asilomar Conference on Signals, Systems and Computers

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“…To deal with the practical case of non-Gaussian noise, prewhitening is proposed [9], to flatten the interference spectrum. Alternatively for interference from specific clutter scatterers, an iterative adaptive matched filtering process can be used to adapt to the reflected signal and the clutter from the nearby range cells [10].…”

Section: Traditional Radar Adaptationmentioning

confidence: 99%

Utilizing Q-Learning to allow a radar to choose its transmit frequency, adapting to its environment

Wabeke¹,

Nel²

2010

2010 2nd International Workshop on Cognitive Information Processing

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Recent research show that utilization of knowledge of the environment can allow a radar system to adapt its processing to improve its performance. Furthermore, a radar system that utilize both a-priori and measured knowledge in an adaptive close loop manner could seem to be cognitive of its environment, able to adapt to changes to optimize performance. Reinforced learning could play a vital role as part of such a closed-loop cognitive radar system. The Q-Learning algorithm is hypothesized to be useful for this cognitive radar domain. This paper investigates the problem of adaptively choosing the radar transmit frequency through application of Q-Learning on measured radar data. A comparison is made against other frequency selection algorithms and its shown that Q-Learning manages to learn a good strategy to adaptively select radar transmit frequency, mostly outperforming the other methods tested in the scenario investigated here.

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“…Most of the interest, however, appears to have taken place within the past few years [4,6,8,14,21,23,41,43,45,63]. The principal advantage of characterizing the observation processes for each hypothesis via a parametric model is that well known algorithms can be utilized to estimate the parameters.…”

Section: S(t) = E [S(t)l X(t') T' < T] (1 -2)mentioning

confidence: 99%

“…For stationary processes, the filter weights will be determined as discussed in section VI B. The processes F,(n) or 1(n) will then be computed in terms of the likelihood ratio [see eq (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21) as well as Fig. 6-1].…”

Section: Monte Carlo Analysis (Stationary Processes)mentioning

confidence: 99%

A Parametric Detection Approach Using Multichannel Processes

Michels¹

1989

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Adaptive Clutter Filtering Using Autogressive Spectral Estimation

Cited by 31 publications

References 9 publications

Improving multistatic MIMO radar performance in data-limited scenarios

Improving multistatic MIMO radar performance in data-limited scenarios

Utilizing Q-Learning to allow a radar to choose its transmit frequency, adapting to its environment

A Parametric Detection Approach Using Multichannel Processes

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