Three Dimensional Matched Filtering

Fries, Robert W.

doi:10.1117/12.951426

Cited by 7 publications

(9 citation statements)

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“…öp,q,v k,lr€ (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13) From the above equations the noncausal minimum variance prediction error filter can be solved for as ,(zi ,z2,z3)=1-I i: a(kJ,r).zfkzlz kJ,eDij)…”

Section: Non-causal Minimum Variance Clutter Modelingmentioning

confidence: 99%

“…To do this we define the variance of the prediction error to be p2 E[e2(m,n,t)], (2)(3)(4)(5)(6)(7)(8)(9)(10) where the prediction error is defined as e(m,n,t)=x(m,n,t)-x(m,n,t) . (2)(3)(4)(5)(6)(7)(8)(9)(10)(11) The orthogonality condition associated with this minimum variance prediction is E[e(m,n,t)x(m-k,n-l,t-'r)] = fl2k,l,r) , (kJ,'r)E Q3D E[e(m,n,t)x(m-k,n-lj-)] = 0 , (k,l,'r)EQ3D…”

Section: Non-causal Minimum Variance Clutter Modelingmentioning

confidence: 99%

“…(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) The covariance function of the prediction error can be calculated from equation (2-11) . (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) Therefore for the noncausal minimum variance representation the prediction error sequence is not white.…”

Section: Non-causal Minimum Variance Clutter Modelingmentioning

confidence: 99%

“…The problem of detecting weak targets in a highly cluttered, non-stationary background with an Infra-red or Electrooptical (IR/EO) sensor has recently been an area of much research [1][2][3]9,11,12]. This problem consists of processing a temporal sequence of spatial images in such a way as to enhance target detectability, while maintaining an acceptable false alarm rate.…”

Section: Introductionmentioning

confidence: 99%

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<title>Adaptive model-based 3-D target detection: Part I - modeling and algorithms</title>

Hartless

Wang

1992

SPIE Proceedings

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This paper investigates methods for adaptive detection of a 3D (space-time) LR/EO target in clutter of unknown and possibly non-stationary statistics. Non-stationary data conditions necessitate estimation of the environmental parameters over local regions. A low degree-of-freedom model for the space-time clutter charteñstics is assumed. This allows the modelbased matched filter to adapt to the changing clutter characteristics better than the usual technique of matched filter construction, e.g. direct sample matrix inversion (SMI).The modeLs used for characterizing the space-time clutter characterics include a 3D autoregressive model, a non-causal minimum variance representation model, and a space-time separable clutter modeL The matched filter algorithms are derived for these models using the estimated model parameters.In addition, the signal-to-noise ratios (SNR) that are achievable in stationary clutter conditions by the model-based filters are compared to that obtained by the optimal linear filter for a variety of clutter and target characteristics. An analysis of losses due to target/clutter mis-modeling and other effects are also presented.

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Section: Non-causal Minimum Variance Clutter Modelingmentioning

confidence: 99%

Section: Non-causal Minimum Variance Clutter Modelingmentioning

confidence: 99%

Section: Non-causal Minimum Variance Clutter Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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<title>Adaptive model-based 3-D target detection: Part I - modeling and algorithms</title>

Hartless

Wang

1992

SPIE Proceedings

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“…The three-dimensional matched filter 31 requires the processing of entire image sequence containing the target. (16) where s i,j,k is the sampled version of the target at the input to the processor.…”

Section: -D Matched Filtermentioning

confidence: 99%