Coherent hidden markov model

• US 5,900,835 A
• Filed: 07/09/1998
• Issued: 05/04/1999
• Est. Priority Date: 07/09/1998
• Status: Expired due to Fees

• Current Assignee: United States Department Of Health And Human Services
• Original Assignee: the united states of america as represented by the secretary of the navy
• Inventors: Stein, David
• Primary Examiner(s): Lobo, Ian J.
• Application Number:
  US09/112,906
• Time in Patent Office: 299 Days
• Field of Search: 342/159, 342/195, 342/89, 342/90
• US Class Current: 342/159
• CPC Class Codes: G01S 13/5246 post processors for coherent MTI discriminators, e.g. residue cancellers, CFAR after Doppler filters
• CPC Class Codes: G01S 7/2922 by using a controlled threshold

Abstract

A hidden Markov detector of the present invention comprises a hidden Markovarameter estimator for coupling to an input data source, a parameter smoother coupled to the parameter estimator, and a detection statistic calculator coupled to the parameter smoother and the input data source.

Claims

1. A hidden Markov detector comprising:

a hidden Markov parameter estimator for coupling to an input data source;

a parameter smoother coupled to the parameter estimator;

and a detection statistic calculator coupled to the parameter smoother and the input data source.

2. The hidden Markov detector of claim 1 further comprising the input data source.

3. The hidden Markov detector of claim 2 wherein the input data source outputs radar return data.

4. The hidden Markov detector of claim 1 further comprising an adaptive normalizer coupled to the detection statistic calculator.

5. The hidden Markov detector of claim 1 further comprising a threshold comparator coupled to the adaptive normalizer.

6. The hidden Markov detector of claim 1 further comprising a display coupled to the threshold comparator.

7. The hidden Markov detector of claim 1 wherein the detection statistic calculator comprises:

a path selector for selecting a probable state path;

a data normalizer coupled to the path selector for each probable state path respectively;

a discrete Fourier transform coupled to each data normalizer respectively;

a modified Bessel function coupled to each discrete Fourier transform respectively;

a calculation weight multiplier coupled to each modified Bessel function for generating a weighted detection statistic;

and a sum function for adding the weighted detection statistics and calculating a logarithm of their sum to generate a detector output.

8. The hidden Markov detector of claim 7 wherein the detector output LHMC_k (x) is substantially defined by the equation:

##EQU6## wherein;

k is a coefficient index,

a is an estimate of signal amplitude,

x is an input data vector,

s is a probable path of states vector,

S is a set of most probable state paths,

I₀ ( ) is a modified Bessel function of order zero,

v is a state variance vector,

(x/ v_s) _k is the k^th Fourier coefficient of the normalized input data vector, ##EQU7## p<

s|x>

is the probability of state path s given the input data vector x, and ##EQU8##

9. The hidden Markov detector of claim 7 wherein the parameter smoother performs the following steps:

replacing original estimates of an expected intensity obtained for each block of range bins with an order statistic applied to the set of original estimates of the expected intensity;

adding a second state for the expected intensity estimates and defining corresponding state transition probabilities and initial probabilities for the second state;

replacing the second state expected intensity estimates that exceed an upper limit value with the limit value; and

replacing transition probabilities a₁,j that exceed an upper bound with the upper bound and adjusting other transition probabilities to maintain a condition expressed by the following formula;

##EQU9##