Ground clutter mitigation using a parametric time domain method
View Patent ↗Methods and systems are disclosed for investigating a region of interest with a radar. A radar signal is propagated to the region of interest. Sampled time-domain radar data scattered within the region of interest are collected. A likelihood function is calculated with the sampled time-domain data within a parametric model of the region of interest for a defined set of parameters. The set of parameters in varied to find an extremum of the likelihood function.
1. A method of investigating a region of interest with a radar, the method comprising:
propagating a radar signal to the region of interest;
generating a waveform with a uniform pulsing scheme or nonuniform pulsing scheme;
collecting sampled time-domain radar data scattered within the region of interest;
calculating a sample covariance matrix with the sampled time-domain data; and
calculating parameters of models that describe ground clutter, system noise, and weather signal, and that minimize a likelihood function calculated with the sample covariance matrix.
2. The method recited in claim 1 wherein the likelihood function is L(θ)=ln(|R(θ)|)+tr(R −1 (θ){circumflex over (R)} v ), where R is the covariance matrix having elements
R
[
k
,
l
]
=
P
p
exp
[
-
8
π
2
σ
p
2
(
k
-
l
)
2
T
s
2
λ
2
]
exp
[
-
j
4
π
v
_
(
k
-
l
)
T
s
λ
]
++
P
c
exp
[
-
8
π
2
σ
c
2
(
k
-
l
)
2
T
s
2
λ
2
]
+
2
T
s
λ
σ
N
2
δ
(
k
-
l
)
,
for
k
,
l
=
1
,
…
,
N
,
wherein:
T s is a measured signal sample;
λ is a wavelength of the radar signal;
j is √{square root over (−1)};
δ is a Kronecker function; and
the set of parameters comprises:
P p as a precipitation signal power;
σ p as a precipitation spectrum width;
v as a mean velocity of precipitation;
P p as a clutter power;
σ as a clutter spectrum width; and
σ N 2 as a noise power.
3. The method recited in claim 1 wherein collecting sampled time-domain radar data comprises collected time-domain radar data distributed nonuniformly in time.
4. The method recited in claim 3 wherein time spacings between subsequent collected time-domain radar data are substantially in a ratio of 2:3.
5. The method recited in claim 3 wherein time spacings between subsequent collected time-domain radar data are substantially in a ratio of 3:4.
6. A radar system for investigating a region of interest with a radar, the system comprising:
a radar source configured to propagate a radar signal;
a radar detector configured to collect radar data; and
a computational system in communication with the radar source and with the radar detector, the computational system comprising a processor and a memory coupled with the processor, the memory comprising a computer-readable medium having a computer-readable program embodied therein for direction operation of the radar system to investigate the region of interest, the computer-readable program including:
instructions for propagating the radar signal into the region of interest with the radar source;
instructions for generating a waveform with a uniform pulsing scheme or a nonuniform pulsing scheme with the radar source;
instructions for collecting sampled time-domain radar data scattered within the region of interest with the radar detector;
instructions for calculating sample covariance matrix with the sampled time-domain data; and
instructions for calculating parameters of models that describe ground clutter, system noise, and weather signal, and that minimize a likelihood function calculated with the sample covariance matrix.
7. The radar system recited in claim 6 wherein the likelihood function is L(θ)=ln(|R(θ)|)+tr(R −1 (θ){circumflex over (R)} v ), where R is the covariance matrix having elements
R
[
k
,
l
]
=
P
p
exp
[
-
8
π
2
σ
p
2
(
k
-
l
)
2
T
s
2
λ
2
]
exp
[
-
j
4
π
v
_
(
k
-
l
)
T
s
λ
]
++
P
c
exp
[
-
8
π
2
σ
c
2
(
k
-
l
)
2
T
s
2
λ
2
]
+
2
T
s
λ
σ
N
2
δ
(
k
-
l
)
,
for
k
,
l
=
1
,
…
,
N
,
wherein:
T s is a measured signal sample;
λ is a wavelength of the radar signal;
j is √{square root over (−1)};
σ is a Kronecker function; and
the set of parameters comprises:
P p as a precipitation signal power;
σ p as a precipitation spectrum width;
v as a mean velocity of precipitation;
P c b as a clutter power;
σ c as a clutter spectrum width; and
σ n 2 as a noise power.
8. The radar system recited in claim 6 wherein the instructions for collecting sampled time-domain radar data comprise instructions for collecting time-domain radar data distributed nonuniformly in time.
9. The radar system recited in claim 6 wherein time spacings between subsequent collected time-domain radar data are substantially in a ratio of 2:3.
10. The radar system recited in claim 6 , wherein time spacings between subsequent collected time-domain radar data are substantially in a ratio of 3:4.
11. The method recited in claim 1 wherein calculating the sample covariance matrix comprises calculating a sample covariance matrix representative of the data collected within the region of interest by averaging covariance matrices calculated for each data subset.
12. The method recited in claim 1 further comprising calculating final parameters of ground clutter, noise, and weather-echo parametric time domain models by finding an extremum of the likelihood function.
13. The method recited in claim 1 further comprising computing a normalized trace of product between sample covariance matrix and estimated covariance matrix.
14. The radar system recited in claim 6 wherein the instructions for calculating the sample covariance matrix comprise instructions for calculating a sample covariance matrix representative of the data collected within the region of interest by averaging covariance matrices calculated for each data subset.
15. The radar system recited in claim 6 wherein the computer-readable program further includes calculating final parameters of ground clutter, noise, and weather-echo parametric time domain models by finding an extremum of the likelihood function.
16. The radar system recited in claim 6 wherein the computer-readable program further includes computing a normalized trace of product between sample covariance matrix and estimated covariance matrix.