摘要 :
High-frequency(HF) skywave over-the-horizon
radar(OTHR) looks down its targets from the ionosphere, it
has a large operating range and a large cover areas, but it
received a large-amplitude backscatterer echo form the
terrain...
展开
High-frequency(HF) skywave over-the-horizon
radar(OTHR) looks down its targets from the ionosphere, it
has a large operating range and a large cover areas, but it
received a large-amplitude backscatterer echo form the
terrain and sea, and strong interference, such as, radio
frequency interference, industrial interference, impulsive
noise, lightning impulsive, meteor trail echoes and so on.
Interference can be divided into long time interference and
transient interference, transient interference lasts a short
time, but its intensity is great. This paper first discusses the
character of transient interference, and uses eigen-
decomposition to separate the sea clutter subspace and
filter it, or filters terrestrial clutter in frequency domain and
back to time domain, then the transient interference is
detected, after that the transient interference are excised
from the original echoes, finally, the excised clutter and
target echoes are predicted by Burg linear prediction
algorithm. This processing method has been successfully
applied to the real data from China experimental OTHR.
收起
摘要 :
High-frequency(HF) skywave over-the-horizon
radar(OTHR) looks down its targets from the ionosphere, it
has a large operating range and a large cover areas, but it
received a large-amplitude backscatterer echo form the
terrain...
展开
High-frequency(HF) skywave over-the-horizon
radar(OTHR) looks down its targets from the ionosphere, it
has a large operating range and a large cover areas, but it
received a large-amplitude backscatterer echo form the
terrain and sea, and strong interference, such as, radio
frequency interference, industrial interference, impulsive
noise, lightning impulsive, meteor trail echoes and so on.
Interference can be divided into long time interference and
transient interference, transient interference lasts a short
time, but its intensity is great. This paper first discusses the
character of transient interference, and uses eigen-
decomposition to separate the sea clutter subspace and
filter it, or filters terrestrial clutter in frequency domain and
back to time domain, then the transient interference is
detected, after that the transient interference are excised
from the original echoes, finally, the excised clutter and
target echoes are predicted by Burg linear prediction
algorithm. This processing method has been successfully
applied to the real data from China experimental OTHR.
收起
摘要 :
High-frequency(HF) skywave over-the-horizon
radar(OTHR) looks down its targets from the ionosphere, it
has a large operating range and a large cover areas, but it
received a large-amplitude backscatterer echo form the
terrain...
展开
High-frequency(HF) skywave over-the-horizon
radar(OTHR) looks down its targets from the ionosphere, it
has a large operating range and a large cover areas, but it
received a large-amplitude backscatterer echo form the
terrain and sea, and strong interference, such as, radio
frequency interference, industrial interference, impulsive
noise, lightning impulsive, meteor trail echoes and so on.
Interference can be divided into long time interference and
transient interference, transient interference lasts a short
time, but its intensity is great. This paper first discusses the
character of transient interference, and uses eigen-
decomposition to separate the sea clutter subspace and
filter it, or filters terrestrial clutter in frequency domain and
back to time domain, then the transient interference is
detected, after that the transient interference are excised
from the original echoes, finally, the excised clutter and
target echoes are predicted by Burg linear prediction
algorithm. This processing method has been successfully
applied to the real data from China experimental OTHR.
收起
摘要 :
Target tracking in over-the-horizon radar (OTHR) is a challenging problem due to the existence of multiple propagation modes between the transmitter,target and receiver.Up to present,all OTHR based target-tracking methods require ...
展开
Target tracking in over-the-horizon radar (OTHR) is a challenging problem due to the existence of multiple propagation modes between the transmitter,target and receiver.Up to present,all OTHR based target-tracking methods require that the ionospheric parameters should be available via the ionosondes.However,the ionosondes can not be arbitrary deployed,for example,in sea area or hostile zone.
收起
摘要 :
Target tracking in over-the-horizon radar (OTHR) is a challenging problem due to the existence of multiple propagation modes between the transmitter, target and receiver. Up to present, all OTHR based target-tracking methods requi...
展开
Target tracking in over-the-horizon radar (OTHR) is a challenging problem due to the existence of multiple propagation modes between the transmitter, target and receiver. Up to present, all OTHR based target-tracking methods require that the ionospheric parameters should be available via the ionosondes. However, the ionosondes can not be arbitrary deployed, for example, in sea area or hostile zone. Besides, the couple of mode identification and state estimation (estimation errors increase identification risk while identification mistake leads to estimation divergence to the actual value) in OTHR target tracking is another challenge. In this paper, a maximum-a-posterior joint mode identification and state estimation algorithm independent of ionosondes is proposed. Firstly, the joint optimization function is derived based on Maximum a Posteriori Penalty Function method. Through modeling both slant returns of different ray models and ray model sequence modeled as Markov process, mode identification can be performed recursively in Viterbi algorithm. Finally, though defining a quadratic penalty function, state estimation can be solved via extended Kalman filter. The simulation shows that the proposed method can effectively estimate the target state and ionospheric heights without the help of ionosondes.
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摘要 :
Target tracking in over-the-horizon radar (OTHR) is a challenging problem due to the existence of multiple propagation modes between the transmitter, target and receiver. Up to present, all OTHR based target-tracking methods requi...
展开
Target tracking in over-the-horizon radar (OTHR) is a challenging problem due to the existence of multiple propagation modes between the transmitter, target and receiver. Up to present, all OTHR based target-tracking methods require that the ionospheric parameters should be available via the ionosondes. However, the ionosondes can not be arbitrary deployed, for example, in sea area or hostile zone. Besides, the couple of mode identification and state estimation (estimation errors increase identification risk while identification mistake leads to estimation divergence to the actual value) in OTHR target tracking is another challenge. In this paper, a maximum-a-posterior joint mode identification and state estimation algorithm independent of ionosondes is proposed. Firstly, the joint optimization function is derived based on Maximum a Posteriori Penalty Function method. Through modeling both slant returns of different ray models and ray model sequence modeled as Markov process, mode identification can be performed recursively in Viterbi algorithm. Finally, though defining a quadratic penalty function, state estimation can be solved via extended Kalman filter. The simulation shows that the proposed method can effectively estimate the target state and ionospheric heights without the help of ionosondes.
收起
摘要 :
Target tracking in over-the-horizon radar (OTHR) is a challenging problem due to the existence of multiple propagation modes between the transmitter, target and receiver. Up to present, all OTHR based target-tracking methods requi...
展开
Target tracking in over-the-horizon radar (OTHR) is a challenging problem due to the existence of multiple propagation modes between the transmitter, target and receiver. Up to present, all OTHR based target-tracking methods require that the ionospheric parameters should be available via the ionosondes. However, the ionosondes can not be arbitrary deployed, for example, in sea area or hostile zone. Besides, the couple of mode identification and state estimation (estimation errors increase identification risk while identification mistake leads to estimation divergence to the actual value) in OTHR target tracking is another challenge. In this paper, a maximum-a-posterior joint mode identification and state estimation algorithm independent of ionosondes is proposed. Firstly, the joint optimization function is derived based on Maximum a Posteriori Penalty Function method. Through modeling both slant returns of different ray models and ray model sequence modeled as Markov process, mode identification can be performed recursively in Viterbi algorithm. Finally, though defining a quadratic penalty function, state estimation can be solved via extended Kalman filter. The simulation shows that the proposed method can effectively estimate the target state and ionospheric heights without the help of ionosondes.
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摘要 :
Skywave over-the-horizon radar (OTHR) often encounters strong transient noise which raises the power spectrum of the return signal and dramatically degrades the target detection performance of the radar system. To locate and elimi...
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Skywave over-the-horizon radar (OTHR) often encounters strong transient noise which raises the power spectrum of the return signal and dramatically degrades the target detection performance of the radar system. To locate and eliminate the transient noise without distorting the clutter return (so that the clutter can be properly handled in a subsequent processing), one can transform the received signal into the time-frequency plane using an appropriate time-frequency representation. In this paper, we propose a transient noise excision algorithm based on the S transform and ridge detection. The proposed algorithm has low computational complexity and is more suitable for the skywave OTHR than the existing time-frequency transient noise excision algorithms. Using real experimental skywave OTHR data, simulation results are provided to verify the effectiveness of the new algorithm.
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摘要 :
The performance of over-the-horizon radar
(OTHR) degrades significantly due to the transient
interferences such as lightning and meteor train echoes. In
this paper, we propose to utilize the adaptive timefrequency
analysis tec...
展开
The performance of over-the-horizon radar
(OTHR) degrades significantly due to the transient
interferences such as lightning and meteor train echoes. In
this paper, we propose to utilize the adaptive timefrequency
analysis technique to suppress the transient
interference. It firstly parameterizes the radar signal, then
the transient interference as well as the ocean/ground
clutter and target signal can be separated in parameter
space, thereby implementing the interference suppression.
No data interpolation is involved in this technique, hence
it may provide better suppression performance when
relatively long transient interference appears or short
coherent integration time (CIT) is used.
收起
摘要 :
The performance of over-the-horizon radar
(OTHR) degrades significantly due to the transient
interferences such as lightning and meteor train echoes. In
this paper, we propose to utilize the adaptive timefrequency
analysis tec...
展开
The performance of over-the-horizon radar
(OTHR) degrades significantly due to the transient
interferences such as lightning and meteor train echoes. In
this paper, we propose to utilize the adaptive timefrequency
analysis technique to suppress the transient
interference. It firstly parameterizes the radar signal, then
the transient interference as well as the ocean/ground
clutter and target signal can be separated in parameter
space, thereby implementing the interference suppression.
No data interpolation is involved in this technique, hence
it may provide better suppression performance when
relatively long transient interference appears or short
coherent integration time (CIT) is used.
收起
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