China Flanker thread
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There is a third option to that designation too, and that is, an upgrade of the existing Su-27, J-11, and J-11A to the J-11B standard. It should be noted that J-8H might actually stand for converted J-8D's to the J-8F avionics standard, and the J-7H are converted J-7Bs to the J-7E avionics standard.
scorpioking
New Member
The "J" stands for "Jian zai", which means fighters for CV.
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link?
You don't have Google Earth? Go to Google and download it.
Anyway, these are the new images from Dingxin showing the sudden surge of J-11s there. I am presuming these are likely to be J-11B as Dingxin is a place used to test the latest toys. Some of these planes are in primer. Note also six J-10s, four of whom seem to be in primer.
Anyway, these are the new images from Dingxin showing the sudden surge of J-11s there. I am presuming these are likely to be J-11B as Dingxin is a place used to test the latest toys. Some of these planes are in primer. Note also six J-10s, four of whom seem to be in primer.
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J-11B_dingxin.JPG -
J-11B_dingxin1.JPG -
J-10_dingxin.JPG
according to wmf,Chinese J-11B radar incorporate "anti stealth" mode.
am I looking "track-before -detection"?
the ideal of track- before- detection was pioneer by the Mitre co.a US corp. back in 1975,as a solution against small RCS target.
according to Janes,Israel officially admit that there CAEW carry TBD technique detect and track stealth target,but i suspect new generation radar such APG-77 have this capability.
am I looking "track-before -detection"?
the ideal of track- before- detection was pioneer by the Mitre co.a US corp. back in 1975,as a solution against small RCS target.
according to Janes,Israel officially admit that there CAEW carry TBD technique detect and track stealth target,but i suspect new generation radar such APG-77 have this capability.
AmiGanguli
Junior Member
Somebody in Sweden did his Master's Thesis on "track-before-detect":
Interesting because it's really just a well-known statistical technique, but of course difficult because you'd have to do it in real-time.
In short, if you use a single scan to decide whether or not a target is present, then you have to decide on a threshold value. If the return signal is stronger than the threshold, then there's something there, otherwise the space is empty. Of course the threshold has to be far enough above the normal noise level that you can be sure not to get a lot of false-positives.
But, you could also base your decision on multiple scans. A "slightly high" return value in a single scan might be noise, but if you get several "slightly high" return values from the same area then you can progressively increase the probability of there really being a target there. This way you can detect targets even if the return signal is weaker than the normal background noise.
The upside: this technique would work with any given radar technology. It's not dependent on increasing the signal, or separating the receiver from the transmitter. The downside: Bayesian statistics takes a lot of computer power.
But given the rate at which computational power increases, it's likely that this technique will be used widely pretty soon, if it isn't already.
Interesting because it's really just a well-known statistical technique, but of course difficult because you'd have to do it in real-time.
In short, if you use a single scan to decide whether or not a target is present, then you have to decide on a threshold value. If the return signal is stronger than the threshold, then there's something there, otherwise the space is empty. Of course the threshold has to be far enough above the normal noise level that you can be sure not to get a lot of false-positives.
But, you could also base your decision on multiple scans. A "slightly high" return value in a single scan might be noise, but if you get several "slightly high" return values from the same area then you can progressively increase the probability of there really being a target there. This way you can detect targets even if the return signal is weaker than the normal background noise.
The upside: this technique would work with any given radar technology. It's not dependent on increasing the signal, or separating the receiver from the transmitter. The downside: Bayesian statistics takes a lot of computer power.
But given the rate at which computational power increases, it's likely that this technique will be used widely pretty soon, if it isn't already.
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