Time alignment method used for single or double base complex high-frequency radar nets
A technology of time alignment and radar network, which is applied in the field of hyperspectral remote sensing data simulation and data fusion, can solve problems such as the reduction of kill zone, the reduction of detection accuracy and power, and the decline of combat effectiveness of air defense weapon systems
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specific Embodiment approach 1
[0013] Specific implementation mode one: combine figure 1 Describe this embodiment, the steps of this embodiment are as follows:
[0014] Step 1: Set simulation parameters; establish a Cartesian coordinate system with the first base station T as the coordinate origin, and set the distance between the first base station T and the second base station R as L, that is, the coordinates of the two stations are: T(0,0) , R(L, 0); then set the moving target P, and set the initial position of the moving target P as P(L, L), and the moving target P is parallel to the first base station T and the second base station R at the speed of vm / s The straight line where it is located moves horizontally; the first sensor A in the first base station T collects data with x seconds as the collection period, and the second sensor B of the second base station R takes y seconds as the collection period to collect data;
[0015] Step 2: Carry out simulation according to the simulation parameters in ste...
specific Embodiment approach 2
[0017] Specific implementation mode two: combination figure 1 , figure 2 and image 3 Describe this embodiment, the difference between this embodiment and specific embodiment 1 is that the curve fitting in step 3 is a spline fitting based on the least squares method, and the spline function is essentially a piecewise polynomial function. A sensor A measures the target n+1 times in a certain observation sampling time period [a, b], and divides the entire observation sampling time interval according to the sampling time: a=t 0 1 n = b, given time point t i The corresponding observed value is: f(t i )=yi(i=0,1,...,n), construct a cubic spline interpolation function s(x), and make it satisfy the following conditions: s(t i )=y i , i=0, 1,..., n; s(t) in each small interval [t i , t i +1] is a cubic polynomial, i=0, 1, ..., n-1; s(t) has a second-order continuous derivative on [a, b];
[0018] The steps to make the cubic spline interpolation function s(x) satisfy the above...
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