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This invention is in the field of radar signal processing. People use an array of radars to obtain the information of location and vector velocity of objects they want to detect. By using multiple antennas located in a compact area, the bearing of objects can be extracted from the phase and amplitude differences of signals received by each antenna. This is the prerequisite of Direction Finding (DF) algorithms. A DF algorithm is a process executed by computers in order to estimate the bearing of targets detected by radars. The bearing is usually called Degree of Arrival (DOA). DOA estimation capability is the key concern in array design and signal processing. There are two categories of DOA estimation techniques, Beam Forming (BF) and Direction Finding (DF). The BF techniques are widely applied in radar systems where analog or digital controlled transmitting/receiving beams are steered to find targets' bearings. J. Capon in 1969 proposed a BF algorithm called Minimum Variance Method (MVM), in which an adaptive beam is formed to minimize the power output of the array while keep the array response to the designated direction as constant. This method is considered as the first to obtain higher resolution than Rayleigh resolution limit of an array. The accuracy of BF detecting results depends on array aperture size, and higher accuracy requires larger array deployment. In cases of limited space for array occupation, DF techniques are applied to estimate DOAs for sparsely distributed targets. DF algorithms exploit the phase and amplitude differences of signals received by each sonar in an aperture limited array. A simple and conventional method of DF technique is to use two orthogonal loop sonars, taking the ratio of signals received by each, then using the arctangent function to extract the single target's DOA. It often fails when there exist more than one targets or when sonar patterns are heavily distorted. Many studies have been carried out on sub-space based DF methods like MUltiple SIgnal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), etc. They have higher angular resolution than conventional BF methods. MUSIC algorithm by Schmidt applies eigenspace method to the correlation matrix of array signals, so that N sonars can generate up to N−1 signal bearings and a noise signal. It outperforms conventional DOA algorithms since it takes advantage of the orthogonality between signal subspaces and those of noises. An important prerequisite of MUSIC is that the number of targets should be known in advance, which is also the main drawback of MUSIC. By building a relation between Vandermonde matrix of sinusoids and covariance matrix of measured data, ESPRIT derives a matrix containing rotational information with respect to DOA information of targets, then they can be obtained directly from immediate matrix calculations.
The methods mentioned above are suitable for fixed-located sonars in an array. Modern Doppler radars adopt coherent cumulation to obtain spectra of moving targets. The coherent cumulative time lasts from seconds to hundreds of seconds depending on the stationarity of the target's echoes. This letter tries to utilize the cumulative time as a key factor in array signal processing to estimate DOA information, i.e., by letting the sonar in an array “moving” in deliberate manners, we can expand the DOA estimation problem from pure space domain to time-space domain. The additional Doppler shifts caused by the sonar movement would help enhance the accuracy of DOA estimation.
There are real-life examples of arrays of moving sonars, but constructing such an array costs much more than stationary sonars and is hard to achieve. This invention presents a technique called Braided Array Sampling via an Inter-Channel Scheme (BASICS) that can randomly braid inter-channel signals among stationary sonars in order to generate the same effect as moving sonar. By this means, BASICS can take the advantage of the high accuracy of DOA estimation of the moving sonars without actually building them.
This invention is in the field of radar signal processing. This invention presents a technique called Braided Array Sampling via an Inter-Channel Scheme (BASICS) that can randomly braid inter-channel signals among stationary sonars in order to generate the same effect as moving sonar. By this means, BASICS can take the advantage of the high accuracy of DOA estimation of the moving sonars without actually building them. This invention presents the principle of BASICS and its theoretical supports, as well as the basic conditions to apply BASICS.
Given that P narrow-band far-field echo signals impinge on an M-sonar array from directions θ=[θ1, θ2, . . . , θP]T, where (·)T represents the transpose operation. The array output vector X(t) is described by
X(t)=AS(t)+N(t),t=1,2, . . . ,L (1)
and S(t)=[s1(t), s2(t), . . . , sP(t)]T denotes the signal vector with zero mean. N(t) represents the additive noise vector with zero mean and σn
A is the array manifold matrix, which is given by
A=[a(θ1),a(θ2), . . . ,a(θp), . . . ,a(θP)] (2)
where
a(θp)=[ejω
and ω0 is the center angular frequency of the signals, kp is the wave vector of pth signal and rm is the radial vector from the first sonar to the mth sonar, and
in which λ0 denotes the center wavelength of the signals. (xm, ym) are co-ordinates of the mth sonar and dmp is the spatial distance between the mth sonar and the first sonar in the direction of the pth signal.
In traditional array signal processing problems, the sonars are immobile and the ejω
In Doppler radar system, a Discrete Fourier Transform (DFT) or Fast Fourier Transform (FFT) operation is often imposed on the output time series of each sonar channel to obtain the Doppler spectra of received echoes. For the signal model of (1), its Doppler spectra is expressed as
X(ωd)=AS(ωd)+N(ωd) (5)
where ωd is the echo's Doppler shift from ω0, and the manifold matrix A keeps the same form as in (2) but with the ejω
Now suppose another case where there is only one moving sonar that receiving echoes from those P narrow-band far-field targets. The sonar's output x(t) is
x(t)=Σp=1Pej[ω
where r(t) is the radial vector from the co-ordinates' origin to the sonar position at moment t.
For the signal model of (6), after the DFT/FFT operation the output Doppler spectra will be the convolution between sp(ωd) and [e−jk
x(ω′d)=Σp=1P{[e−jk
where * denotes convolution operation, and ω′d stands for the combined Doppler shift containing contributions both from echo's ωd and the equivalent Doppler shift caused by sonar movement. Equation (7) suggests that if the sonar are designed to move along R different routines, then we will obtain R possibly different Doppler spectra, from which the DOA information in kp may be extracted in a definite manner by solving a system of linear equations, for R can be designed to be much larger than the sonar number Min equation (1).
Now we manage to generate virtual movements of an sonar from an array of immobile sonars, so that the DOA estimation advantages described in equation (7) can be exploited. Because a radar system samples signals at discrete moments, a moving sonar appears at different locations at different sampling moments. When two consecutive samples appear, one cannot tell whether they are the outputs from two separately located immobile sonars at consecutive sampling moments, or they are the outputs of a moving sonar which locates just exactly at positions of that two sonars at the consecutive moments. Thus we can generate equivalent moving sonar samplings from those of an array of immobile sonars.
In practice, there are not as many as ML braided sampling routines that can be used for DOA estimation. Because of the existence of noises, many slightly different routines makes almost no significant differences in their Doppler spectra and cannot be of help in calculations. The most important reason is the constraint cast by Nyquist-Shannon Sampling Theorem, the combination of virtual movement velocity and the target's real velocity should not exceed the allowed maximum velocity corresponding to the sample interval time.
If no movement is assumed on sonar, then by Nyquist-Shannon Sampling Theorem, we only need to make sure that the Doppler Shift of targets, ωd, is less than fs/4π, where fs is the radar sample rate. However, since the sonar is “moving” in our case, we have to consider to enhance fs to satisfy Nyquist-Shannon Sampling Theorem.
Note that
where
is the mean velocity at moment t averaged from sample start, and its direction is from co-ordinate origin pointing to the sonar's current position. The kp·
x(ω′d)=Σp=1P[fp(ωv)*sp(ωd)]|ω′
From (8) ωv can be positive or negative, suppose its range is [−ωvmax, ωvmax]. And target's Doppler range is [0, ωdmax]. According to definition of convolution, the od in (7) and (9) should be within the range of [−ωvmax, ωvmax+ωdmax]. The Nyquist-Shannon Sampling Theorem requires that the fs of a real number sampling should satisfy
2πfs>2(2ωvmax+ωdmax) (10)
In our scheme of engendering virtual movement from immobile sonars array, enhancing fs means transition interval between sonars is reduced and the ωvmax may also enhances as well. To keep ωvmax not enhance as fs does, the virtual routines as shown in
The above constraints are not so harassing because in (8) as t becomes larger and larger, the ωv from virtual movement will decrease linearly. The largest ωvs normally come from initial steps of virtual movement.
As discussed in the above sections, BASICS obtain DOA information in Doppler domain. A L points time series corresponds to the same point Doppler spectra. For an M-element array, if we design R different virtual routines, we can get RL linear equations from (9). The unknowns of the equation system are Doppler spectra in every bearing cell. If bearing cells number is D, Doppler spectra point number in every direction cell is L′(L′ normally less than L as discussed in the context of formula (10)), then the total unknowns number is DL′, which is much less than RL. By solving the equation system, we will obtain the Doppler spectra result in every bearing cell, and directly get the DOA information of possible targets.
To sum up, BASICS can be addressed as the following process:
1. Randomly generate an ordered list of M elements {i1, i2, . . . , il, . . . , iL}, with each element be in {rm|m∈{1, 2, . . . , M}}. Each list represents a virtual movement of a single sonar such that it is at ri
2. Check if this movement satisfies the requirement given in inequality (10).
3. If false, regenerate a list; if true, make a time series of L samples corresponding to this movement.
4. Do DFT/FFT on the time series in 3, generating a spectra consists of L points, which corresponds to the left side of equation (9).
5. Discretizing the integral at the right side of equation (9) (by dividing the radar bearing scope into D cells, and L′ unknown spectra points in each cell), to get L linear equations for this movement.
6. Repeat Steps 1-5 to generate more groups of equations with L equations in each group, until group number R reaches ten or a hundred times than M.
7. Solve these RL equations to obtain the Doppler spectra result in every bearing cell.
In the above sections, we introduce the principles of BASICS by engendering virtual movement from actually immobile sonars, that is a kind of frequency modulation, as described in
x(t)=Σp=1PA(θ,t)ej[ω
and
x(ω′d)=Σp=1P{[A(θ,t)e−jk
where A(θ, t) depicts the variation of sonar pattern's changing with bearing and time. For this model, the above stated BASICS principle and algorithm can also be applied.
Provisional patent: 63/124,804, A Novel Direction-Finding Technique in Radar Array Signal Processing