1.

INTRODUCTION

Trends in modern radioelectronic system (RES) development have resulted in increasingly complex and often contradictory requirements for these systems. Concerning radar systems, such a contradiction consists of the expectation of extended range and improved effectiveness while at the same moment ensuring noise immunity and stealth operation. Such expectations, as a rule, cause an inconsistency in the requirements with respect to the desired parameters of the emitted radio signals and achievable ranges of the RES. There are well-known approaches to partially solve the problems stated above. They consist of using radio signals with complex types of modulation [1-6], or combining single measurements from different radars [1], [7-8] in order to obtain a desired effect in a multi-position radar system (MPRS).

In this paper, an approach to solve the above contradiction is proposed. It involves using modular code binary sequences (CBSs) in radars with structures optimized by the criteria of secrecy and noise immunity [1], [4-6], but with ambiguous estimation of the radar-target distance. Additionally, the approach involves the joint processing of ambiguous measurements of each of the radars, and on this basis, getting unambiguous estimates of the target coordinates. The implementation of the proposed approach is based on the construction of a two-dimensional MPRS analytical model, using a method of nonlinear conjugation of ambiguous scales and an algorithm of ambiguity disclosure.

2.

PROBLEM FORMULATION

It is assumed that each radar operates in an active mode, emitting a continuous radio signal modulated by the optimal CBS. In general, modulators of the CBS are paired differently and are quasi-orthogonal for each pair of radars. Thus, all radars emit radio signals with quasi-orthogonal modulations, which ensures their effective identification when receiving and processing reflected radio targets. The duration of one cycle of a modulated radio signal (repetition period T_i of the modulating CBS in the radio signal of the i-th radar) should not exceed the value R_min/c, where R_min represents the minimum distance to the target; c is the free-space electromagnetic wave velocity.

The interval (scaled in distance units) of a single distance measurement of the radar is determined by the ratio d_i = T_i · c. The time of the radio signal passing from the radar to the target and back is T_pass = 2R_i/c, where R_i is the distance, and R_i/d_i = T_pass · c/(2d_i) is the relative distance from the i-th radar to the target. The receiver of the i-th radar estimates the fractional part of the relative distance R_i/d_i. The corresponding ratios can be written as follows:

where [ ]⁺ is a floor function of R_i/d_i and { }⁺ means a fractional part function of R_i/d_i;

[R_i/d_i]⁺ = K_i - unknown number of entire relative periods in the distance R_i/d_i;

{R_i/d_i}⁺ = ϕ_i - fractional part of the relative period at the distance R_i/d_i for i ∈ {1, 2,…, n}.

From (1) the following relations between the relative distance, period T_i and time interval Δt_i follow, which define the fractional part ϕ_i:

Thus, the time interval Δt_i, which can be estimated in the i-th radar using the reflected radio signal, is determined by relation (2). In order to further formulate the MPRS model, we will need to establish an analytic formula that expresses the value of ϕ_i by means of {2ϕ_i}⁺ · T_i. From expression (2), we can write the following congruence:

Formula (3) has the following solution:

The plot of ϕ_i vs Δt_i/T_i is shown in Fig. 1.

Figure 1.

Dependence of ϕ_i value on Δt_i/T_i

Equation (4) and Fig. 1 imply that Δt_i/T_i is mapped onto ϕ_i ambiguously.

The schematic representation shown in Fig. 2 is used to form an analytical two-dimensional model of the MPRS.

Figure 2.

Two-dimensional representation of MPRS

From the above, it follows that a separate i-th radar can estimate a fractional part of ϕ_i but cannot determine the whole value K_i. Thus, the problem can be solved by developing a deterministic analytical model of the MPRS and determining the conditions for unambiguous mapping of coordinates x₀, y₀ onto multiple values ϕ_i. In addition, it is necessary to determine the conditions for the unambiguous map of values ϕ_i in K_i for two selected radars to elaborate an algorithm for the disclosure of an ambiguity. Note that the term “deterministic model” used here means that the values ϕ_i are not random.

To solve this problem, the approach developed in [9-11] concerning the methods of revealing ambiguity in the linear conjugation of scales was developed.

3.

ANALYTICAL MODEL OF A MULTI-POSITION RADAR SYSTEM

From the above considerations, a deterministic analytical model of the radar can be represented in the form of a system of nonlinear congruences:

where x₀, y₀ - target coordinates; x_i, y_i - coordinates of the i-th radar. It allows the two following statements to be formulated:

Statement 1: a necessary and sufficient condition for unambiguity mapping (x₀, y₀) → ϕ_i in the system of nonlinear congruences (5) has the form of inequality:.

For two arbitrarily selected i-th and j-th radars, a system of equations can be obtained from (5) by elementary transformations:

where ΔR_0i = ϕ_id_i, ΔR_0j = ϕ_jd_j.

In general, from (5) we can write n independent equations similar in form to (6). The values K_i and K_j are unknown integer parameters, x₀, y₀ are unknown coordinates of the target, and ΔR_0i, ΔR_0j are known values. Therefore, it is necessary to express x₀, y₀ from (6) by K_i·d_i, K_j·d_j and ΔR_0i, ΔR_0j. Next, substitute x₀, y₀ to the rest of n – 2 equations that are formed from (5) and obtain n – 2 equations that contain n unknown integer values K_m for m ∈ {l,…,n}, m ≠ i, j. An essential feature of the obtained integer equations is that the integer parameters K_m enter linearly into equations with indices m. Next, for the resulting system of n–2 equations with n unknown integer variables K_m, conditions under which this system has a single solution are formulated.

Therefore, based on this consideration, when carrying out the equivalent transformations of the system (6), the following expressions for x_0i, y_0i can be obtained:

where A_i = K_i·d_i, + ΔR_0i; A_j = K_j·d_j + ΔR_0j; x_ij = x_i – x_j; y_ij = y_i –y_j; ; x_0i = x₀ – x_i; y_0i = y₀ – y_i. Substituting (7) and (8) into (6) with index m ≠ i, j and carrying out a series of transformations gives the following equation with three unknown integers K_i, K_j, K_m:

The practical significance has the variant of MPRS radar placement on a straight line which is taken as the x-axis of the coordinate system (Fig. 2). In this case, the values y_ij for i ≠ j, i ∈ {1,…,n} are equal zeroes y_ij =0, and (9) can be written after a series of transformations in the following form:

The equation (10) with integer variables K_i,K_j,K_m for i ≠ j and m ≠ i, j provides the ability to write down the system of n–2 nonlinear congruences, each containing only two integer variables K_i and K_j:

The system of congruences (11) gives the following mapping: for each fixed pair of values K_i,K_j selected from the set of integers, the set of left-hand sides of the congruence system (11) defines {(ϕ_i,ϕ_j) → (ϕ₁, ϕ₂,…, ϕ_m,…, ϕ_m)} for i ≠ j and m ≠ i, j. This map is continuous and sets the n–2 dimensional surface in a single cube, and provides a tool for forming multiple values K_i,K_j within which unambiguous mapping (x₀, y₀) ⇔ (ϕ₁, ϕ₂,…, ϕ_n) ⇔ (K_i,K_j) is ensured.

Statement 2: a necessary and sufficient condition for mutual unambiguous mapping in the form (x₀, y₀) ⇔ (ϕ₁, ϕ₂,…, ϕ_n) ⇔ (K_i,K_j) requires that there is no congruence solution:

Where ϕ_i,ϕ_j ∈ [0 ÷ 1), K_ip,K_jq ∈ Θ_i, K_ir,K_jl ∈ Θ_i and Θ_i, Θ_j are sets of K_i, K_j values within the range of unambiguity.

The system of congruences (11) gives a practical algorithm for obtaining estimates of K_i,K_j values and calculating the coordinates by the equations (7) and (8).

The sequence of the algorithm steps is as follows:

where ξ_ijm represents the left-hand sides of (11).

4.

NUMERICAL EXAMPLE

A quantitative example for a two-dimensional radar system is shown below.

(x₀,y₀) = (7;23), R₁/d₁ = 3.4345185, R₂/d₂ = 4.07074408

(x₁,y₁) = (0;0), (x₂, y₂) = (2;0), (x₃,y₃) = (5;0)

d₁ = 7.0, d₂ = 5.0, d₃ = 3.0

ϕ₁ = 0.4345185, ϕ₂ = 0.7074408, ϕ₃ = 0.6955973.

A set of permissible values of (K_i, K_j) is shown in Table 1.

Table 1.

The set of permissible pairs (K1, K2)

The results of the calculations according to criterion (13) are shown in Table 2.

Table 2.

Results of (K1,K2) calculations using criterion (13)

The calculations performed according to the developed algorithm restore the values (K_i, K_j), which unambiguously, by the formulae (7) and (8), provide the determination of coordinates (x₀, y₀).

5.

CONCLUSIONS

The paper presented a two-dimensional analytical model of a multi-position radar system with ambiguous measurements of distances from each of the radars of the system, a method of the nonlinear conjugation of radar measuring scales, and an algorithm for revealing the ambiguity of measurements of a set of radar scales. The developed algorithm provides an effective ambiguity disclosure since the dimension of the recoverable two-dimensional parameter does not depend on the number of radars in the system. An example of a two-dimensional multi-position radar system and the results of a numerical simulation of the ambiguity detection algorithm elements were given.

From the studies and the results obtained in this work, the following directions of designing multi-position radar systems can be formulated:

The obtained results provide a good basis for enabling the implementation of multi-position radar systems.