Long-baseline acoustic unambiguous navigation method based on aggregation degree likelihood joint test

Abstract

The invention relates to a long-baseline acoustic unambiguous navigation method based on polymerization degree likelihood joint inspection, and belongs to the field of long-baseline acoustic unambiguous navigation. The invention aims to solve the problems of large navigation result error and low navigation accuracy when the initial position of an underwater target is not calibrated or the calibration error is large in the existing long baseline navigation. The method is applied to a long baseline navigation system in which the initial position of an underwater target is unknown or uncredible and the signal form is not processed. According to the long-baseline acoustic navigation range ambiguity resisting method based on the aggregation degree-likelihood joint test, the unambiguous number combination truth value is solved through pruning of the multi-hypothesis maintenance at the initial moment and the aggregation degree-likelihood joint test at the subsequent moment, so that the waste of the calculated amount in the trajectory maintenance stage in navigation is avoided; when the initial position of the underwater target is not calibrated or the calibration error is large, the navigation accuracy is improved.

Description

Technical Field

[0001] The invention relates to a long-baseline acoustic unambiguous navigation method based on aggregation degree likelihood joint test, and belongs to the field of long-baseline acoustic unambiguous navigation. Background Art

[0002] Underwater target navigation technology plays a vital role in ocean development, marine scientific research, and other fields, and is a key topic in marine engineering. Underwater vehicles that need to navigate underwater for extended periods of time require long-term, high-precision self-navigation systems. Inertial navigation technology inevitably suffers from the drawback of error accumulation, causing navigation errors in vehicles using this technology to increase over time. Furthermore, due to the rapid attenuation of electromagnetic waves underwater, satellite navigation systems that utilize electromagnetic waves, such as GPS and Beidou, cannot operate in deep seas. Therefore, underwater acoustic navigation and positioning technology plays a key role.

[0003] Currently, most underwater acoustic navigation systems rely on geometric principles for acoustic positioning. Based on baseline length, they can be categorized as long baseline systems (LBL), short baseline systems (SBL), and ultra-short baseline systems (USBL). Long baseline systems have baseline lengths ranging from several hundred meters to several thousand meters, short baseline systems have baseline lengths ranging from several meters to tens of meters, and ultra-short baseline systems have baseline lengths less than or equal to half a wavelength.

[0004] Long-baseline navigation systems use acoustic triggering, and in practice operate in two modes: synchronized beaconing and interrogation-response. Array elements are arranged in a specific geometric pattern, and the target's position is calculated by measuring the distance between the target and each element. However, the measurement delay measured by the navigation system is the remainder of the acoustic signal propagation delay and the pulse synchronization period. For long-baseline navigation systems with large array element spacing and a small pulse synchronization period, the absolute acoustic signal propagation delay is greater than the pulse synchronization period. Consequently, the measurement delay will differ from the acoustic signal propagation delay by one or more pulse synchronization periods, resulting in multi-valued navigation results and, in turn, range ambiguity.

[0005] In response to the range ambiguity problem in long baseline positioning and navigation, many researchers have proposed two types of solutions based on the source of the range ambiguity problem.

[0006] The first type of signal design scheme increases the unambiguous distance of the acoustic signal by designing acoustic signal parameters that distinguish different pulse synchronization periods. However, this scheme requires the modification of the sound source and signal processing algorithm, and the technical implementation is relatively complex.

[0007] The second category involves software logic decision schemes, which utilize a series of decision logic to correct for measurement delays and identify the true solution. These schemes can be categorized into reference position methods and show-of-hand voting methods. The reference position method suppresses range ambiguity before solving, and can be considered an a priori anti-ambiguity method. The required prior information is the target's reference position. This reference position, also known as the initial position, requires a priori knowledge. Furthermore, the reference position can be automatically updated at each subsequent moment. However, this method relies heavily on the accuracy of the initial position calibration. The algorithm uses this initial position to determine the ambiguity period of each array element. If the initial position is incorrect, the correct solution cannot be obtained. The show-of-hand voting method fully utilizes archival information implicit in the system or accumulated during tracking to distinguish the true solution from a large number of ambiguous solutions. This algorithm essentially operates like an "election," exhaustively enumerating all possible solutions as trial solutions. It then uses redundant array elements or historical trajectory information to determine the true solution from among all possible trial solutions. Finally, the delay corresponding to the determined true solution serves as the input to the navigation algorithm. The voting method can be called a posteriori anti-ambiguity method because anti-ambiguity is performed after the positioning solution. However, in practical applications, there is a problem that the computational complexity increases exponentially with the maximum number of ambiguity cycles. Summary of the Invention

[0008] The purpose of the present invention is to solve the problem of large navigation result error and low navigation accuracy in the existing long baseline navigation when the initial position of the underwater target is not calibrated or the calibration error is large, and to propose a long baseline acoustic unambiguous navigation method based on the joint test of aggregation likelihood.

[0009] A long baseline acoustic unambiguous navigation method based on the joint test of aggregation likelihood is described as follows:

[0010] Step 1: Build N p A long baseline navigation system composed of array elements is used to initialize the coordinate position and synchronization period of each array element and obtain the maximum ambiguity number of the time delay;

[0011] Step 2: Initialize the frame count k=1, calculate the k-th frame tentative depolymerization degree, and obtain J′ hypothetical trajectories based on the k-th frame tentative depolymerization degree;

[0012] Step 3: Determine if J′=1. If so, go to step 6; if not, go to step 4.

[0013] Step 4: Let k = k + 1, calculate the aggregation degree and joint likelihood function of the k-th frame trial solution, and obtain Y hypothetical trajectories after pruning, Y ≤ J′;

[0014] Step 5: Determine if Y=1. If so, proceed to step 6. If not, repeat step 4.

[0015] Step 6: Let k = k + 1, estimate the unambiguous delay of the kth frame by the unambiguous number combination of the k-1th frame and the joint likelihood function, and obtain the motion trajectory of the underwater target based on the unambiguous delay of the kth frame.

[0016] Preferably, in step 1, N p The long baseline navigation system consists of array elements, initializes the coordinate position and synchronization period of each array element, and obtains the maximum ambiguity number of the delay; the specific process is:

[0017] Step 1. Build N p A long baseline navigation system composed of array elements, initializing the coordinate position pi of each array element;

[0018] p i =[x i ,y i ,z i ],i=1,2,...,N p

[0019] Among them, pi is the coordinate position of the i-th array element, [x i ,y i ,z i ] are the position coordinates of the i-th array element in the Cartesian coordinate system x, y, and z axes respectively;

[0020] The Cartesian coordinate system has an x-axis pointing due east, a y-axis pointing due north, and a z-axis perpendicular to the x and y plane pointing upwards.

[0021] Step 1 and 2: Initialize the equivalent sound speed c of the current water area and the synchronization period T of the long baseline navigation system;

[0022] Step 13: Based on the coordinate position pi of each array element, calculate the distance between the target and each array element in the current water area, and select the maximum distance and record it as R max ;

[0023] Based on the equivalent sound speed c, synchronization period T and maximum distance R of the current water area max , calculate the maximum number of fuzzy cycles N max .

[0024] Preferably, in the steps 1 and 3, based on the coordinate position p of each array element i Calculate the distance between the target and each array element in the current water area, and select the maximum distance as R max ;

[0025] Based on the equivalent sound speed c, synchronization period T and maximum distance R of the current water area max , calculate the maximum number of fuzzy cycles N max ;

[0026] The specific process is:

[0027] N max =floor(2R max / cT)

[0028] Among them, floor() is a rounding down function.

[0029] Preferably, in step 2, the frame count k=1 is initialized, the k-th frame tentative depolymerization degree is calculated, and J′ hypothetical trajectories are obtained based on the k-th frame tentative depolymerization degree. The specific process is:

[0030] Step 2.1: kth frame N p The round-trip propagation delay sequence received by each array element is T r (k); expressed as:

[0031]

[0032] in,

[0033] is the round-trip propagation delay received by the first array element in the kth frame;

[0034] is the round-trip propagation delay received by the second array element in the kth frame;

[0035] The Nth frame of the kth frame p The round-trip propagation delay received by each array element;

[0036] Step 22: Based on the kth frame N p The two-way propagation delay sequence T received by each array element r (k), calculate the unambiguous delay T of the kth frame m (k); expressed as:

[0037] T m (k) = T r (k)+N j (k)×T

[0038] in,

[0039] N j (k) is the jth unambiguous number combination in the kth frame;

[0040]

[0041] in,

[0042] n1 is the unambiguous number of the first array element, n2 is the unambiguous number of the second array element, and n i is the unambiguous number of the i-th array element, For N p The unambiguous number of array elements;

[0043] For N p Array elements participate in the solution, and each frame delay has combinations without fuzzy numbers; for the jth combination, j=1,2,…,J;

[0044] Step 2 and 3: Calculate the jth non-fuzzy number combination N j The tentative solution X j , based on the jth non-fuzzy number combination N j The tentative solution X j Calculate the tentative solution X j The area of the enclosed figure S(N j ), based on the heuristic solution X j The area of the enclosed figure S(N j ) Calculate the tentative solution X j The degree of polymerization H(X j );

[0045] Step 24: Solve the trial solution X j The degree of polymerization H(X j ) Sort from large to small and find H(X j ) is greater than the threshold Λ s The non-fuzzy number combination N j′ , no fuzzy number combination N j′ The corresponding index set is J′; it is expressed as:

[0046] H(X j′ )>Λ s

[0047] j′=1,2,...,J′

[0048] Each non-fuzzy number combination N j′ Corresponding to a trajectory, there are J′ non-fuzzy number combinations N J′ Composed of J′ hypothetical trajectories, J′ hypothetical trajectories form a group of trajectories;

[0049] No fuzzy number combination n′ i =0,1,...,N max

[0050] No fuzzy number combination N j′ Medium fuzzy period number n′ i To determine the value;

[0051] in,

[0052] n′1 is the non-fuzzy number combination N j′ The unambiguous number of the first element in the array, n′2 is the unambiguous number combination N j′The unambiguous number of the second element in the array, n′ i is a fuzzy number combination N j′ The unambiguous number of the i-th array element in, is a fuzzy number combination N j′ Nth p The unambiguous number of array elements.

[0053] Preferably, in steps 2 and 3, the jth non-fuzzy number combination N is calculated. j The tentative solution X j , based on the jth non-fuzzy number combination N j The tentative solution X j Calculate the tentative solution X j The area of the enclosed figure S(N j ), based on the heuristic solution X j The area of the enclosed figure S(N j ) Calculate the tentative solution X j The degree of polymerization H(X j ); the specific process is:

[0054] Step 231. Calculate the jth non-fuzzy number combination N j The specific process is as follows:

[0055] 1) Set the kth frame to have no blur delay T m (k) After delay correction, the one-way delay T′ is obtained m (k); expressed as:

[0056]

[0057] in,

[0058] τ is the array element transponder delay;

[0059] T m (k-1) is the unambiguous delay of the k-1th frame;

[0060] 2) Based on one-way delay T′ m (k) Obtain the jth non-fuzzy number combination N through the intersection solution method j The tentative solution X j ; The specific process is:

[0061] One-way delay T′ obtained based on delay correction m (k) and the array element position coordinate p i Construct the equation:

[0062]

[0063] in,

[0064] is the one-way propagation delay received by the first array element in the kth frame;

[0065] is the one-way propagation delay received by the second array element in the kth frame;

[0066] The Nth frame of the kth frame p One-way propagation delay received by each array element;

[0067] [x s (k),y s (k)] is the target position to be determined. If we want to get a unique solution, the above equation has Combination methods, get N s The set of solutions is denoted as X j :

[0068]

[0069] in,

[0070] (x1,y1) is the jth non-fuzzy number combination N j The first set of solutions among the trial solutions of ;

[0071] (x2,y2) is the jth non-fuzzy number combination N j The second group of solutions in the trial solution of ;

[0072] is the jth non-fuzzy number combination N j The Nth trial solution s group solution;

[0073] N s is the number of trial solutions;

[0074] Step 232: Calculate the jth non-fuzzy number combination N j The tentative solution X j The area of the enclosed figure S(N j );

[0075] Step 233: Based on X j The area of the enclosed figure S(N j ), calculate the trial solution X j The degree of polymerization H(X j ).

[0076] Preferably, in step 232, the jth non-fuzzy number combination N is calculated. j The tentative solution X j The area of the enclosed figure S(N j ); expressed as:

[0077]

[0078] in,

[0079] S(N j ) is X j The area of the enclosed figure;

[0080] (x l ,y l ) is the jth non-fuzzy number combination N j The lth group of solutions in the trial solution of ;

[0081] (x l+1 ,y l+1 ) is the jth non-fuzzy number combination N j The l+1th group of solutions in the trial solution of .

[0082] Preferably, in the steps 2, 3 and 3, based on X j The area of the enclosed figure S(N j ), calculate the trial solution X j The degree of polymerization H(X j ); expressed as:

[0083] H(X j )=S(N j ) -1 .

[0084] Preferably, in step 4, let k=k+1, calculate the aggregation degree and joint likelihood function of the k-th frame trial solution, and obtain Y hypothetical trajectories after pruning, Y≤J′; the specific process is:

[0085] Step 4.1: Calculate the trend function of the fuzzy period change of the i-th array element in the k-th frame Expressed as:

[0086]

[0087] in,

[0088] is the round-trip propagation delay received by the i-th array element in the k-th frame;

[0089] is the round-trip propagation delay received by the i-th array element in the k-1th frame;

[0090] n′ i (k) is the unambiguous number of the i-th element in the k-th frame;

[0091] n′ i (k-1) is the unambiguous number of the i-th array element in the k-1-th frame;

[0092] T is the synchronization period;

[0093] is the trend function of the fuzzy period change of the i-th array element in the k-th frame;

[0094] Step 42: Trend function based on the change of blur period of the i-th element in the k-th frame Obtain the delay-fuzzy number likelihood function of the i-th array element in the k-th frame Expressed as:

[0095]

[0096] Step 43: Delay-fuzzy number likelihood function based on the i-th element of the k-th frame Get N p The joint likelihood function L(N) of the long baseline system with two-way propagation delay of 100 elements is j′ |T r );

[0097] Step 4. For the J′ hypothesis trajectories obtained in step 2, if the degree of tentative solution of the fuzzy number combination of the j′th trajectory is greater than the threshold Λ s And the joint likelihood function is greater than the decision threshold Λ L , then keep the j'th trajectory, otherwise prune the j'th trajectory; j'=1,2,…,J′;

[0098] Until all J′ hypothesis trajectories are judged, the pruned Y hypothesis trajectories are obtained, Y ≤ J′.

[0099] Preferably, the delay-fuzzy number likelihood function of the i-th element in the k-th frame in step 43 is Get N p The joint likelihood function L(N) of the long baseline system with two-way propagation delay of 100 elements is j′ |T r ); expressed as:

[0100]

[0101] Preferably, in step 6, let k=k+1, estimate the unambiguous delay of the kth frame by combining the unambiguous numbers of the k-1th frame and the joint likelihood function, and obtain the motion trajectory of the underwater target based on the unambiguous delay of the kth frame, thereby realizing the self-navigation of the underwater target; the specific process is:

[0102] Select the joint likelihood function L(N j′ |T r )The largest non-fuzzy number combination N j′ (k) is a correct combination of non-fuzzy numbers;

[0103] Based on the correct non-fuzzy number combination N j′(k) and the round-trip propagation delay sequence T of the k-th frame r (k) Get the estimate of the unambiguous delay of the kth frame Estimation of unambiguous delay based on the kth frame Get the one-way propagation delay

[0104]

[0105] Based on one-way propagation delay Construct the equation with the array element position coordinate pi:

[0106]

[0107] in,

[0108] is the one-way propagation delay received by the first array element in the kth frame;

[0109] is the one-way propagation delay received by the second array element in the kth frame;

[0110] The Nth frame of the kth frame p One-way propagation delay received by each array element;

[0111] c is the equivalent sound speed in the current sea area;

[0112] The underwater target position [x s (k),y s (k),z s (k)], and then the motion trajectory of the underwater target is obtained, and the underwater target self-navigation is realized.

[0113] The beneficial effects of the present invention are:

[0114] This paper proposes a range ambiguity-resistant method based on a combined decision-making process of tentative solution convergence and delay likelihood testing. This method is an improvement on the hand-voting method. The likelihood function is constructed using the number of ambiguity cycles and the measured delay. Possible ambiguity cycles are screened by calculating the convergence of multiple array elements at the same ambiguity cycle number. The globally optimal ambiguity cycle number, which passes the likelihood function test, is then recorded as the unambiguous underwater acoustic signal propagation delay.

[0115] In a long-baseline navigation system where the initial position of an underwater target is unknown or unreliable and the signal form is not processed, the existing processing algorithm that relies solely on the algorithm for time delay anti-range ambiguity has the disadvantages of combinatorial explosion with the maximum number of ambiguity cycles and large amount of calculation. The long-baseline acoustic navigation anti-range ambiguity method based on the aggregation degree-likelihood joint test proposed in the present invention solves the true value of the unambiguous number combination through multi-hypothesis maintenance at the initial moment and aggregation degree-likelihood joint test pruning at subsequent moments, avoiding the waste of calculation in the trajectory maintenance stage of navigation, improving navigation accuracy when the initial position of the underwater target is not calibrated or the calibration error is large, and has better application value in real-time processing programs. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] Figure 1 is a flow chart of the method of the present invention;

[0117] Figure 2 The two-way propagation delay measurement diagram of each array element;

[0118] Figure 3 This is a schematic diagram for the calculation of the degree of depolymerization. x is the x-axis of the Cartesian coordinate system, y is the y-axis of the Cartesian coordinate system, the x-axis of the Cartesian coordinate system points due east, the y-axis points due north, and the z-axis is perpendicular to the x and y planes and points upward;

[0119] Figure 4 The distribution diagram of polymerization degree of different combinations;

[0120] Figure 5 is the trajectory map without range blur, x is the x-axis of the Cartesian coordinate system, and y is the y-axis of the Cartesian coordinate system;

[0121] Figure 6 is the anti-range ambiguity trajectory diagram of the voting method, x is the x-axis of the Cartesian coordinate system, and y is the y-axis of the Cartesian coordinate system;

[0122] Figure 7 This is the anti-range blur trajectory diagram of the present invention, where x is the x-axis of the Cartesian coordinate system, and y is the y-axis of the Cartesian coordinate system. DETAILED DESCRIPTION

[0123] Specific implementation method 1: This implementation method is a long baseline acoustic unambiguous navigation method based on the aggregation likelihood joint test. The specific process is as follows:

[0124] Step 1: Build N p A long baseline navigation system composed of array elements is used to initialize the coordinate position and synchronization period of each array element and obtain the maximum ambiguity number of the time delay;

[0125] Step 2: Initialize the frame count k=1, calculate the k-th frame tentative depolymerization degree, and obtain J′ hypothetical trajectories based on the k-th frame tentative depolymerization degree;

[0126] Step 3: Determine if J′=1. If so, go to step 6; if not, go to step 4.

[0127] Step 4: Let k = k + 1, calculate the aggregation degree and joint likelihood function of the k-th frame trial solution, and obtain Y hypothetical trajectories after pruning, Y ≤ J′;

[0128] Step 5: Determine if Y=1. If so, proceed to step 6. If not, repeat step 4.

[0129] Step 6: Let k = k + 1, estimate the unambiguous delay of the kth frame by the unambiguous number combination of the k-1th frame and the joint likelihood function, and obtain the motion trajectory of the underwater target based on the unambiguous delay of the kth frame, thereby realizing underwater target navigation.

[0130] Specific implementation method 2: This implementation method is different from the specific implementation method 1 in that the step 1 is to construct a p The long baseline navigation system consists of array elements, initializes the coordinate position and synchronization period of each array element, and obtains the maximum ambiguity number of the delay; the specific process is:

[0131] Step 1. Build N p A long baseline navigation system composed of array elements, initializing the coordinate position pi of each array element;

[0132] p i =[x i ,y i ,z i ],i=1,2,...,N p

[0133] in,

[0134] p i is the coordinate position of the ith array element, [x i ,y i ,z i ] are the position coordinates of the i-th array element in the Cartesian coordinate system x, y, and z axes respectively;

[0135] The Cartesian coordinate system has an x-axis pointing due east, a y-axis pointing due north, and a z-axis perpendicular to the x and y plane pointing upwards.

[0136] Step 1 and 2: Initialize the equivalent sound speed c of the current water area and the synchronization period T of the long baseline navigation system;

[0137] Step 13: Based on the coordinate position pi of each array element, calculate the distance between the target (the target position is known) and each array element (the position of each array element is known) in the current water area, and select the maximum distance and record it as R max ;

[0138] Based on the equivalent sound speed c, synchronization period T and maximum distance R of the current water areamax , calculate the maximum number of fuzzy cycles N max .

[0139] Other steps and parameters are the same as those in the first embodiment.

[0140] Specific embodiment three: This embodiment differs from specific embodiment one or two in that in step one or three, the distance between the target (target position is known) and each array element (each array element position is known) in the current water area is calculated based on the coordinate position pi of each array element, and the maximum distance is selected and recorded as R max ;

[0141] Based on the equivalent sound speed c, synchronization period T and maximum distance R of the current water area max , calculate the maximum number of fuzzy cycles N max ; The specific process is:

[0142] N max =floor(2R max / cT)

[0143] Among them, floor() is a rounding down function.

[0144] Other steps and parameters are the same as those in the first or second embodiment.

[0145] Specific embodiment 4: This embodiment differs from any one of specific embodiments 1 to 3 in that in step 2, the frame count k is initialized to 1, the k-th frame tentative depolymerization degree is calculated, and J′ hypothetical trajectories are obtained based on the k-th frame tentative depolymerization degree. The specific process is as follows:

[0146] Step 2.1: kth frame N p The round-trip propagation delay sequence received by each array element is T r (k) (known); expressed as:

[0147]

[0148] in,

[0149] is the round-trip propagation delay received by the first array element in the kth frame;

[0150] is the round-trip propagation delay received by the second array element in the kth frame;

[0151] The Nth frame of the kth frame p The round-trip propagation delay received by each array element;

[0152] The round-trip propagation delay is the effective value of the delay information that the target can obtain from the long baseline navigation system;

[0153] Step 2: Navigation solution requires unambiguous delay information, based on the kth frame N p The two-way propagation delay sequence T received by each array element r (k), calculate the unambiguous delay T of the kth frame m (k) (known); expressed as:

[0154] T m (k) = T r (k)+N j (k)×T

[0155] in,

[0156] N j (k) is the jth unambiguous number combination of the kth frame (unknown, required);

[0157]

[0158] in,

[0159] n1 is the unambiguous number of the first array element, n2 is the unambiguous number of the second array element, and n i is the unambiguous number of the i-th array element, For N p The unambiguous number of array elements;

[0160] For N p Array elements participate in the solution, and each frame delay has combinations without fuzzy numbers; for the jth combination, j=1,2,…,J;

[0161] Step 2 and 3: Calculate the jth non-fuzzy number combination N j The tentative solution X j , based on the jth non-fuzzy number combination N j The tentative solution X j Calculate the tentative solution X j The area of the enclosed figure S(N j ), based on the heuristic solution X j The area of the enclosed figure S(N j ) Calculate the tentative solution X j The degree of polymerization H(X j );

[0162] Step 24: Solve the trial solution X j The degree of polymerization H(X j ) Sort from large to small and find H(X j ) is greater than the threshold Λ s The non-fuzzy number combination N j′ (Satisfy H(X j ) is greater than the threshold Λs The non-fuzzy number combination N j′ There are 1 or more), no fuzzy number combination N j′ The corresponding index set is J′; it is expressed as:

[0163] H(X j′ )>Λ s

[0164] j′=1,2,...,J′

[0165] Each non-fuzzy number combination N j′ Corresponding to a trajectory, there are J′ non-fuzzy number combinations N J′ Composed of J′ hypothetical trajectories, J′ hypothetical trajectories form a group of trajectories;

[0166] No fuzzy number combination

[0167] No fuzzy number combination N j′ Medium fuzzy period number n' i To determine the value;

[0168] in,

[0169] n'1 is the non-fuzzy number combination N j＇ The unambiguous number of the first element in the array, n′2 is the unambiguous number combination N j′ The unambiguous number of the second element in the array, n' i is a fuzzy number combination N j＇ The unambiguous number of the i-th array element in, is a fuzzy number combination N j＇ Nth p The unambiguous number of array elements.

[0170] The other steps and parameters are the same as those in the first to third embodiments.

[0171] Specific embodiment 5: This embodiment differs from the specific embodiments 1 to 4 in that the jth non-fuzzy number combination N is calculated in steps 2 and 3. j The tentative solution X j , based on the jth non-fuzzy number combination N j The tentative solution X j Calculate the tentative solution X j The area of the enclosed figure S(N j ), based on the heuristic solution X j The area of the enclosed figure S(N j ) Calculate the tentative solution X j The degree of polymerization H(X j );

[0172] The specific process is:

[0173] Step 231. Calculate the jth non-fuzzy number combination N j The specific process is as follows:

[0174] 1) Set the kth frame to have no blur delay T m (k) After delay correction, the one-way delay T' is obtained m (k); expressed as:

[0175]

[0176] in,

[0177] τ is the array element transponder delay, which is known a priori when the array elements are deployed;

[0178] T m (k-1) is the unambiguous delay of the k-1th frame;

[0179] 2) Based on one-way delay T' m (k) Obtain the jth non-fuzzy number combination N through the intersection solution method j The tentative solution X j ; The specific process is:

[0180] One-way delay T' obtained based on delay correction m (k) and the array element position coordinate p i Construct the equation:

[0181]

[0182] in,

[0183] is the one-way propagation delay received by the first array element in the kth frame (the one-way delay T' obtained by delay correction m (k));

[0184] is the one-way propagation delay received by the second array element in the kth frame (the one-way delay T' obtained by delay correction m (k));

[0185] The Nth frame of the kth frame p The one-way propagation delay received by each array element (the one-way delay T' obtained by delay correction) m (k));

[0186] [x s (k),y s (k),z s (k)] is the target position to be sought, where z s(k) can be read from the gas carried by the target itself and is known a priori; if a unique solution is to be obtained, the above equation has Combination methods, get N s The set of solutions is denoted as X j :

[0187]

[0188] in,

[0189] (x1,y1) is the jth non-fuzzy number combination N j The first set of solutions among the trial solutions of ;

[0190] (x2,y2) is the jth non-fuzzy number combination N j The second group of solutions in the trial solution of ;

[0191] is the jth non-fuzzy number combination N j The Nth trial solution s group solution;

[0192] N s is the number of trial solutions, such as N p =4, If N p >4,

[0193] Step 232: Calculate the jth non-fuzzy number combination N j The tentative solution X j The area of the enclosed figure S(N j );

[0194] Step 233: Based on X j The area of the enclosed figure S(N j ), calculate the trial solution X j The degree of polymerization H(X j ).

[0195] The other steps and parameters are the same as those in the first to fourth embodiments.

[0196] Specific embodiment 6: The difference between this embodiment and specific embodiments 1 to 5 is that the j-th non-fuzzy number combination N is calculated in step 232. j The tentative solution X j The area of the enclosed figure S(N j ); expressed as:

[0197]

[0198] in,

[0199] S(Nj ) is X j The area of the enclosed figure;

[0200] (x l ,y l ) is the jth non-fuzzy number combination N j The lth group of solutions in the trial solution of ;

[0201] (x l+1 ,y l+1 ) is the jth non-fuzzy number combination N j The l+1th group of solutions in the trial solution of .

[0202] The other steps and parameters are the same as those in the first to fifth embodiments.

[0203] Specific embodiment seven: This embodiment differs from the specific embodiments one to six in that the steps two, three and three are based on X j The area of the enclosed figure S(N j ), calculate the trial solution X j The degree of polymerization H(X j ); expressed as:

[0204] H(X j )=S(N j ) -1

[0205] Use H(X j ) reflects the degree of aggregation of the trial solution. If H(X j ) is larger, the higher the degree of aggregation of this set of trial solutions is, otherwise the lower the degree of aggregation is.

[0206] The other steps and parameters are the same as those in the first to sixth embodiments.

[0207] Specific embodiment eight: This embodiment differs from any one of specific embodiments one to seven in that, in step four, k=k+1 is set, the degree of aggregation and joint likelihood function of the k-th frame trial solution are calculated, and Y hypothetical trajectories after pruning are obtained, where Y≤J′; the specific process is as follows:

[0208] Step 4.1: Calculate the trend function of the fuzzy period change of the i-th array element in the k-th frame Expressed as:

[0209]

[0210] in,

[0211] is the round-trip propagation delay received by the i-th array element in the k-th frame;

[0212] is the round-trip propagation delay received by the i-th array element in the k-1th frame;

[0213] n' i (k) is the unambiguous number of the i-th element in the k-th frame;

[0214] n' i (k-1) is the unambiguous number of the i-th array element in the k-1-th frame;

[0215] T is the synchronization period;

[0216] is the trend function of the fuzzy period change of the i-th array element in the k-th frame;

[0217] Step 42: Trend function based on the change of blur period of the i-th element in the k-th frame Obtain the delay-fuzzy number likelihood function of the i-th array element in the k-th frame Expressed as:

[0218]

[0219] The delay-fuzzy number likelihood function of the k-th frame element i Indicates the i-th array element at the given current delay measurement In the case of the current unfuzzy number n' i (k) the possibility of taking values;

[0220] Step 43: Delay-fuzzy number likelihood function based on the i-th element of the k-th frame Get N p The joint likelihood function L(N) of the long baseline system with two-way propagation delay of 100 elements is j′ |T r )(the joint likelihood function is a number);

[0221] Step 4. For the J′ hypothesis trajectories obtained in step 2, if the degree of tentative solution of the fuzzy number combination of the j′th trajectory is greater than the threshold Λ s And the joint likelihood function is greater than the decision threshold Λ L , then keep the j′th trajectory, otherwise prune the j′th trajectory; threshold Λ s , threshold Λ L Artificial setting; j′=1,2,…,J′;

[0222] Until all J′ hypothesis trajectories are judged, the pruned Y hypothesis trajectories are obtained, Y ≤ J′.

[0223] The other steps and parameters are the same as those in the first to seventh embodiments.

[0224] Specific embodiment nine: This embodiment differs from any one of specific embodiments one to eight in that the delay-fuzzy number likelihood function based on the i-th array element of the k-th frame in step four-three is Get N p The joint likelihood function L(N) of the long baseline system with two-way propagation delay of 100 elements is j′ |T r )(joint likelihood function is a number); expressed as:

[0225]

[0226] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0227] Specific embodiment 10: This embodiment differs from any one of specific embodiments 1 to 9 in that, in step 6, k=k+1 is set, and the unambiguous delay of the kth frame is estimated by the unambiguous number combination of the k-1th frame and the joint likelihood function. The motion trajectory of the underwater target is obtained based on the unambiguous delay of the kth frame, thereby realizing underwater target self-navigation (jumping to step 6 only leaves one solution group); the specific process is as follows:

[0228] Select the joint likelihood function L(N j′ |T r )The largest non-fuzzy number combination N j′ (k) is a correct combination of non-fuzzy numbers;

[0229] Based on the correct non-fuzzy number combination N j′ (k) and the round-trip propagation delay sequence T of the k-th frame r (k) Get the estimate of the unambiguous delay of the kth frame Estimation of unambiguous delay based on the kth frame Get the one-way propagation delay

[0230]

[0231] Based on one-way propagation delay Construct the equation with the array element position coordinate pi:

[0232]

[0233] in,

[0234] is the one-way propagation delay received by the first element in the kth frame (one-way propagation delay in);

[0235] is the one-way propagation delay received by the second array element in the kth frame (one-way propagation delay in);

[0236] The Nth frame of the kth frame p One-way propagation delay received by each array element (one-way propagation delay in);

[0237] c is the equivalent sound speed in the current sea area;

[0238] The underwater target position [x s (k),y s (k),z s (k)], and then the motion trajectory of the underwater target is obtained, and the underwater target self-navigation is realized.

[0239] The other steps and parameters are the same as those in Specific Embodiments 1 to 9.

[0240] Example:

[0241] To verify the anti-range ambiguity performance of the present invention in long-baseline underwater target navigation, it is assumed that the interrogation-response working mode is adopted, the underwater target moves in a uniform linear motion, and the acoustic beacon carried by the underwater target transmits the interrogation signal with a period of 2s. The round-trip propagation delay of each array element is measured as follows: Figure 2 shown.

[0242] The time delay measured by the simulation target is subjected to anti-distance ambiguity using the aggregation degree-likelihood joint test anti-distance ambiguity method (the present invention) and the traditional voting algorithm. Figure 3 Schematic diagram of tentative depolymerization degree calculation in the present invention; Figure 4 This is a distribution diagram of different unambiguous number combinations proposed by this method and the final adopted combinations. Different numbers in the figure represent the index of different combination numbers.

[0243] Without anti-range blurring, all possible solution trajectories are as follows Figure 5 As shown in the figure, the traditional voting method relies only on the time delay of redundant array elements to judge the ambiguity solution. The computational complexity is large and it is easy to make wrong decisions during the whole navigation cycle. Figure 6 As shown in the figure, the number of ambiguity cycles is misjudged at multiple moments, resulting in a decrease in the navigation performance of the algorithm. The anti-ambiguity trajectory of the long baseline acoustic unambiguous navigation method based on the joint test of the aggregation likelihood proposed in this invention is as follows: Figure 7 As shown in the figure, this method can correctly maintain the navigation trajectory after iteratively obtaining the correct anti-ambiguity combination number, and can improve the navigation performance of the long baseline real-time navigation system.

[0244] The present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A long-baseline acoustic unambiguous navigation method based on a joint test of aggregation likelihood, characterized by: The specific process of the method is: Step 1: Build N p A long baseline navigation system composed of array elements is used to initialize the coordinate position and synchronization period of each array element and obtain the maximum ambiguity number of the time delay; Step 2: Initialize the frame count k=1, calculate the k-th frame tentative depolymerization degree, and obtain J′ hypothetical trajectories based on the k-th frame tentative depolymerization degree; Step 3: Determine if J′=1. If so, go to step 6; if not, go to step 4. Step 4: Let k = k + 1, calculate the aggregation degree and joint likelihood function of the k-th frame trial solution, and obtain Y hypothetical trajectories after pruning, Y ≤ J′; Step 5: Determine if Y=1. If so, proceed to step 6. If not, repeat step 4. Step 6: Let k = k + 1, estimate the unambiguous delay of the kth frame by the unambiguous number combination of the k-1th frame and the joint likelihood function, and obtain the motion trajectory of the underwater target based on the unambiguous delay of the kth frame.

2. The long baseline acoustic unambiguous navigation method based on the joint test of aggregation likelihood according to claim 1 is characterized by: In step 1, N p The long baseline navigation system consists of array elements, initializes the coordinate position and synchronization period of each array element, and obtains the maximum ambiguity number of the delay; the specific process is: Step 1. Build N p The long baseline navigation system consists of array elements, and the coordinate position of each array element is initialized. i ; p i =[x i ,y i ,z i ],i=1,2,...,N p Among them, p i is the coordinate position of the ith array element, [x i ,y i ,z i ] are the position coordinates of the i-th array element in the Cartesian coordinate system x, y, and z axes respectively; The Cartesian coordinate system has an x-axis pointing due east, a y-axis pointing due north, and a z-axis perpendicular to the x and y plane pointing upwards. Step 1 and 2: Initialize the equivalent sound speed c of the current water area and the synchronization period T of the long baseline navigation system; Step 13: Based on the coordinate position p of each array element i Calculate the distance between the target and each array element in the current water area, and select the maximum distance as R max ; Based on the equivalent sound speed c, synchronization period T and maximum distance R of the current water area max , calculate the maximum number of fuzzy cycles N max .

3. The long baseline acoustic unambiguous navigation method based on the joint test of aggregation likelihood according to claim 2 is characterized by: In the steps 1 and 3, based on the coordinate position p of each array element i Calculate the distance between the target and each array element in the current water area, and select the maximum distance as R max ; Based on the equivalent sound speed c, synchronization period T and maximum distance R of the current water area max , calculate the maximum number of fuzzy cycles N max ; The specific process is: N max =floor(2R max / cT) Among them, floor() is a rounding down function.

4. The long baseline acoustic unambiguous navigation method based on the joint test of aggregation likelihood according to claim 3 is characterized by: In step 2, the frame count k=1 is initialized, the k-th frame tentative depolymerization degree is calculated, and J′ hypothetical trajectories are obtained based on the k-th frame tentative depolymerization degree. The specific process is as follows: Step 2.

1. kth frame N p The round-trip propagation delay sequence received by each array element is T r (k); expressed as: in, is the round-trip propagation delay received by the first array element in the kth frame; is the round-trip propagation delay received by the second array element in the kth frame; The Nth frame of the kth frame p The round-trip propagation delay received by each array element; Step 22: Based on the kth frame N p The two-way propagation delay sequence T received by each array element r (k), calculate the unambiguous delay T of the kth frame m (k); expressed as: T m (k)=T r (k)+N j (k)×T in, N j (k) is the jth unambiguous number combination in the kth frame; in, n1 is the unambiguous number of the first array element, n2 is the unambiguous number of the second array element, and n i is the unambiguous number of the i-th array element, For Nth p The unambiguous number of array elements; For N p Array elements participate in the solution, and each frame delay has combinations without fuzzy numbers; for the jth combination, j=1,2,…,J; Step 2 and 3: Calculate the jth non-fuzzy number combination N j The tentative solution X j , based on the jth non-fuzzy number combination N j The tentative solution X j Calculate the tentative solution X j The area of the enclosed figure S(N j ), based on the heuristic solution X j The area of the enclosed figure S(N j ) Calculate the tentative solution X j The degree of polymerization H(X j ); Step 24: Solve the trial solution X j The degree of polymerization H(X j ) Sort from large to small and find H(X j ) is greater than the threshold Λ s The non-fuzzy number combination N j′ , no fuzzy number combination N j′ The corresponding index set is J′; it is expressed as: H(X j′ )>L s j′=1,2,...,J′ Each non-fuzzy number combination N j′ Corresponding to a trajectory, there are J′ non-fuzzy number combinations N J′ Composed of J′ hypothetical trajectories, J′ hypothetical trajectories form a group of trajectories; No fuzzy number combination No fuzzy number combination N j′ Medium fuzzy period number n′ i To determine the value; in, n′1 is the non-fuzzy number combination N j′ The unambiguous number of the first element in the array, n′2 is the unambiguous number combination N j′ The unambiguous number of the second element in the array, n′ i is a fuzzy number combination N j′ The unambiguous number of the i-th array element in, is a fuzzy number combination N j′ Nth p The unambiguous number of array elements.

5. The long baseline acoustic unambiguous navigation method based on the joint test of aggregation likelihood according to claim 4 is characterized by: In the steps 2 and 3, the jth non-fuzzy number combination N is calculated. j The tentative solution X j , based on the jth non-fuzzy number combination N j The tentative solution X j Calculate the tentative solution X j The area of the enclosed figure S(N j ), based on the heuristic solution X j The area of the enclosed figure S(N j ) Calculate the tentative solution X j The degree of polymerization H(X j ); the specific process is: Step 231. Calculate the jth non-fuzzy number combination N j The specific process is as follows: 1) Set the kth frame to have no blur delay T m (k) After delay correction, the one-way delay T′ is obtained m (k); expressed as: in, τ is the array transponder delay; T m (k-1) is the unambiguous delay of the k-1th frame; 2) Based on one-way delay T′ m (k) Obtain the jth non-fuzzy number combination N through the intersection solution method j The tentative solution X j ; The specific process is: One-way delay T′ obtained based on delay correction m (k) and the array element position coordinate p i Construct the equation: in, is the one-way propagation delay received by the first array element in the kth frame; is the one-way propagation delay received by the second array element in the kth frame; The Nth frame of the kth frame p One-way propagation delay received by each array element; [x s (k),y s (k)] is the target position to be determined. If we want to get a unique solution, the above equation has Combination methods, get N s The set of solutions is denoted as X j : in, (x1,y1) is the jth non-fuzzy number combination N j The first set of solutions among the trial solutions of ; (x2,y2) is the jth non-fuzzy number combination N j The second group of solutions in the trial solution of ; is the jth non-fuzzy number combination N j The Nth trial solution s group solution; N s is the number of trial solutions; Step 232: Calculate the jth non-fuzzy number combination N j The tentative solution X j The area of the enclosed figure S(N j ); Step 233: Based on X j The area of the enclosed figure S(N j ), calculate the trial solution X j The degree of polymerization H(X j ).

6. The long baseline acoustic unambiguous navigation method based on the joint test of aggregation likelihood according to claim 5 is characterized by: In step 232, the jth non-fuzzy number combination N is calculated. j The tentative solution X j The area of the enclosed figure S(N j ); expressed as: in, S(N j ) is X j The area of the enclosed figure; (x l ,y l ) is the jth non-fuzzy number combination N j The lth group of solutions in the trial solution of ; (x l+1 ,y l+1 ) is the jth non-fuzzy number combination N j The l+1th group of solutions in the trial solution of .

7. The long baseline acoustic unambiguous navigation method based on the joint test of aggregation likelihood according to claim 6 is characterized by: In the steps 2, 3 and 3, based on X j The area of the enclosed figure S(N j ), calculate the trial solution X j The degree of polymerization H(X j ); expressed as: H(X j )=S(N j ) -1 。 8. The long baseline acoustic unambiguous navigation method based on the joint test of aggregation likelihood according to claim 7 is characterized by: In step 4, let k=k+1, calculate the aggregation degree and joint likelihood function of the k-th frame trial solution, and obtain Y hypothetical trajectories after pruning, Y≤J′; the specific process is: Step 4.1: Calculate the trend function of the fuzzy period change of the i-th array element in the k-th frame Expressed as: in, is the round-trip propagation delay received by the i-th array element in the k-th frame; is the round-trip propagation delay received by the i-th array element in the k-1th frame; n′ i (k) is the unambiguous number of the i-th element in the k-th frame; n′ i (k-1) is the unambiguous number of the i-th array element in the k-1-th frame; T is the synchronization period; is the trend function of the fuzzy period change of the i-th array element in the k-th frame; Step 42: Trend function based on the change of blur period of the i-th element in the k-th frame Obtain the delay-fuzzy number likelihood function of the i-th array element in the k-th frame Expressed as: Step 43: Delay-fuzzy number likelihood function based on the i-th element of the k-th frame Get N p The joint likelihood function L(N) of the long baseline system with two-way propagation delay of 100 elements is j′ |T r ); Step 4. For the J′ hypothesis trajectories obtained in step 2, if the degree of tentative solution of the fuzzy number combination of the j′th trajectory is greater than the threshold Λ s And the joint likelihood function is greater than the decision threshold Λ L , then keep the j′th trajectory, otherwise prune the j′th trajectory; j′=1,2,…,J′; Until all J′ hypothesis trajectories are judged, the pruned Y hypothesis trajectories are obtained, Y ≤ J′.

9. The long baseline acoustic unambiguous navigation method based on the joint test of aggregation likelihood according to claim 8 is characterized by: The delay-fuzzy number likelihood function based on the i-th array element of the k-th frame in step 43 is Get N p The joint likelihood function L(N) of the long baseline system with two-way propagation delay of 100 elements is j′ |T r ); expressed as:

10. The long baseline acoustic unambiguous navigation method based on the joint test of aggregation likelihood according to claim 9, characterized in that: In step 6, let k=k+1, and estimate the unambiguous delay of the kth frame by combining the unambiguous number of the k-1th frame and the joint likelihood function. Based on the unambiguous delay of the kth frame, the motion trajectory of the underwater target is obtained, thereby realizing the self-navigation of the underwater target. The specific process is as follows: Select the joint likelihood function L(N j′ |T r )The largest non-fuzzy number combination N j′ (k) is a correct combination of non-fuzzy numbers; Based on the correct non-fuzzy number combination N j′ (k) and the round-trip propagation delay sequence T of the kth frame r (k) Get the estimate of the unambiguous delay of the kth frame Estimation of unambiguous delay based on the kth frame Get the one-way propagation delay Based on one-way propagation delay and the array element position coordinate p i Construct the equation: in, is the one-way propagation delay received by the first array element in the kth frame; is the one-way propagation delay received by the second array element in the kth frame; The Nth frame of the kth frame p One-way propagation delay received by each array element; c is the equivalent sound speed in the current sea area; The underwater target position [x s (k),y s (k),z s (k)], and then the motion trajectory of the underwater target is obtained, and the underwater target self-navigation is realized.

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