Object Location Method Based on Constraint Information

Abstract

The invention discloses a constraint information-based target positioning method. The constraint information-based target positioning method is used for tracking and positioning maneuvering targets in real time and comprises the following steps of, firstly, performing constraint model modeling; secondly performing system dimension reduction and correlating measured values with new variables; thirdly; obtained measurement information and designing a recursive filter; fourthly, reconstructing the current estimated values of state values and error covariance; fifthly, repeating the steps above to track and position the maneuvering targets in real time. According to the constraint information-based target positioning method, by means of system dimension reduction and correlation between the measured values and the new variables, original mathematical models and computation complexity can be reduced, obtaining optimal estimated effects can be facilitated, and the positioning precision can be improved; by designing the recursive filter and determining the current estimated values of the new variables and the error covariance according to the estimated values of the new variables and the error covariance on a last moment as well as the current values of the measure values, real-time tracking and positioning of the maneuvering targets can be effectively achieved, meanwhile, previous measured values and constraint information do not need to be retained, so that the storage space can be saved.

Description

technical field [0001] The invention relates to the technical field of target tracking, in particular to a target positioning method based on constraint information. Background technique [0002] Maneuvering target tracking is a multidisciplinary technology. Target positioning refers to the process of estimating and predicting the operating state of a target using target observation data sets from several sensors. With the continuous advancement of science and technology, there are more and more types of maneuvering targets, and their running speeds are getting faster and faster. However, it is difficult to achieve fast tracking and precise positioning only by the output of sensors. Therefore, it is necessary to make full use of all useful information to improve the accuracy of target positioning. precision. [0003] In fact, useful prior information can often be mined by analyzing the environment and trajectory of the target, and the rational use of prior information can a...

AI Technical Summary

Problems solved by technology

In the article "Constrained linear state estimation—a moving horizon approach" (Automatica, 2001, 37, pp1619–1628), C. Rao et al. took the maneuvering target running at a constant speed as the object, and extracted the target velocity and acceleration vector to always keep orthogonal This relationship is used as known prior information, combined with the calculation process of Kalman filtering, a better filtering algorithm is proposed, and the asymptotic unbiased convergence of the filter is demonstrated. However, this method does not have a recursive function and is not suitable for Tracking and positioning of maneuvering targets

Later, D. Simon et al. proposed a method based on projection (PJ) in the article "Kalmanfiltering with state equality constraints" (Trans.AES, 2002, 38, pp.128-136), by combining the Kalman filtering results with the constraints The new constraint estimation is obtained by combining space and space, which is specifically expressed as solving a Lagrangian equation to achieve target positioning. However, the positioning accuracy of this method still needs to be improved.

In the article "State estimation for linear systems with state equality constraints" (Automatica, 2007, 43:1363–1368), Ko et al. made assumptions about some parameters of the constraint system, and derived a constraint predictor on this basis. It is verified that the predictor has more accurate estimation results than the projection method, and provides reliable auxiliary monitoring data for ship positioning and navigation. However, several assumptions made in this method limit the scope of application of this method

Afterwards, Hewett et al. proposed a positioning method based on null space decomposition (NS) in the article "A robust null space method for linear equality constrained state estimation" (Trans.SP 2010,58,3961–3971), and considered that the state vector of the system It is composed of a fixed part and a random part. First, the estimated values ​​of the two are obtained separately, and then the estimated value of the original constraint system state is obtained through reconstruction. However, this method loses part of the information in the process of reducing the order, resulting in low estimation accuracy. , the precise positioning of the target cannot be achieved

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