Target-tracking device and target-tracking method

Abstract

A target-tracking device comprises an STF-parameter setting unit (21) that acquires an observation value and a prediction value of a target, and that dynamically sets an STF-parameter ρ on the basis of the observation value and the prediction value that have been acquired.

Description

Target tracking device and target tracking method

[0001] The present disclosure relates to target tracking techniques.

[0002] When tracking targets that are difficult to fully model using sensors such as radar or optical cameras, a multi-model approach is often used, in which the reliability of each model is calculated based on the likelihood of multiple models and then weighted integration is performed to achieve tracking. However, multi-model approaches require a high computational load because multiple models are operated, and it is known that it is difficult to perform correlation processing on multiple observations. Therefore, there is a demand for a target tracking filter that can maintain tracking accuracy equivalent to that of a multi-model with a single model. One document that discloses a technology that meets this demand is Non-Patent Document 1, which proposes a Strong Tracking Filter (STF).

[0003] D. F. Crouse, “Strong Tracking Filters: Derivation and Improved Heuristic,” in 2019 22th International Conference on Information Fusion (FUSION), Jul. 2019, pp. 1-8.

[0004] The STF is a filter that obtains a certain level of tracking accuracy by adaptively expanding the prediction error in the tracking filter according to the difference between the predicted value and the observed value, based on the premise that it is difficult to accurately estimate the amount of error of a single model in advance. The degree to which the prediction error of the STF is expanded depends on the STF parameter ρ, which is set in advance to a fixed value ranging from 0 to 1. However, because the optimal value of this STF parameter ρ differs depending on the tracking situation, there is a problem in that the value of the set STF parameter ρ cannot be dynamically set.

[0005] The present disclosure has been made to solve such problems, and aims to provide a target tracking technology that allows the value of the STF parameter ρ to be dynamically set.

[0006] One aspect of a target tracking device according to an embodiment of the present disclosure includes an STF parameter setting unit that acquires observed values ​​and predicted values ​​of a target and dynamically sets an STF parameter ρ based on the acquired observed values ​​and predicted values.

[0007] According to the target tracking device according to the embodiment of the present disclosure, the value of the STF parameter ρ can be dynamically set.

[0008] It is a functional block diagram showing a configuration example of a target tracking device. It is a diagram showing a hardware configuration example of a target tracking device. It is a flowchart showing the operation of the target tracking device. It is a functional block diagram showing a configuration example of an STF parameter setting unit.

[0009] Various embodiments of the present disclosure will be described in detail below with reference to the drawings. In the drawings, identical or similar parts are designated by identical or similar reference numerals, and redundant explanations of such parts will be omitted. In addition, in this disclosure, the term "or" is used to mean an inclusive logical OR unless otherwise specified.

[0010] Embodiment 1. <Configuration> A target tracking device according to embodiment 1 of the present disclosure will be described with reference to FIG. 1 . FIG. 1 is a functional block diagram showing an example configuration of a target tracking device. As shown in FIG. 1 , the target tracking device includes an observation processing unit 11, a prediction processing unit 12, an STF parameter setting unit 21, an STF processing unit 16, and a time update processing unit 17. The STF parameter setting unit 21 is a functional unit that sets STF parameters used in STF processing. To achieve this function, the STF parameter setting unit 21 includes, as an example, an average residual calculation processing unit 13, a distance calculation processing unit 14, and an STF parameter determination processing unit 15.

[0011] (Observation Processing Unit) The observation processing unit 11 receives signals from sensors such as radar or optical cameras (not shown), detects the state of a target from the received signals, and generates observed values ​​z(t) as vectors. Examples of the state of the target include physical quantities such as position or velocity, or image features. The observation processing unit 11 supplies the generated observed values ​​z(t) to the mean residual calculation processing unit 13, the STF processing unit 16, and the time update processing unit 17.

[0012] (Prediction Processing Unit) The prediction processing unit 12 predicts the state of the target at the next time t. Specifically, similar to a known Kalman filter, the prediction processing unit 12 predicts the state x t-1 , the state transition matrix, and the noise vector to generate a predicted value x^(t|t-1) and a prediction error covariance matrix P~(t|t-1) as vectors. The prediction processing unit 12 supplies the generated predicted value x^(t|t-1) and prediction error covariance matrix P~(t|t-1) to the average residual calculation processing unit 13. Note that the generated prediction error covariance matrix P~(t|t-1) is data used by the distance calculation processing unit 14, so the prediction processing unit 12 may supply the generated prediction error covariance matrix P~(t|t-1) directly to the distance calculation processing unit 14.

[0013] (Average Residual Calculation Processing Unit) The average residual calculation processing unit 13 receives the observed value z(t) from the observation processing unit 11, and receives the predicted value x^(t|t-1) and the prediction error covariance matrix P~(t|t-1) from the prediction processing unit 12. The average residual calculation processing unit 13 uses the predicted value x^(t|t-1) and the observed value z(t) to calculate the average e(t) of the difference between the predicted value and the observed value. The average of the difference between the predicted value and the observed value is calculated, for example, according to the following equation (1).

[0014] Here, H represents the observation matrix, and n represents a design parameter having an arbitrary integer value of 1 or more.

[0015] The average residual calculation processing unit 13 supplies the prediction error covariance matrix P~(t|t-1) received from the prediction processing unit 12 and the calculated average e(t) of the differences between the predicted values ​​and the observed values ​​to the distance calculation processing unit 14. When the prediction processing unit 12 supplies the prediction error covariance matrix P~(t|t-1) to the distance calculation processing unit 14, the average residual calculation processing unit 13 does not need to supply the prediction error covariance matrix P~(t|t-1) to the distance calculation processing unit 14.

[0016] (Distance Calculation Processing Unit) The distance calculation processing unit 14 receives the prediction error covariance matrix P~(t|t-1) from the prediction processing unit 12 or the average residual calculation processing unit 13, and receives the average e(t) of the differences between the predicted values ​​and the observed values ​​and the observation matrix H from the average residual calculation processing unit 13. The distance calculation processing unit 14 performs processing to calculate the square of the Mahalanobis distance as the statistical distance using the received prediction error covariance matrix P~(t|t-1), the average e(t) of the differences between the predicted values ​​and the observed values, and the observation matrix H.

[0017] The distance calculation processing unit 14 first calculates the observation residual covariance matrix S(k) by the following equation (2).

[0018] In equation (2), R represents the observation covariance matrix, and T represents the transpose of the matrix.

[0019] Next, the distance calculation processing unit 14 calculates the square value d(t) of the Mahalanobis distance using the observation residual covariance matrix S(k) according to the following equation (3): d(t)=e(t) T S(t) -1 e(t) (3)

[0020] The distance calculation processing unit 14 supplies the square value d(t) of the calculated Mahalanobis distance to the STF parameter determination processing unit 15 .

[0021] (STF Parameter Determination Processor) The STF parameter determination processor 15 determines the STF parameter ρ using the following equation (4): ρ(t)=min(1,P / d(t)) (4)

[0022] Here, P is an arbitrary parameter determined from a chi-square distribution according to the number of dimensions of the observed or predicted tracking values. The parameter P is a parameter determined by the number of dimensions (or the magnitude of the vector) of the observed or predicted values. Here, the dimensions (state quantities) of the observed and predicted values ​​are not limited to specific dimensions (e.g., the position or velocity of the target, pixel values ​​or size, or shape features) and may include any dimension. The STF parameter determination processing unit 15 obtains the value of the parameter P from the observed value z(t). Furthermore, min(·,·) is a function that returns the value of the smaller of its arguments. Therefore, when P / d(t) is a value equal to or greater than 1, the value of 1 is used as the value of the STF parameter ρ, and when P / d(t) is a value less than 1, the value of P / d(t) is used as the value of the STF parameter ρ. Therefore, the value of the STF parameter ρ can be dynamically and appropriately set so that the value of P is not taken into account when it is an outlier, and the value of P is taken into account when it is not an outlier.

[0023] The STF parameter determination processing unit 15 supplies the determined STF parameter ρ to the STF processing unit 16 .

[0024] (STF Processing Unit) The STF processing unit 16 is a functional unit that receives the observed value z(t) from the observation processing unit 11 and the STF parameter ρ from the STF parameter determination processing unit 15, and performs STF processing using the observed value z(t) and the STF parameter ρ. Briefly describing the STF processing, the STF processing unit calculates a residual covariance matrix using the prediction error covariance matrix, the cross product of the observed prediction error at the current time, and the STF parameter ρ (Equation (60) in Non-Patent Document 1), calculates an expansion coefficient λ of the prediction error covariance matrix using the observation error covariance matrix and the calculated residual covariance matrix (Equation (71) in Non-Patent Document 1), and updates the prediction error covariance matrix by applying the calculated expansion coefficient λ to the prediction error covariance matrix (Equation (76) in Non-Patent Document 1), thereby newly calculating the prediction error covariance matrix P(t|t-1). For details of the STF processing, please refer to publicly known documents such as Non-Patent Document 1. The STF processing unit 16 outputs the updated prediction error covariance matrix P(t|t−1) to the temporal update processing unit 17 .

[0025] (Time update processing unit) When the time update processing unit 17 receives the observation value z(t) from the observation processing unit 11 and the updated prediction error covariance matrix P(t|t-1) from the STF processing unit 16, it updates the time and supplies the updated time, the observation value z(t), and the updated prediction error covariance matrix P(t|t-1) to the prediction processing unit 12.

[0026] Next, an example of the hardware configuration of a target tracking device will be described with reference to Figures 2A and 2B. Each function of the target tracking device is realized by a processing circuit. The processing circuitry may be a dedicated processing circuit 100a as shown in Figure 2A, or a processor 100b that executes a program stored in a memory 100c as shown in Figure 2B.

[0027] When the processing circuitry is a dedicated processing circuit 100a, the dedicated processing circuit 100a may be, for example, a single circuit, a multiple circuit, a programmed processor, a parallel programmed processor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination thereof. The functions of the target tracker may be realized by separate processing circuits, or the functions of the target tracker may be realized together in a single processing circuit.

[0028] When the processing circuitry is a processor 100b, the functions of the target tracking device are realized by software, firmware, or a combination of software and firmware. The software and firmware are written as programs and stored in memory 100c. The processor 100b realizes the functions of the target tracking device by reading and executing the programs stored in memory 100c. Here, examples of memory 100c include non-volatile or volatile semiconductor memory such as random access memory (RAM), read-only memory (ROM), flash memory, erasable programmable read-only memory (EPROM), and electrically erasable programmable read-only memory (EEPROM), as well as magnetic disks, flexible disks, optical disks, compact disks, minidisks, and DVDs.

[0029] It is also possible to realize some of the functions of the target tracking device using dedicated hardware, and other functions using software or firmware. In this way, the processing circuit can realize the functions of the target tracking device using hardware, software, firmware, or a combination of these.

[0030] <Operation> Next, the operation of the target tracking device according to the first embodiment will be described with reference to FIG.

[0031] (Step ST11) In step ST11, the observation processing unit 11 receives a signal from a sensor such as a radar or an optical camera (not shown), detects the state of the target from the received signal, and generates an observation value z(t).

[0032] (Step ST12) In step ST12, the prediction processing unit 12 predicts the state of the target at the next time t. Specifically, the prediction processing unit 12 predicts the state x t-1 , the state transition matrix, and the noise vector are used to generate the predicted value x̂(t|t-1) and the prediction error covariance matrix P̂(t|t-1).

[0033] (Step ST13) In step ST13, the average residual calculation processing unit 13 calculates the average e(t) of the difference between the predicted value and the observed value using the predicted value x^(t|t-1) and the observed value z(t).

[0034] (Step ST14) In step ST14, the distance calculation processing unit 14 calculates the squared value of the Mahalanobis distance using the prediction error covariance matrix P~(t|t-1), the average e(t) of the difference between the predicted value and the observed value, and the observation matrix H.

[0035] (Step ST15) In step ST15, the STF parameter determination processing unit 15 determines the STF parameter ρ using equation (4).

[0036] (Step ST16) In step ST16, the STF processing unit 16 performs STF processing using the observed value z(t) and the STF parameter ρ.

[0037] (Step ST17) In step ST17, the time update processing unit 17 updates the time.

[0038] According to the target tracking device of embodiment 1 described above, the value of the STF parameter ρ is dynamically set in accordance with equation (4). Furthermore, as shown in equation (4), the STF parameter ρ is determined based on an arbitrary parameter P determined from a chi-square distribution according to the number of dimensions of the observed or predicted tracking values, so the value of the STF parameter ρ can be set appropriately depending on the tracking situation.

[0039] <Modifications> Various modifications of the first embodiment will be described below.

[0040] <Modification 1> The following equations (5) and (6) may be used instead of equation (1) in the first embodiment. That is, the average may be a weighted average: e(0)=0 (5)

[0041] <Modification 2> Equation (3) in the first embodiment may be replaced by finding a statistical test quantity such as Kullback-Leibler divergence, Fisher information, or t-value using e or e and S. In that case, P in equation (4) is changed to a numerical value corresponding to each value.

[0042] <Modification 3> The result obtained by equation (4) does not necessarily have to be applied to STF processing. For example, the tracking prediction error may be directly adjusted by using ρ(t) obtained by equation (4) as a prediction error correction amount and applying it to a parameter related to the drive noise of the Kalman filter. In this case, the value range of the parameter to be adjusted may be adjusted by multiplying ρ(t) by an arbitrary coefficient k.

[0043] Embodiment 2 The target tracking device may include an STF parameter setting unit 21A as shown in Fig. 4, instead of the STF parameter setting unit 21 according to embodiment 1. As shown in Fig. 4, the STF parameter setting unit 21A includes a target detection accuracy calculation processing unit 22 and an STF parameter determination processing unit 15A.

[0044] (Target Detection Accuracy Calculation Processing Unit) The target detection accuracy calculation processing unit 22 is a functional unit that calculates the target detection accuracy according to known techniques. For example, when tracking is performed using radar, the target detection accuracy may be derived from a known model such as a radar equation, or may be calculated from the error from the true value using Monte Carlo simulation. Furthermore, the target detection accuracy may be calculated from measurement results in a real environment, or, if the true value is unknown, may be expressed as its expected value and variance, etc. The target detection accuracy calculation processing unit 22 supplies the calculated target detection accuracy to the STF parameter determination processing unit 15A.

[0045] (STF Parameter Determination Processor) When the STF parameter determination processor 15A acquires the calculated target detection accuracy, it references a ρ table (not shown) in which values ​​of the target detection accuracy and values ​​of the STF parameter ρ are previously associated with each other, and acquires the value of the STF parameter ρ that corresponds to the calculated target detection accuracy. The ρ table is stored in the memory 100c or a storage device (not shown).

[0046] For example, in the case of radar, the accuracy of distance measurement changes depending on the distance to the target. Therefore, if there is a correlation between the target detection accuracy of the sensor and ρ, as in the second embodiment, a ρ table may be prepared in advance to hold values ​​of the STF parameter ρ corresponding to the target detection accuracy, and the value of ρ may be changed according to the ρ table.

[0047] It is possible to combine the embodiments, and to modify or omit each embodiment as appropriate.

[0048] The target tracking device of the present disclosure can be used as a device that tracks a target using a sensor such as a radar or an optical camera.

[0049] 11 observation processing unit, 12 prediction processing unit, 13 average residual calculation processing unit, 14 distance calculation processing unit, 15 STF parameter determination processing unit, 15A STF parameter determination processing unit, 16 STF processing unit, 17 time update processing unit, 21 STF parameter setting unit, 21A STF parameter setting unit, 22 target detection accuracy calculation processing unit, 100a processing circuit, 100b processor, 100c memory.

Claims

1. A target tracking device comprising: an STF parameter setting unit that acquires observed values ​​and predicted values ​​of a target, and dynamically sets an STF parameter ρ based on the acquired observed values ​​and predicted values.

2. A target tracking device as described in claim 1, wherein the STF parameter setting unit comprises: an average residual calculation processing unit that calculates an average residual between the observed value and the predicted value; a distance calculation processing unit that calculates a statistical distance of the average residual from the average residual and a residual covariance matrix; and an STF parameter determination processing unit that determines the STF parameter ρ based on the calculated statistical distance.

3. A target tracking device according to claim 2, wherein the statistical distance is the square of the Mahalanobis distance, and the STF parameter determination processing unit determines the smaller of the square of the calculated Mahalanobis distance, the value of an arbitrary parameter determined from a chi-squared distribution, or 1 as the STF parameter ρ.

4. A target tracking device as described in claim 1, wherein the STF parameter setting unit comprises: a target detection accuracy calculation processing unit that calculates the target detection accuracy of the target from the observed value and the predicted value; and an STF parameter determination processing unit that refers to a table that holds values ​​associated with the target detection accuracy, and obtains the value corresponding to the calculated target detection accuracy as the STF parameter ρ.

5. A target tracking method performed by a target tracking device having an STF parameter setting unit, comprising: a step in which the STF parameter setting unit acquires observed values ​​and predicted values ​​of the target and calculates an average residual between the acquired observed values ​​and predicted values; a step in which the STF parameter setting unit calculates a statistical distance of the average residual from the average residual and a residual covariance matrix; and a step in which the STF parameter setting unit determines, as an STF parameter ρ, the smaller of the squared value of the calculated Mahalanobis distance, the value of an arbitrary parameter determined from the chi-squared distribution, or 1.

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