Radar device

Abstract

A radar device of one aspect of the present disclosure includes a transmission unit, a reception unit, a setting unit, a detection unit, an observation unit, a target tracking unit, a residual calculation unit, an evaluation unit, and a ghost determination unit. The residual calculation unit calculates a velocity residual that is a difference between a velocity prediction value and a velocity observation value of a target. The evaluation unit calculates an evaluation value that corresponds to a possibility that the target is a wrap-around ghost based on a size of a deviation of the velocity residual in a time series of the target. The ghost determination unit determines whether the target is a wrap-around ghost based on the evaluation value of the target.

Description

Technical Field

[0001] This disclosure relates to radar devices. Background Technology

[0002] When using radar to track objects, there are cases where the relative velocity of the observed objects is ambiguous. For example, when calculating the relative velocity based on the phase rotation of frequency components continuously detected for the same object, there is a possibility that for the detected phase φ, the actual phase is φ+2π×m (where m is an integer), and the relative velocity cannot be determined.

[0003] The radar device described in Patent Document 1 below determines the true relative velocity by tracking multiple hypotheses that assume ambiguity in the relative velocity. These multiple hypotheses assume different number of foldbacks, m. Specifically, the radar device calculates the likelihood of each of the multiple hypotheses and selects the hypothesis with the higher likelihood. Then, the radar device determines the relative velocity of the selected hypothesis as the true relative velocity.

[0004] Patent Document 1: Japanese Patent Application Publication No. 2019-168449

[0005] The inventors' detailed research revealed the following problem: when the aforementioned radar device uses a time series of observation results to track multiple hypotheses, it may mistakenly identify multiple distinct objects as the same object, thus selecting an incorrect hypothesis. Furthermore, the discovery of a problem arises where selecting an incorrect hypothesis can lead to ghosting. For example, mistakenly identifying multiple consecutive roadside objects as the same object can result in ghosting. Ghosting can cause stationary objects to be misidentified as moving objects. This misidentification of stationary objects as moving objects can lead to unnecessary warnings to the driver and / or unnecessary vehicle control measures. Summary of the Invention

[0006] One aspect of this disclosure is to provide a radar device capable of determining ghosting.

[0007] One aspect of this disclosure is a radar device mounted on a vehicle, comprising a transmitter, a receiver, a setting unit, a detection unit, an observation unit, an object tracking unit, a residual calculation unit, an evaluation unit, and a ghosting determination unit. The transmitter is configured to transmit a transmission signal at a set repetition period. The transmission signal is a pulse signal or a chirped signal. The receiver is configured to receive a reflected signal generated by the transmission signal transmitted by the transmitter being reflected by at least one object. The setting unit is configured to set a repetition period in the next processing cycle that is different from the repetition period in the immediately preceding processing cycle. The detection unit is configured to detect at least one object signal corresponding to at least one object from the reflected signal received by the receiver. The observation unit is configured to calculate at least one object observation value based on the at least one object signal detected by the detection unit. Each of the at least one object observation values ​​includes a velocity observation value that considers the velocity reflection of at least one object. The object tracking unit includes a prediction unit and an estimation unit, and is configured to track each of the at least one object based on the time series of the at least one object observation values ​​calculated by the observation unit. The prediction unit is configured to calculate a predicted value for the current state of each of the at least one objects, based on an estimated value corresponding to the past state of each of the at least one objects. The estimation unit is configured to establish a correlation between the predicted values ​​and observed values, and to calculate an estimated value for the current state of each of the at least one objects based on the correlated predicted and observed values. The predicted values ​​include velocity predictions within the features. The residual calculation unit is configured to calculate the velocity residual for each of the at least one objects. The velocity residual is the difference between the predicted and observed velocity values. The evaluation unit is configured to calculate an evaluation value for each of the at least one objects based on the magnitude of the deviation of the velocity residuals in the time series of each of the at least one objects. The evaluation value corresponds to the probability that each of the at least one objects is a ghost image. The ghost image determination unit is configured to determine whether each of the at least one objects is a ghost image based on its evaluation value.

[0008] In one aspect of the radar apparatus of this disclosure, a velocity residual is calculated, which is the difference between the predicted velocity value and the observed velocity value for each of at least one target. In the case where each of the at least one target is a ghost image, the deviation of the velocity residual in the time series increases. Therefore, based on the magnitude of the deviation of the velocity residual in the time series, an evaluation value corresponding to the probability that each of the at least one target is a ghost image is calculated. Thus, it is possible to determine whether each of the at least one target is a ghost image based on the calculated evaluation value. Attached Figure Description

[0009] Figure 1 This is a block diagram showing the structure of the driving assistance system according to the first embodiment.

[0010] Figure 2 This is a diagram showing an example of the mounting location and inspection area of ​​the radar device according to the first embodiment.

[0011] Figure 3 The diagram shows other examples of the mounting location and inspection area of ​​the radar device according to the first embodiment.

[0012] Figure 4 This is a diagram showing the transmission signal having two chirp periods in the first embodiment.

[0013] Figure 5 This is a flowchart illustrating the object detection process of the first embodiment.

[0014] Figure 6 This represents the subroutine for ghosting detection processing in the first embodiment.

[0015] Figure 7 This is a subroutine representing the ghosting evaluation value calculation process of the first embodiment.

[0016] Figure 8 This is an explanatory diagram representing a summary of a two-dimensional FFT.

[0017] Figure 9 It is a diagram illustrating the return of speed.

[0018] Figure 10 This is a graph representing the detection speed when the true speed value is -100 km / h and the maximum detection speed is 80 km / h.

[0019] Figure 11 This is a graph representing the detection speed when the true speed value is -100 km / h and the maximum detection speed is 70 km / h.

[0020] Figure 12 This diagram illustrates a vehicle traveling in a tunnel equipped with both primary and secondary lighting.

[0021] Figure 13 It is a diagram representing the observations of the first illumination and the three hypotheses generated based on those observations.

[0022] Figure 14 It is a diagram showing the observed values ​​of the second illumination and the hypothetical predicted positions based on the observed values ​​of the first illumination.

[0023] Figure 15 This is a subroutine representing the ghosting evaluation value calculation process in the second embodiment.

[0024] Figure 16 This is a flowchart illustrating the object detection process in the third embodiment.

[0025] Figure 17 This refers to the subroutine for determining the same object in the third embodiment.

[0026] Figure 18 This represents the subroutine for ghosting error detection suppression processing in the third embodiment.

[0027] Figure 19 This is a subroutine representing a ghosting error suppression process in another example of the third embodiment.

[0028] Figure 20 This is a diagram showing the trailing edge signal generated by the trailing edge of the vehicle in front and the tire signal generated by the tires of the vehicle in front.

[0029] Figure 21 It means and Figure 20 Graphs of trailing edge signal and tire signal at different times.

[0030] Figure 22 This diagram illustrates a scenario where the first object disappears due to misconnection, and the second object, which is mistakenly identified as a ghost, continues to be identified.

[0031] Figure 23 This is a flowchart illustrating the object detection process in the fourth embodiment.

[0032] Figure 24 This refers to the subroutine for tracking suppression processing in the fourth embodiment.

[0033] Figure 25 This refers to a subroutine for tracking suppression processing, representing another example of the fourth implementation. Detailed Implementation

[0034] The following description, with reference to the accompanying drawings, illustrates the methods used to implement this disclosure.

[0035] (First Implementation)

[0036] <1-1. Structure>

[0037] First, refer to Figure 1 The structure of the driving assistance system 100 according to this embodiment will be described. The driving assistance system 100 includes a radar device 10 and a driving assistance device 50, and is mounted on a vehicle 60.

[0038] like Figure 2 As shown, the radar device 10 is mounted at the front center of the vehicle 60 (e.g., at the center of the front bumper). The radar device 10 has a detection area Rd that includes the area at the front center of the vehicle 60. Or, as Figure 3As shown, radar devices 10 can also be mounted on the front center of vehicle 60, the left front side and right front side of vehicle 60 (e.g., the left and right ends of the front bumper), and the left rear side and right rear side of vehicle 60 (e.g., the left and right ends of the rear bumper). That is, radar devices 10 can also have inspection areas Rd that include the front center, left front, right front, left rear, and right rear of vehicle 60. Not all five radar devices 10 need to be mounted on vehicle 60. Only one of the five radar devices 10 may be mounted on vehicle 60, or two or more radar devices 10 may be mounted on vehicle 60.

[0039] The radar device 10 is a millimeter-wave radar using FCM (Fast Chirp Modulation) for transmitting and receiving chirped signals. The radar device 10 includes a processing unit 20, a transmitting antenna 11, and a receiving antenna 12. The processing unit 20 includes a CPU, ROM, RAM, and a core processor. The core processor performs high-speed Fourier transform (FFT) processing, etc. The core processor can also be removed from the processing unit 20. The processing unit 20 implements the functions of the setting unit, detection unit, observation unit, prediction unit, estimation unit, residual calculation unit, evaluation unit, decision unit, stationary velocity calculation unit, ground velocity calculation unit, and vehicle control unit of this disclosure by executing programs stored in the ROM. Some or all of the functions implemented by the processing unit 20 can also be implemented in hardware. Furthermore, at least one of the functions of the setting unit, detection unit, observation unit, prediction unit, estimation unit, residual calculation unit, evaluation unit, decision unit, stationary velocity calculation unit, ground velocity calculation unit, and vehicle control unit can be removed from the functions implemented by the processing unit 20.

[0040] The transmitting antenna 11 includes a transmitting array antenna composed of multiple antenna elements. The transmitting antenna 11 repeatedly transmits chirped signals at a repetition period set by the processing device 20.

[0041] like Figure 4 As shown, a chirped signal is a radar signal whose frequency has been modulated in a sawtooth-like pattern. That is, the chirped signal has a frequency that increases or decreases continuously. Figure 4 The chirp signal shown has a continuously increasing frequency, but it can also have a continuously decreasing frequency. The repetition period of the chirp signal (hereinafter referred to as the chirp period) is the period from the start of one chirp signal to the start of the next chirp signal.

[0042] The transmitting antenna 11 alternately transmits a chirped signal with a first chirped period T1 (hereinafter referred to as the first chirped signal) and a chirped signal with a second chirped period T2 (hereinafter referred to as the second chirped signal). The first chirped period T1 is a period of length T. The second chirped period T2 is longer than the first chirped period T1, and is a period of length T + ΔT (ΔT > 0). The transmitting antenna 11 transmits M first chirped signals in the first processing cycle and N second chirped signals in the second processing cycle. The transmitting antenna 11 alternately repeats the first processing cycle and the second processing cycle. M and N are natural numbers.

[0043] The receiving antenna 12 includes a receiving array antenna composed of multiple antenna elements, which receives the reflected signal generated by the reflection of the first chirped signal or the second chirped signal by the target.

[0044] The processing unit 20 detects a target signal representing a target based on the reflected signal received by the receiving antenna 12. Based on the detected target signal, the processing unit 20 detects the target's speed observation, distance observation, and azimuth observation. The processing unit 20 tracks the detected target to determine target information and generates control commands corresponding to the target based on the determined target information. Then, the processing unit 20 outputs the generated control commands to the driving assistance device 50.

[0045] The driver assistance device 50 assists the driving of the vehicle 60 based on control commands output from the processing device 20. For example, the driver assistance device 50 may issue a warning to the driver to report the possibility of a collision or perform braking actions to avoid a collision.

[0046] <1-2. Processing>

[0047] Next, refer to Figure 5 The flowchart below describes the object detection process performed by the processing apparatus 20 of the first embodiment. The processing apparatus 20 repeatedly performs this process at a predetermined processing cycle.

[0048] First, in S10, a first chirp cycle T1 or a second chirp cycle T2 is set within the chirp cycle. If the first chirp cycle T1 was set in the previous processing cycle, the second chirp cycle T2 is set within the chirp cycle in the current processing cycle. If the second chirp cycle T2 was set in the previous processing cycle, the first chirp cycle T1 is set within the chirp cycle in the current processing cycle.

[0049] Next, in S20, when the first chirp period T1 is set in S10, a first chirp signal is transmitted from the transmitting antenna 11, and the reflected signal received by the receiving antenna 12 is acquired. Additionally, when the second chirp period T2 is set in S10, a second chirp signal is transmitted from the transmitting antenna 11, and the reflected signal received by the receiving antenna 12 is acquired.

[0050] Next, in S30, at least one target signal is detected based on the reflected signal received in S20. Specifically, as... Figure 8 As shown, beat signals are obtained based on the transmitted and reflected signals. When the first chirp signal is transmitted, M beat signals are obtained. Furthermore, when the second chirp signal is transmitted, N beat signals are obtained.

[0051] Then, as the first FFT process, an FFT is performed on each of the acquired M or N beat signals to calculate M or N distance spectra. The distance spectrum is equivalent to a two-dimensional spectrum representing the power relative to the distance. Since the beat signals have frequency components corresponding to the distance to the object, the frequency BIN of the calculated distance spectrum is equivalent to the distance BIN.

[0052] Furthermore, as a second process, FFT processing is performed on each distance BIN of the calculated M or N distance spectra to calculate the distance-velocity spectrum. The distance-velocity spectrum is equivalent to a three-dimensional spectrum representing power relative to distance and velocity. Then, the velocity BIN and distance BIN that become peaks are searched from the calculated distance-velocity spectrum, and the peaks of the distance-velocity spectrum are extracted as landmark signals. The landmark signal indicates the presence of a landmark. In the case of multiple peaks, multiple landmark signals are extracted.

[0053] Next, in S40, the object observation value is calculated based on the velocity BIN and distance BIN of the object signal extracted in S30. The object observation value includes the object's velocity observation value, distance observation value, and orientation observation value as elements. At least one of the velocity observation value, distance observation value, and orientation observation value can also be deleted from the object observation value. The velocity observation value corresponds to the observation value of the relative velocity of the object relative to the vehicle 60. As described later, the velocity observation value is calculated based on the detection velocity Vo detected by the velocity BIN, the selected number of retracements m1 or m2, and the maximum detection velocity Vmax. The distance observation value corresponds to the observation value of the distance from the vehicle 60 to the object. The orientation observation value of the object is calculated based on the orientation spectrum containing the orientation information of the object relative to the vehicle 60. The orientation spectrum is calculated by applying an arrival direction estimation algorithm to the object signal.

[0054] Here, the maximum detection speed Vmax1 of the first chirped signal is represented by equation (1), and the maximum detection speed Vmax2 of the second chirped signal is represented by equation (2). The maximum detection speed Vmax1 is the maximum value of the speed observation that can be checked without backtracking when the first chirped signal has been sent. The maximum detection speed Vmax2 is the maximum value of the speed observation that can be checked without backtracking when the second chirped signal has been sent. c is the speed of light, and fc is the center frequency of the first and second chirped signals. As shown in equations (1) and (2), since the first chirped period T1 is shorter than the second chirped period T2, the maximum detection speed Vmax1 is greater than the maximum detection speed Vmax2.

[0055] Vmax1=c / (4×fc×T1) (1)

[0056] Vmax2=c / (4×fc×T2) (2)

[0057] like Figure 9 As shown, when the observed velocity exceeds the maximum detection velocity Vmax1 or Vmax2, a velocity retracement occurs. Equations (3) and (4) hold true between the observed velocity, the maximum detection velocity, and the true velocity value. Vo1 is the detection velocity when the first chirp signal is transmitted, Vo2 is the detection velocity when the second chirp signal is transmitted, V is the true velocity value, and m1 and m2 are the number of retracements and are integers.

[0058] Vo1=V-2Vmax1×m1 (3)

[0059] Vo2=V-2Vmax2×m2 (4)

[0060] For example, such as Figure 10 As shown, with a maximum detection speed Vmax1 of 80 km / h and a true velocity of Tg0 of the object of -100 km / h, a -1 retracement occurs, detecting a velocity of +60 km / h as the detection speed Vo of Tg0. Additionally, as... Figure 11 As shown, when the maximum detection speed Vmax2 is 70 km / h and the true value of the speed of the object Tg0 is -100 km / h, a -1 retrace occurs, and +40 km / h is detected as the detection speed Vo of the object Tg0.

[0061] Next, in S50, it is determined whether there is any unprocessed object information. Specifically, it is determined whether there are any objects being tracked in the current processing cycle that have not yet been processed in subsequent S60 to S170. If it is determined in S50 that there are unprocessed objects, one of the unprocessed objects is selected and the processing in S60 is entered, and the processing in S60 to S170 is performed on the selected object. On the other hand, if it is determined that there are no unprocessed objects, the current processing cycle ends.

[0062] In S60, the predicted value of an object is calculated based on object information from past processing cycles. The predicted value is calculated based on the estimated value of the object calculated in past processing cycles. The predicted value includes the predicted velocity, distance, and azimuth as features. Alternatively, the predicted value includes the predicted velocity, X-coordinate, and Y-coordinate as features. The estimated value includes the estimated velocity, distance, and azimuth as features. Alternatively, the estimated value includes the estimated velocity, X-coordinate, and Y-coordinate as features. The X-axis and Y-axis are mutually orthogonal axes on the road surface.

[0063] Next, in S70, the estimated object values ​​for the current processing cycle are calculated. First, the predicted object values ​​calculated in S60 are correlated with the object observations calculated in S40 that have a high probability of being the same object. Specifically, the predicted object values ​​are correlated with the object observations when the differences between each element of the predicted object value and each element of the object observations for the current processing cycle are within the correlation range. The correlation range is set according to each element of the predicted object value and the object observations. Then, filtering is performed on the predicted object value and the object observations correlated with it to calculate the estimated object values ​​for the current processing cycle. The filtering process may use a Kalman filter, for example.

[0064] Next, in S80, the ground velocity of the target is calculated. Specifically, the ground velocity of the target is calculated using the velocity and orientation observations of the target calculated in S40, as well as the vehicle speed of vehicle 60.

[0065] Next, in S90, it is determined whether the ground velocity calculated in S80 is above a velocity threshold. The velocity threshold is used to determine whether an object is the object for which ghosting determination is performed. In ghosting determination, it is determined whether the object is a folded ghost or a real object. Folded ghosting occurs when different objects are mistakenly tracked as the same object. That is, folded ghosting occurs because the predicted values ​​and observed values ​​of different objects are correlated.

[0066] As mentioned above, the detected velocity Vo has the potential to bounce back. Therefore, when an object is detected for the first time, considering the bounce back of the velocity, multiple hypotheses for the velocity observations are generated. For example, in equations (3) and (4), three hypotheses are generated that assume the bounce count to be -2, -1, and 0. Furthermore, each generated hypothesis is tracked, and the most reliable hypothesis, i.e., the bounce count, is selected. In this process of tracking each hypothesis, one hypothesis may become close to the observations of other objects. As a result, it is possible to establish a correlation between a hypothesis and the observations of other objects, resulting in a bounce ghosting.

[0067] Because of this ghosting effect, it is possible to misidentify a stationary object as a moving object. Figure 12 This example illustrates a scenario where vehicle 60 is traveling inside a tunnel. A first illumination Tg1 and a second illumination Tg2 are installed at predetermined intervals on the tunnel ceiling. Vehicle 60's speed is 100 km / h, and its maximum detection speed Vmax is 80 km / h.

[0068] First, the first illumination Tg1 enters the inspection area Rd of the radar device 10, and the first illumination Tg1 is observed. At this time, as... Figure 13 As shown, the first, second, and third hypotheses are generated. The first hypothesis assumes the number of retracements is 0. The second hypothesis assumes the number of retracements is -1. The third hypothesis assumes the number of retracements is -2. Under the first hypothesis, the observed velocity of the first illumination Tg1 is -100 km / h. Under the second hypothesis, the observed velocity of the first illumination Tg1 is +60 km / h. Under the third hypothesis, the observed velocity of the first illumination Tg1 is +220 km / h. In this case, the first hypothesis is actually correct.

[0069] Next, the first illumination Tg1 moves out of the inspection area Rd, and the second illumination Tg2 enters the inspection area Rd, thus the second illumination Tg2 is observed. For example... Figure 14 As shown, based on the first, second, and third assumptions of the first illumination Tg1, the predicted positions of the first, second, and third assumptions are calculated. Furthermore, there are cases where the predicted position of the second assumption is close to the observed value of the second illumination Tg2. In this case, the predicted position of the second assumption is correlated with the observed value of the second illumination Tg2. Moreover, the second assumption is determined to be the most reliable, and is selected as the true landmark, while the first and third assumptions are discarded. That is, -1 is chosen as the number of retracements.

[0070] As a result, the first illumination Tg1 and the second illumination Tg2 are identified as moving objects relative to the vehicle 60 at a relative speed of 60 km / h. That is, objects that are actually stationary are misidentified as moving objects. Such misidentified objects are equivalent to ghosting. If ghosting occurs, it may lead to incorrect vehicle control actions such as unnecessary warnings or unnecessary braking controls being performed on the driver.

[0071] Therefore, in the processing after S100, it is determined whether the object is a ghosting. Here, since erroneous vehicle control is not performed if a moving object is misidentified as a stationary object, the possibility of this becoming a problem is low even without considering it. Therefore, ghosting determination is performed only if there is a possibility that a stationary object is misidentified as a moving object. That is, ghosting determination is performed only if there is a possibility that the object is a moving object or a moving object is a ghost. The speed threshold in S90 takes into account the speed error of vehicle 60 and is set to a value that can distinguish between stationary and moving objects. For example, if the speed of vehicle 60 is 100 km / h and the speed error is 10%, the speed threshold is set to 10 km / h. Moreover, in S90, if it is determined that the ground speed is above the speed threshold, the process proceeds to S100; if it is determined that the ground speed is below the speed threshold, the process returns to S50.

[0072] In S100, it is determined whether the ghosting detection of the object selected in S50 has been completed. If the ghosting detection is completed in S100, the process returns to S50; if the ghosting detection is not completed, the process proceeds to S105.

[0073] In S105, ghosting detection processing is performed. Specifically, the following steps are executed: Figure 6 The subroutine shown first calculates the velocity residual in S110. The velocity residual is the difference between the predicted velocity value calculated in S60 and the velocity observation value associated with that predicted velocity value.

[0074] Next, in S120, a ghosting evaluation value is calculated based on the magnitude of the deviation of the velocity residuals in the time series. The ghosting evaluation value indicates the probability that the object is a reflected ghost.

[0075] like Figure 9 and Figure 10As shown, the detection velocities Vo1 and Vo2 relative to the true velocity value V vary according to the maximum detection velocities Vmax1 and Vmax2. When the correct assumptions are chosen, i.e., the correct number of retracements m1 and m2, even if the maximum detection velocities Vmax1 and Vmax2 are different, the velocity observations calculated based on the detection velocities Vo1 and Vo2 detected from the same object are approximately equal. On the other hand, when the incorrect assumptions are chosen, i.e., the incorrect number of retracements m1 and m2, if the maximum detection velocities Vmax1 and Vmax2 are different, the velocity observations calculated based on the detection velocities Vo1 and Vo2 detected from the same object will be different.

[0076] Therefore, when the first and second chirp signals are transmitted alternately, the difference between the predicted and observed velocity values ​​is relatively small when the correct assumption is selected. On the other hand, the difference between the predicted and observed velocity values ​​is relatively large when the incorrect assumption is selected. However, even when the correct assumption is selected, the difference between the predicted and observed velocity values ​​remains relatively large when the object accelerates or decelerates.

[0077] However, when incorrect assumptions are chosen, the deviation between the predicted and observed velocity values ​​in the time series is relatively large. Conversely, when correct assumptions are chosen, even when the object accelerates or decelerates, the deviation between the predicted and observed velocity values ​​in the time series is relatively small. Therefore, the ghosting evaluation value is calculated based on the magnitude of the deviation of the velocity residuals in the time series.

[0078] Specifically, execution Figure 7 The subroutine shown first calculates the change in velocity residuals in S300, representing the magnitude of the deviation of the velocity residuals in the time series. The change in velocity residuals is equivalent to the absolute value of the difference between the velocity residuals calculated in the current processing cycle and the velocity residuals calculated in the previous processing cycle.

[0079] Next, in S305, it is determined whether the change in velocity residual calculated in S300 is above a set first threshold. In S305, if it is determined that the change in velocity residual is above the first threshold, the process proceeds to S310; if it is determined that the change in velocity residual is less than the first threshold, the process proceeds to S320.

[0080] In S310, since the object is highly likely to be a ghost image, the ghost evaluation value is increased. For example, "1" is added to the ghost evaluation value. Then, the process proceeds to S340.

[0081] On the other hand, in S320, it is determined whether the change in velocity residual is below a set second threshold. The second threshold is less than the first threshold. If in S320 it is determined that the change in velocity residual is below the second threshold, the process proceeds to S330.

[0082] In S330, since the probability of the object being a ghost image is relatively low, the ghost evaluation value is reduced. For example, "1" is subtracted from the ghost evaluation value. Then, the process proceeds to S340.

[0083] On the other hand, if in S320 it is determined that the change in velocity residual is greater than the second threshold, the probability that the object is a ghost image is neither high nor low. Therefore, in this case, the ghost image evaluation value is not changed, and the process proceeds to S340.

[0084] Next, in S340, the stationary object velocity is calculated. The stationary object velocity is the detected velocity of the object assuming it is stationary. Specifically, the stationary object velocity is calculated based on the speed and orientation observation of the vehicle 60. The stationary object velocity is equivalent to a value that makes the projected velocity of the vehicle 60 in the orientation direction of the object negative.

[0085] Next, in S350, it is determined whether the detected speed Vo (i.e., the speed assuming the return before turning back) is consistent with the stationary speed calculated in S340. That is, it is determined whether the object is stationary. Specifically, it is determined whether the difference between the detected speed Vo and the stationary speed is less than or equal to a predetermined value. The predetermined value is a sufficiently small value that can be considered as the detected speed Vo being consistent with the stationary speed. If it is determined in S350 that the detected speed Vo is consistent with the stationary speed, the process proceeds to S360. On the other hand, if it is determined in S350 that the detected speed Vo is inconsistent with the stationary speed, the subroutine ends and the process proceeds to S130.

[0086] In S360, the ghosting evaluation value is increased. The frequency of ghosting caused by erroneously tracking stationary objects is relatively high. Therefore, when the target is determined to be stationary, the ghosting evaluation value is increased. Afterwards, the subroutine ends and proceeds to processing in S130.

[0087] Return to Figure 6 In S130, it is determined whether the ghosting evaluation value is above the set third threshold. If the ghosting evaluation value is determined to be above the third threshold in S130, the process proceeds to S140; if the ghosting evaluation value is determined to be below the third threshold, the process proceeds to S150.

[0088] In S140, the object is determined to be a ghost image.

[0089] Next, in S150, vehicle control for the object determined to be a ghosting in S140 is suppressed. That is, the output of the control command corresponding to the object to the driver assistance device 50 is suppressed. After that, the process returns to S50. Furthermore, the object determined to be a ghosting has completed the ghosting determination in this processing cycle, but is excluded from the ghosting determination in the next processing cycle. That is, in S100 of the next processing cycle, it is determined that the object has completed the ghosting determination.

[0090] On the other hand, in S160, it is determined whether the ghosting evaluation value is below the set fourth threshold. The fourth threshold is less than the third threshold. If in S160 it is determined that the ghosting evaluation value is below the fourth threshold, the process proceeds to S170.

[0091] In S170, the object is determined to be a non-ghosting object, i.e., a solid object. Then, the process returns to S50. Furthermore, objects determined to be non-ghosting objects undergo ghosting determination in this processing cycle, but are excluded from ghosting determination in subsequent processing cycles.

[0092] Furthermore, if in S160 the ghosting evaluation value is determined to be greater than the fourth threshold, the process does not determine whether the object is a ghost or a real object, and returns to the processing in S50. Since the object did not complete the ghosting determination in this processing cycle, it will become the object of ghosting determination in the next processing cycle. That is, in S100 of the next processing cycle, it will be determined that the object did not complete the ghosting determination.

[0093] <1-3. Effects>

[0094] According to the first embodiment described above, the following effects can be obtained.

[0095] (1) Calculate the velocity residual and, based on the magnitude of the deviation of the velocity residual in the time series, calculate the ghosting evaluation value. Therefore, it is possible to determine whether the object is a reflection ghost based on the calculated ghosting evaluation value.

[0096] (2) By using the change in velocity residual, it is possible to instantly determine whether the object is a ghost image.

[0097] (3) By increasing the ghosting evaluation value when the velocity residual change is above the first threshold, the possibility of judging the object as a ghosting can be increased.

[0098] (4) By reducing the evaluation value when the change in velocity residual is below the second threshold, the possibility of judging the object as a ghost image can be reduced.

[0099] (5) When the ghost evaluation value is above the third threshold, the object can be identified as a ghost because the probability of the object being a ghost is high enough.

[0100] (6) When the ghost evaluation value is below the fourth threshold, the object can be identified as an entity because the probability of the object being a ghost is low enough.

[0101] (7) The frequency of ghosting caused by erroneous tracking of stationary objects is relatively high. By increasing the evaluation value when the observation speed of the target is consistent with the speed of the stationary object, ghosting caused by erroneous tracking of stationary objects can be properly detected. Furthermore, it is possible to suppress the misidentification of stationary objects as moving objects.

[0102] (8) Problems will arise in vehicle control of stationary objects when they are mistakenly identified as moving objects. Therefore, objects with a ground speed exceeding a certain threshold are considered to be subject to ghosting. Thus, ghosting detection is only performed when vehicle control problems occur. This can suppress unnecessary ghosting detection and reduce the workload.

[0103] (9) By suppressing the output of control commands corresponding to objects that are determined to be ghosting, it is possible to suppress unnecessary alarms and / or unnecessary vehicle controls to the driver.

[0104] (Second Implementation)

[0105] <2-1. Differences from the first embodiment>

[0106] Since the basic structure of the second embodiment is the same as that of the first embodiment, the description of the common structures is omitted, and the description focuses on the differences. Furthermore, the same reference numerals as in the first embodiment indicate the same structures, as described above.

[0107] The ghosting evaluation value calculation process in the second embodiment differs from that in the first embodiment. In the first embodiment, the ghosting evaluation value is increased or decreased based on the change in velocity residuals. In contrast, the second embodiment uses the variance of the velocity residuals in the time series as the ghosting evaluation value, which differs from the first embodiment.

[0108] <2-2. Processing>

[0109] Next, refer to Figure 15 The subroutine describes the ghosting evaluation value calculation process performed by the processing apparatus 20 of the second embodiment. In the ghosting evaluation value calculation process of S120, the processing apparatus 20 of this embodiment replaces... Figure 7 The subroutine shown is executed. Figure 15 The subroutine shown.

[0110] First, in S500, the variance of the velocity residuals calculated in the K processing cycles prior to the current processing cycle is calculated. K is an integer greater than or equal to 2.

[0111] Next, the velocity residual calculated in S500 will be used as the ghosting evaluation value.

[0112] Next, in S520 to S540, the same processing as in S340 to S360 is performed.

[0113] <2-3. Effects>

[0114] According to the second embodiment described above, in addition to the effects (1), (3) to (9) mentioned above, the following effects can also be obtained.

[0115] (10) By using the variance of the velocity residuals in the time series, it is possible to determine whether the object is a ghosting based on the statistical bias of the velocity residuals.

[0116] (Third Implementation)

[0117] <3-1. Differences from the first and second embodiments>

[0118] The basic structure of the third embodiment is the same as that of the first and second embodiments, so the description of the common structures is omitted, and the description focuses on the differences. Furthermore, the same reference numerals as in the first and second embodiments indicate the same structures, and the preceding description is also referenced.

[0119] The third embodiment differs from the first or second embodiment in that it further performs the same object determination process and the ghosting error determination suppression process in the object detection process of the first or second embodiment.

[0120] <3-2. Treatment>

[0121] Next, refer to Figure 16 The flowchart below describes the object detection process performed by the processing apparatus 20 of the third embodiment.

[0122] First, in S610 to S705, execution and Figure 5 The flowchart shown contains the same processes S10 to S105. Here, within the ghosting determination process of S105, the ghosting evaluation value calculation process of S120 can be executed... Figure 11 The subroutine shown can also be executed. Figure 15 The subroutine shown.

[0123] Next, if it is determined in S650 that there are no unprocessed targets, the process proceeds to S800 to perform the same object determination process. Specifically, the process is executed... Figure 17 The subroutine shown.

[0124] like Figure 20 As shown, there is a situation where radar device 10 receives multiple reflected signals from a vehicle 70 traveling in front of vehicle 60. These multiple reflected signals are generated at multiple locations on the vehicle 70. For example, the multiple reflected signals include a trailing edge signal R1 and a tire signal R2. The trailing edge signal R1 is generated at the trailing edge of the vehicle 70. The tire signal R2 is generated at the tires of the vehicle 70. Figure 21 Showing with Figure 20 The trailing edge signal R1 and the tire signal R2 at different times.

[0125] The trailing edge signal R1 is the dominant signal among multiple reflected signals. For example... Figure 20 and Figure 21 As shown, the temporal deviation of the velocity observations calculated from the trailing edge signal R1 is relatively small. Therefore, it is difficult to classify the trailing edge marker Tga corresponding to the trailing edge signal R1 as a ghost image. On the other hand, the temporal deviation of the velocity observations calculated from the tire signal R2 is relatively large. Therefore, the velocity residual is also relatively large. Therefore, although the tire marker Tgb corresponding to the tire signal R2 is a marker generated by an actual existing object, there is still a possibility of misclassifying it as a ghost image.

[0126] like Figure 22 As shown, after the tire marker Tgb is misidentified as a ghosting effect, the trailing edge marker Tga may be lost due to misconnections during marker tracking. For example, if the number of markers generated based on the preceding vehicle 70 decreases in subsequent processing cycles, the predicted value of the marker corresponding to the trailing edge marker Tga may not be connected to the observed value, resulting in the loss of the trailing edge marker Tga. Alternatively, the predicted value of the marker corresponding to the tire marker Tgb may be connected to the observed value, and the tire marker Tgb may persist. In such cases, ghosting detection continues. Furthermore, vehicle control for the preceding vehicle 70 continues to be suppressed.

[0127] Therefore, it is determined whether the second object corresponding to the second target is the same as the first object corresponding to the first target. Moreover, if it is determined that the second object is the same as the first object, and it is determined that the first target is not a ghost image, the determination of the second target as a ghost image is suppressed.

[0128] First, in S810, it is determined whether there is any object that has not been selected as the first object among the objects being tracked in the current processing cycle. If it is determined in S810 that there is no unselected object, the subroutine ends and the process proceeds to S900.

[0129] On the other hand, if it is determined in S810 that there is an unselected object, the process enters S820, and the first object is selected from the unselected objects.

[0130] Next, in S830, it is determined whether there is an object that has not been selected as the second object among the tracked objects existing in the current processing cycle. The second object is equivalent to determining whether it corresponds to the same object as the first object.

[0131] In S830, if it is determined that there is no unselected object, the process returns to S810; if it is determined that there is an unselected object, the process proceeds to S840.

[0132] In S840, a second object is selected from the unselected objects. Here, the second object is a different object from the first object selected in S810.

[0133] Next, in S850, it is determined whether the second object is the same as the object corresponding to the other object. The second object is the object corresponding to the second object, and the second object originates from the second object. The first object, described later, is the object corresponding to the first object, and the first object originates from the first object. If it is determined in S850 that the object is the same, the process returns to S830. On the other hand, if it is determined in S850 that the object is not the same, the process proceeds to S860.

[0134] Next, in S860, it is determined whether the longitudinal position of the second object is (i) closer to the inside than the longitudinal position of the first object and (ii) the difference in position is below a longitudinal threshold. The longitudinal position of the first object, the longitudinal position of the second object, and the lateral position (described later) are calculated based on at least one of the object observation value, the object prediction value, and the object estimate value. The longitudinal position corresponds to the position in the direction of travel of the vehicle 60, and the lateral position corresponds to the position in the direction orthogonal to the direction of travel of the vehicle 60. In addition, "inside" here corresponds to the side away from the vehicle 60. The difference in position is equivalent to the difference between the longitudinal positions of the first object and the longitudinal positions of the second object. The longitudinal threshold is a value representing the length of the vehicle. In S860, if a negative determination is made, the process returns to S830; if a positive determination is made, the process proceeds to S870.

[0135] Next, in S870, it is determined whether the difference between the lateral position of the second object and the lateral position of the first object is below a lateral threshold. The lateral threshold is a value representing the width of the vehicle. In S870, if it is determined that the difference in lateral position is greater than the lateral threshold, the process returns to S830; if it is determined that the difference in lateral position is below the lateral threshold, the process proceeds to S880.

[0136] Next, in S880, it is determined whether the difference between the ground velocity of the second target and the ground velocity of the first target is below a velocity threshold. The velocity threshold is equivalent to a threshold used to determine whether the ground velocity of the second target can be considered the same as that of the first target. In S880, if it is determined that the difference in ground velocities is greater than the velocity threshold, the process returns to S830; if it is determined that the difference in ground velocities is below the velocity threshold, the process proceeds to S890.

[0137] In S890, since the longitudinal position, lateral position, and ground velocity of the second object are sufficiently close to the longitudinal position, lateral position, and ground velocity of the first object, it is determined that the second object is the same as the first object. That is, it is determined that the first object and the second object originate from the same object.

[0138] Return to Figure 16 In the S900, ghosting error detection suppression processing is performed. Specifically, it performs... Figure 18 The subroutine is shown. As described above, when multiple targets are generated from the same object, there may be cases where the multiple targets include targets that are easily misclassified as ghosting. Therefore, when multiple targets are generated from the same object, and one of the targets is determined to be non-ghosting, the determination of the remaining targets as ghosting is suppressed.

[0139] First, in S910, it is determined whether there is a second target that was not selected in the ghosting error detection and suppression process. If it is determined in S910 that there is no unselected second target, the subroutine ends and returns to the target detection process, and the target detection process ends. On the other hand, if it is determined in S910 that there is an unselected second target, one of the unselected second targets is selected, and the process proceeds to S920.

[0140] In S920, it is determined whether there exists a first object corresponding to the first object that is determined to be the same as the second object. Here, the second object is the object corresponding to the second object selected in S910. In S920, if it is determined that the first object does not exist, the process returns to the process in S910; if it is determined that the first object exists, the process proceeds to the process in S930.

[0141] In S930, it is determined whether the first object identified as existing in S920 is determined to be a non-ghosting object. In S930, if it is not determined to be a non-ghosting object, the process returns to S910; if it is determined to be a non-ghosting object, the process proceeds to S940.

[0142] In S940, the ghosting evaluation value of the second object selected in S910 is reduced, and the process returns to S910. This suppresses the misclassification of the second object as a ghosting effect. That is, if one of multiple objects generated from the same object is judged as a non-ghosting effect, the misclassification of the remaining objects as ghosting effects is suppressed.

[0143] <3-4. Effects>

[0144] According to the third embodiment described above, in addition to the effects (1) to (10) mentioned above, the following effects can also be obtained.

[0145] (11) Determine whether the second object corresponding to the second marker is the same as the first object corresponding to the first marker. Furthermore, if (i) the first marker is determined to be a non-ghosting image and (ii) the second object is determined to be the same as the first object, suppress the determination of the second marker as a ghosting image. Thus, it is possible to suppress the misclassification of markers generated by actual objects as ghosting images. Furthermore, it is possible to suppress the continued identification of markers that are misclassified as ghosting images.

[0146] (12) If the first object is determined to be a non-ghosting object and the second object is determined to be the same as the first object, the ghosting evaluation value of the second object is reduced. Thus, it is possible to suppress the determination of the second object as a ghosting object.

[0147] <3-5. Other Examples of the Third Implementation>

[0148] Next, refer to Figure 19 The subroutines will be used to describe other examples of ghosting detection and suppression processing in the third embodiment. That is, the processing apparatus 20 of the third embodiment can also be used instead of... Figure 18 The subroutine shown is executed. Figure 19 The subroutine shown.

[0149] First, in S950 to S970, execution and Figure 18 The subroutines S910 to S930 shown have the same processing.

[0150] If the case is determined to be non-ghosting in S970, then proceed to the processing in S980.

[0151] In S980, the second object selected in S950 is determined to be a non-ghosting image. This avoids misjudging the second object as a ghosting image.

[0152] In addition to the effects (1) to (11) described above, other examples of the third embodiment described above can also yield the following effects.

[0153] (13) If (i) the first object is determined to be a non-ghosting object and (ii) the second object is determined to be the same as the first object, the second object is determined to be a non-ghosting object. Thus, it is possible to avoid determining the second object as a ghosting object.

[0154] (Fourth Implementation)

[0155] <4-1. Differences from the third embodiment>

[0156] The basic structure of the fourth embodiment is the same as that of the third embodiment, so the description of the common structures is omitted, and the description focuses on the differences. Furthermore, the same reference numerals as in the third embodiment indicate the same structures, and the preceding description is also relevant.

[0157] In the third embodiment, when multiple targets are generated from the same object, ghosting misidentification suppression processing is performed. In contrast, in the fourth embodiment, tracking suppression processing is performed when multiple targets are generated from the same object, which differs from the third embodiment. Specifically, in the third embodiment, when (i) multiple targets are generated from the same object, and (ii) one of the targets is determined to be a non-ghosting object, the misidentification of the remaining targets as ghosting objects is suppressed. Thus, the continued identification of targets misidentified as ghosting objects is suppressed. In contrast, in the fourth embodiment, when (i) multiple targets are generated from the same object, and (ii) one of the targets is determined to be a non-ghosting object, tracking of the target among the multiple targets that is determined to be a ghosting object is suppressed. Thus, the continued identification of targets misidentified as ghosting objects is suppressed.

[0158] <4-2. Treatment>

[0159] Next, refer to Figure 23 The flowchart illustrates the object detection process performed by the processing apparatus 20 of the fourth embodiment.

[0160] First, in S615 to S805, execution and Figure 16 The processes in S610 to S800 of the flowchart shown are the same.

[0161] Next, in S905, tracking suppression processing is performed. Specifically, the following steps are executed: Figure 24 The subroutine shown.

[0162] First, in S915 to S935, execution and Figure 18 The subroutines S910 to S930 shown have the same processing.

[0163] If the first object is determined to be a non-ghosting in S935, the process proceeds to S945.

[0164] In S945, it is determined whether the second object is determined to be a ghost image. If the second object is not determined to be a ghost image in S945, the process returns to S915. If the second object is determined to be a ghost image, the process proceeds to S955.

[0165] In S955, the association range of the second object is narrowed, and the process returns to S915. The association range is the range within which the predicted and observed values ​​of the object are correlated. Specifically, the velocity range within the association range is narrowed. The velocity range is the range within which the predicted and observed velocity values ​​are correlated. The predicted and observed values ​​are correlated only if the difference between the predicted and observed velocity values ​​is within the velocity range. By narrowing the association range, the correlation between the predicted and observed values ​​can be suppressed. As a result, the tracking of the second object can be suppressed.

[0166] <3-4. Effects>

[0167] According to the fourth embodiment described above, in addition to the effects (1) to (10) mentioned above, the following effects can also be obtained.

[0168] (14) Determine whether the second object is the same as the first object. Furthermore, if (i) the first object is determined to be a non-ghosting object, (ii) the second object is determined to be the same as the first object, and (iii) the second object is determined to be a ghosting object, then tracking of the second object is suppressed. Thus, tracking of objects that are mistakenly determined to be ghosting objects can be suppressed. Furthermore, continued identification of objects that are mistakenly determined to be ghosting objects can be suppressed.

[0169] (15) When (i) the first object is determined to be a non-ghosting object, (ii) the second object is determined to be the same as the first object, and (iii) the second object is determined to be a ghosting object, the correlation range is narrowed. Thus, tracking the second object can be suppressed.

[0170] (16) In cases where (i) the first object is determined to be a non-ghosting object, (ii) the second object is determined to be the same as the first object, and (iii) the second object is determined to be a ghosting object, the velocity range is narrowed in particular. As a result, tracking of the second object can be appropriately suppressed.

[0171] <4-3. Other Examples of the Fourth Implementation>

[0172] Next, refer to Figure 25 The subroutines will be used to describe other examples of the tracking suppression processing in the fourth embodiment. That is, the processing device 20 of the fourth embodiment can also be replaced by... Figure 24 The subroutine shown is executed. Figure 25 The subroutine shown.

[0173] First, in S958 to S988, execution and Figure 24 The subroutines S915 to S945 shown have the same processing.

[0174] In S988, if the second target is determined to be a ghost image, the process proceeds to S998.

[0175] In S998, the second object selected in S958 is removed from the objects being tracked in the current processing cycle. This avoids tracking the second object.

[0176] In addition to the effects (1) to (10) and (14) described above, other examples of the fourth embodiment described above can also obtain the following effects.

[0177] (17) If (i) the first object is determined to be a non-ghosting object, (ii) the second object is determined to be the same as the first object, and (iii) the second object is determined to be a ghosting object, the second object is deleted. Thus, tracking the second object can be avoided.

[0178] (Other implementation methods)

[0179] The above describes the methods for implementing this disclosure, but this disclosure is not limited to the above-described embodiments and can be implemented in various ways.

[0180] (a) In the above embodiment, the radar device 10 alternately transmits M first chirped signals and N second chirped signals with different repetition periods, but it may also transmit pulse signals instead of chirped signals. That is, the radar device 10 may also alternately transmit M first pulse signals and N second pulse signals with different repetition periods.

[0181] (b) The method for implementing the radar device 10 and its functions as described in this disclosure can also be implemented by a dedicated computer, which is provided by a processor and a memory programmed to perform one or more functions embodied in the computer program. Alternatively, the method for implementing the radar device 10 and its functions as described in this disclosure can also be implemented by a dedicated computer, which is provided by a processor composed of one or more dedicated hardware logic circuits. Alternatively, the method for implementing the radar device 10 and its functions as described in this disclosure can also be implemented by one or more dedicated computers, which are composed of a processor and a memory programmed to perform one or more functions, and a processor composed of one or more hardware logic circuits. In addition, the computer program can also be stored as instructions executed by the computer on a computer-readable non-transitional tangible recording medium. The method for implementing the functions of the various parts included in the radar device 10 does not necessarily need to include software, and all its functions can be implemented using one or more hardware components.

[0182] (c) Multiple functions of one component in the above embodiments can be achieved through multiple components, or one function of one component can be achieved through multiple components. Alternatively, multiple functions of multiple components can be achieved through one component, or one function achieved by multiple components can be achieved through one component. Furthermore, a portion of the structure in the above embodiments can be omitted. Additionally, at least a portion of the structure in the above embodiments can be added to the structure of other above embodiments or replaced with the structure of other above embodiments.

[0183] (d) In addition to the radar device described above, this disclosure can also be implemented in various ways, such as a system that includes the radar device, a program for enabling a computer to function as the radar device, a non-transitional physical recording medium such as a semiconductor memory that records the program, and an object tracking method.

Claims

1. A radar device, mounted on a vehicle, comprising: The transmitting unit is configured to transmit a transmission signal at a set repetition period, wherein the transmission signal is a pulse signal or a chirp signal; The receiving unit is configured to receive a reflected signal generated when the transmitted signal sent by the transmitting unit is reflected by at least one object; The setting unit is configured to set a repeating cycle in the next processing cycle that is different from the repeating cycle in the immediately preceding processing cycle. The detection unit is configured to detect at least one target signal corresponding to at least one target from the reflected signal received by the receiving unit. The observation unit is configured to calculate at least one object observation value based on the at least one object signal detected by the detection unit, wherein each of the at least one object observation value includes a velocity observation value that takes into account the velocity return of the at least one object. The object tracking unit includes a prediction unit and an estimation unit, and is configured to track each of the at least one objects based on the time series of each of the at least one object observations calculated by the observation unit. The prediction unit is configured to calculate an object prediction value corresponding to the current state of each of the at least one objects based on an object estimation value corresponding to the past state of each of the at least one objects. The estimation unit is configured to establish a correlation between the object prediction value and the object observation value, and calculate the object estimation value corresponding to the current state of each of the at least one objects based on the mutually correlated object prediction value and the object observation value. The object prediction value includes a velocity prediction value among the elements. The residual calculation unit is configured to calculate the velocity residual of each of the at least one of the above-mentioned objects, wherein the velocity residual is the difference between the predicted velocity value and the observed velocity value; The evaluation unit is configured to calculate an evaluation value for each of the at least one objects based on the magnitude of the deviation of the velocity residual in the time series of each of the at least one objects. The evaluation value corresponds to the probability that each of the at least one objects is a ghost image. The ghosting determination unit is configured to determine whether each of the at least one objects is a ghost based on the evaluation value of each of the at least one objects.

2. The radar device according to claim 1, wherein, The aforementioned evaluation unit is configured to calculate the variance of the aforementioned velocity residuals in the time series, and use it as the aforementioned evaluation value.

3. The radar device according to claim 1, wherein, The evaluation unit is configured to calculate the residual change as the magnitude of the deviation of the speed residual, and to increase or decrease the evaluation value based on the calculated residual change. The residual change is the absolute value of the difference between the speed residual in the current processing cycle and the speed residual in the previous processing cycle.

4. The radar device according to claim 3, wherein, The evaluation unit is configured to increase the evaluation value when the residual change is above a first threshold.

5. The radar device according to claim 4, wherein, The evaluation unit is configured to reduce the evaluation value when the residual change is below the second threshold, wherein the second threshold is less than the first threshold.

6. The radar device according to any one of claims 1 to 5, wherein, The aforementioned ghosting determination unit is configured to determine that, when the evaluation value is above the third threshold, the object corresponding to the evaluation value among the at least one of the objects is a ghosted object.

7. The radar device according to claim 6, wherein, The aforementioned ghosting determination unit is configured to determine that, when the evaluation value is below the fourth threshold, the object corresponding to the evaluation value among the at least one object is not a ghost, and the fourth threshold is less than the third threshold.

8. The radar device according to any one of claims 1 to 5, wherein, Each of the at least one of the above-mentioned landmark observations includes the observation azimuth. The aforementioned observation unit is configured to calculate the detection speed based on each of the at least one target signal, and to calculate the observed speed value based on the calculated detection speed and the selected number of retracements. The aforementioned radar device further includes a stationary object speed calculation unit, which is configured to calculate the stationary object speed of each of the at least one of the at least one objects, assuming that each of the at least one of the at least one objects is stationary, based on the speed of the vehicle and the observation azimuth of each of the at least one objects. The evaluation unit is configured to increase the evaluation value when the detection speed is the same as the speed of the stationary object.

9. The radar device according to any one of claims 1 to 5, wherein, Each of the at least one of the above-mentioned landmark observations includes the observation azimuth. The aforementioned radar device also includes a ground velocity calculation unit, which is configured to calculate the ground velocity of each of the at least one targets based on the speed of the vehicle and the observation azimuth of each of the at least one targets. The evaluation unit is configured such that, when the ground velocity is above a velocity threshold, it uses the at least one of the objects corresponding to the ground velocity as the object to determine whether it is a ghost image.

10. The radar device according to any one of claims 1 to 5, wherein, The aforementioned at least one object includes a first object and a second object, wherein the second object is different from the first object. The aforementioned at least one object includes a first object corresponding to the aforementioned first landmark and a second object corresponding to the aforementioned second landmark. The aforementioned radar device includes: The same object determination unit is configured to determine whether the second object and the first object are the same object based on at least one of the position observation value, position prediction value, and position estimate value of each of the first and second objects, and at least one of the velocity observation value, velocity prediction value, and velocity estimate value of each of the first and second objects. The position observation value is included in the object observation value, the position prediction value is included in the object prediction value, and the position estimate value and velocity estimate value are included in the object estimate value. The determination suppression unit is configured to suppress the ghosting determination unit from determining the second object as a folded ghost when (i) the ghosting determination unit determines that the first object is a non-folded ghost and (ii) the same object determination unit determines that the second object is the same object as the first object.

11. The radar device according to any one of claims 1 to 5, wherein, The aforementioned at least one object includes a first object and a second object, wherein the second object is different from the first object. The aforementioned at least one object includes a first object corresponding to the aforementioned first landmark and a second object corresponding to the aforementioned second landmark. The aforementioned radar device includes: The same object determination unit is configured to determine whether the second object and the first object are the same object based on at least one of the position observation value, position prediction value, and position estimate value of each of the first and second objects, and at least one of the velocity observation value, velocity prediction value, and velocity estimate value of each of the first and second objects. The position observation value is included in the object observation value, the position prediction value is included in the object prediction value, and the position estimate value and velocity estimate value are included in the object estimate value. The tracking suppression unit is configured to suppress the object tracking unit from tracking the second object when (i) the ghosting determination unit determines that the first object is a non-reflection ghost, (ii) the same object determination unit determines that the second object is the same object as the first object, and (iii) the ghosting determination unit determines that the second object is a reflection ghost.

12. The radar device according to claim 10, wherein, The aforementioned determination suppression unit is configured to reduce the evaluation value of the second object when (i) the ghost determination unit determines that the first object is a non-reflection ghost and (ii) the same object determination unit determines that the second object is the same object as the first object.

13. The radar device according to claim 10, wherein, The above-mentioned determination suppression unit is configured to determine the second object as a non-reflection ghost when (i) the ghost determination unit determines that the first object is a non-reflection ghost and (ii) the same object determination unit determines that the second object is the same object as the first object.

14. The radar device according to claim 11, wherein, The estimation unit is configured to establish a correlation between the predicted and observed values ​​of the object when the difference between the predicted and observed values ​​is within a set correlation range. The tracking suppression unit is configured to narrow the correlation range when (i) the ghost determination unit determines that the first object is a non-reflection ghost, (ii) the same object determination unit determines that the second object is the same object as the first object, and (iii) the ghost determination unit determines that the second object is a reflection ghost.

15. The radar device according to claim 14, wherein, The aforementioned correlation range includes the range of speeds used to establish a correlation between the aforementioned predicted speed values ​​and the aforementioned observed speed values. The tracking suppression unit is configured to narrow the speed range when (i) the ghosting determination unit determines that the first object is a non-ghosting, (ii) the same object determination unit determines that the second object is the same as the first object, and (iii) the ghosting determination unit determines that the second object is a ghosting.

16. The radar device according to claim 11, wherein, The tracking suppression unit is configured to delete the second object when (i) the ghost determination unit determines that the first object is a non-ghosting, (ii) the same object determination unit determines that the second object is the same as the first object, and (iii) the ghost determination unit determines that the second object is a ghosting.

17. The radar device according to any one of claims 1 to 5, wherein, It also includes a vehicle control unit configured to output control commands corresponding to each of the at least one of the targets to a driving assistance device, wherein the driving assistance device is a device that assists in driving the vehicle. The vehicle control unit is configured to suppress the output of the control command corresponding to the at least one of the objects determined to be a ghost image when the ghost image determination unit determines that any one of the at least one objects is a ghost image.

Images